Gross primary productivity (GPP), the amount of carbon plants fixed by photosynthesis, is pivotal for understanding the global carbon cycle and ecosystem functioning. Process-based models built on the knowledge of ecological processes are susceptible to biases stemming from their assumptions and approximations. These limitations potentially result in considerable uncertainties in global GPP estimation, which may pose significant challenges to our net zero goals. This study presents UFLUX v2.0, a process-informed model that integrates state-of-the-art ecological knowledge and advanced machine learning (ML) technique to reduce uncertainties in GPP estimation by learning the biases between process-based models and eddy covariance (EC) measurements. In our findings, UFLUX v2.0 demonstrated a substantial improvement in model accuracy, achieving an$R {^{{2}}}$of 0.79 with a reduced RMSE of 1.60 g$\cdot $Cm−2d−1, compared to the process-based model’s$R {^{{2}}}$of 0.51 and RMSE of 3.09 g$\cdot $Cm−2d−1. Our global GPP distribution analysis indicates that while UFLUX v2.0 and the process-based model achieved similar global total GPP (137.47 and 132.23 PgC, respectively), they exhibited large differences in spatial distribution, particularly in latitudinal gradients. These differences are very likely due to systematic biases in the process-based model and differing sensitivities to climate and environmental conditions. This study offers improved adaptability for GPP modeling across diverse ecosystems and further enhances our understanding of global carbon cycles and its responses to environmental changes.

Near real-time precipitation estimation from geostationary satellites plays an important role in flood forecasting, water resource management, and disaster prevention and reduction. Currently, near real-time precipitation products based on geostationary satellites still face great challenges in accurately detecting precipitation and monitoring small-scale precipitation. In this study, a novel Geostationary Satellite Precipitation Estimation (GSPE) algorithm for near real-time condition was developed to retrieve precipitation at a spatial resolution of 0.05°×0.05° every 10 minutes both day and night. The major highlight of the GSPE is that a new precipitation detection scheme was created by introducing a newly proposed precipitation detection index (PDI) and 24-hour continuous cloud microphysical parameters for the first time. Another highlight is that a dynamic updating scheme was proposed in building conversion models between brightness temperature of geostationary satellite and precipitation to keep the accuracy and stability of the estimated precipitation. Furthermore, the 10-minute temporal resolution of the estimated precipitation could accurately capture the evolution of a short precipitation process and improve the calculation of total precipitation amount. According to the validation using rain gauges observations from Chinese mainland, the Heidke Skill Score of the GSPE in hourly scale could reach up to 0.39 which was improved by 11.43% compared to the GSMaP_NOW. The root mean square error of precipitation from the GSPE in hourly, daily, and monthly scale are 1.66mm, 13.65mm, and 97.76mm respectively, and were improved by 15.74%, 13.17%, and 21.01% respectively compared to that of the GSMaP_NOW.

Lake ice thickness (LIT) significantly impacts water, climate, and socio-economic activities. Due to lack of observations, estimating the spatiotemporal variations in LIT is still a challenging task. Brightness temperature (TB) measured by microwave radiometers correlates well with LIT at certain frequencies, which potentially enables global and daily estimations of LIT by spaceborne passive microwave (PMW) remote sensing over decades. In this study, a generalized empirical linear regression algorithm to estimate LIT using PMW is proposed using TB as the sole data source. The method is based on statistical analysis of TB over 18 lakes. The analysis revealed a large variability of linear coefficients relating TB to LIT; however, the coefficients could be tied to TB at the Freeze-up End (FUE). When compared with LITs derived from in-situ measurements, radar altimetry, and published LIT data, the root mean square errors (RMSE) were 0.19 m, 0.16 m, and 0.15 m, respectively. This method was then used to estimate the LITs for 96 lakes in the Northern Hemisphere from 2002 to 2011, which was proven to be applicable for long-term LIT mapping and monitoring for large lakes, demonstrating improved generalizability.

1 Abstract-The cloud water path (CWP) has an important influence on the radiative effects of clouds and the water cycle in the Earth’s atmospheric system, serving as a key parameter in physical cloud processes. In this study, a novel method for retrieving CWP by leveraging the advantages of multisource and multiband active and passive satellite observations is proposed. A retrieval model to retrieve CWP that using Himawari-8/AHI) thermal infrared channels is established by learning from active radar (CloudSat) measurements, the model enables continuous CWP retrieval throughout the day. Compared with all-day CloudSat-CWP, our CWP products has has a higher retrieval accuracy that that of MODIS. The distribution of the monthly average CWP product based on the Himawari-8 full-disk dataset resembles that of CloudSat observations, with the highest average CWPs in equatorial region, followed by the CWPs in midlatitude regions. This spatial pattern of CWP is possibly due to the prevalence of strong convective systems in these areas, which facilitate the formation and progression of deep clouds, leading to higher CWP values. This algorithm can offer valuable data support for atmospheric-related analyses and has been integrated into the Cloud Remote Sensing, Atmospheric Radiation, and Renewable Energy Application (CARE) platform for atmospheric remote sensing algorithms.

The depolarization ratio (PR), defined as the ratio of C-band HV to VV backscattering coefficients, is sensitive to snow depth, making it a useful metric for estimating large-scale snow depth distribution using Sentinel-1 data. However, its application in certain regions has led to significant errors, indicating an incomplete understanding of the underlying physical mechanisms. In an experiment conducted in the Altay Mountains, China, we observed markedly different depolarization ratios in adjacent areas with similar snow depths, where the underlying surface conditions differed. Directly using the depolarization ratio for snow depth retrieval in such cases leads to significantly different estimates, which are not supported by in situ observations. In this study, we demonstrate that this inconsistency is likely caused by the generation of cross-polarization backscattering due to the coupling of bistatic rough surface scattering and snow volume scattering. In particular, a rough surface may produce high cross-polarization scattering under certain incident and scattering angles, which interacts with the snow volume through bistatic scattering. An iterative solution to the vector radiative transfer equation is used to properly account for the coupling between polarimetric bistatic rough surface scattering and snow volume scattering. The simulation results show that after this improvement, the depolarization ratio under rough surface conditions increased, and its potential to match real-world observations was significantly enhanced. It is further demonstrated that simply subtracting the depolarization ratio under snow-free conditions cannot fully cancel the influence of the rough surface, as the remaining depolarization ratio still contains terms related to snow-surface interaction. Therefore, further correction of the rough surface effect is necessary to improve the accuracy of C-band depolarization ratio-based snow depth retrieval.

Accurately simulating soil profile information throughout all seasons using microwave emission models is crucial for guiding the development of soil moisture retrieval algorithms. This study based on ground-based radiometer and ground measurements at Maqu and Yudaokou in China to investigate the potential of the soil stratified coherence model (Wilheit) combined with the τ-ω vegetation model and optimized soil dielectric model (Zhang-Zhao) for simulating passive microwave brightness temperature (TB) of soil at L-band (1.4GHz) and P-band (0.706 GHz). The results showed that the correlation coefficient (R), bias, and RMSE between the L-band simulations and the ground-based microwave radiometer observations at the Maqu and Yudaokou is 0.84~0.86, -2.75~0.63k, and 3.70~7.30k at V polarization, and 0.79~0.84, -2.18~2.48k, and 7.69~11.49k at H polarization, respectively. In addition, L-band TB simulations can effectively capture the change of the TB observations in the time series at Maqu site. The simulation results in the P-band need to be further validation.

Snow density is the key parameter in converting snow depth to snow mass. A two-parameter retrieval algorithm has been developed to estimate snow density and soil permittivity simultaneously in ground-based experiments. This study tested the two-parameter retrieval algorithm applied for the L-band multiple-angle SMOS (Soil Moisture Ocean Salinity) and single-angle SMAP (Soil Moisture Active Passive) missions, respectively, at 46 sites in Quebec, Canada. To alleviate the ill-posed problem, we also developed a one-parameter retrieval algorithm, where only the snow density was retrieved using a soil permittivity calculated from the GLDAS-Noah soil simulations. Results showed that, the two-parameter retrieval algorithm achieved an ubRMSE of 40 and 60 kg⁄m3for SMOS and SMAP, respectively, but the correlation is low (0.23-0.24), because the sensitivity of observations to snow density is weaker than soil parameters, and the estimates from coarse-resolution satellite observations were validated against point-scale measurements. On the contrary, if soil permittivity is determined despite a small bias, the one-parameter retrieval algorithm based on SMOS can increase the correlation coefficient to 0.65 in the October to June period. It indicates the importance of a soil permittivity prior or a stable snow condition (for example, frozen soil condition) to achieve high accuracy for satellite-based snow density estimation.

The Chinese L-band twin-satellite SAR constellation, LuTan-1, that was launched in 2022 became the first spaceborne L-band bistatic InSAR mission. In this work, we will explore this valuable dataset of bistatic InSAR mode to estimate forest height. Since the majority of this mode only acquires single-polarization (HH-pol) data, we use the few-look InSAR phase histogram method developed by our previous work to estimate the digital terrain height (DTM) and InSAR phase center height simultaneously. Then, the HH-pol complex InSAR coherence measurements in combination with the DTM (or phase center height) are used to invert for forest total height using a physical model approach, namely the Random Volume over Ground (RVoG) model. Preliminary inversion results are shown and validated against spaceborne lidar (NASA’s GEDI and ICESat-2/ATLAS) and airborne lidar data over the tropical test site on the Hainan island of China.

This paper describes the Altay 2024 airborne field campaign in support of snow observation retrieved from spaceborne InSAR measurements from the Chinese LuTan-1 (a spaceborne L-band SAR constellation launched in 2022). The airborne and field measurements that are synchronized with LuTan-1 InSAR acquisitions will be conducted in January-February 2024 (snow on) and May-July 2024 (snow off). The remote sensing and in-situ measurements include various in-situ observations and drone-based lidar measurements. We first provide the overview of the Altay 2024 campaign including the choice of the in-situ measurement locations and flight tracks of the drone-based lidar. Then, historical InSAR dataset from all the available L/C-band SAR’s (e.g. JAXA’s ALOS, ESA’s Sentinel-1, China’s LuTan-1) over the study area are used to generate SWE change products, which are further compared against the in-situ measurements when available. This synergetic spaceborne airborne field campaign will directly validate the LuTan-1 derived snow products using the acquired airborne and field dataset, which can also support the design of future spaceborne mission concepts for snow retrieval.

Snow water equivalent (SWE) is a critical component in the global water and energy cycles. This study proposes a SWE inversion method applicable to VV and VH dual-polarization Synthetic Aperture Radar (SAR) data. The method establishes a cost function between simulated and SAR-measured scattering and subsequently solves this function to obtain SWE values. Using the central region of the Sierra Nevada Mountains in the United States as the study area, SWE was retrieved using five Sentinel-1 images acquired from January to February 2021. The verification results show the feasibility of the method, which will help expand the application of Sentinel-1 in snow monitoring.

The characterization of snow dynamics in mountainous regions requires high-resolution snow depth (SD) and snow water equivalent (SWE) data from remote sensing techniques. To enhance our comprehension of coarse- resolution passive microwave signals in complex terrains and to maximize their utility in these areas, we developed a new SWE estimation method, leveraging physically-based snow process and snow radiative transfer models. This method utilizes 0.1-degree AMSR2 brightness temperatures and 3-km fractional snow cover (FSC) time series from MODIS to construct an observation equation of nested grids. Ensembles of SWE, snow cover, and brightness temperature (TB) time series are created through perturbed meteorological datasets. Subsequently, ensemble weights are determined and utilized to generate a SWE product in 3-km resolution. This method, resembling adjustment computation theory more than data assimilation techniques, ensures the preservation of water balance within the estimation. It will undergo testing and evaluation in the Qinghai-Tibetan Plateau and Xinjiang province in China, with comparisons to station SD measurements.

Large inaccuracies remain in the traditional convective cloud identification system over the plateau area struggles to capture mid- and low-level clouds due to the complex topographic effects influencing cloud pressure. Besides, the lack of efficient nighttime cloud-type products hinders progress in the research on the diurnal cycle and seasonal variation in convective clouds (including deep convection and cumulus clouds) over the Tibet Plateau (TP). In this study, we incorporated Shapley additive explanation (SHAP) tuning into the fundamental machine learning CatBoost Classifier technology, which was applied to a 24-h convective cloud detection algorithm utilizing cloud top temperature (CTT) and optical thickness data derived from the Himawari-8 infrared channels. This specifically tackles the problem of underestimating cumulus clouds in plateau areas. This innovative product enables capturing important processes of deep convection, especially for cumulus clouds, facilitating a comprehensive spatial-temporal analysis of the entire TP region. The results confirm that the new algorithm shows significant improvements in cumulus detection compared to the official cloud product of Himawari-8. In addition, the deep convective clouds have also improved from 35.85% to 63.05% for hit rate (HR) value. The analysis reveals a notable diurnal variation in convective cloud activity over the TP, predominantly occurring from noon to night. This finding underscores the influential heating role of the TP in convective activity.

Global surface soil moisture (SSM) mapping at a 9-km intermediate resolution from microwave remote sensing could play a pivotal role in advancing detailed global hydrological investigations. Despite the wide recognition of the soil moisture active passive (SMAP) mission’s 9-km SSM products based on oversampling of the L-band radiometry, concerns persist regarding its ability to capture higher SSM heterogeneity at finer resolutions. For addressing this concern, a novel methodological framework was proposed in this study. This framework advocates the downscaling of the SMAP 36-km dataset through a fusion with high-frequency (Ka-band) passive microwave (PMW) observations. The resultant 9-km all-weather SSM product, derived from this novel approach, is evaluated using ground-based measurements worldwide. The findings reveal a significantly enhanced accuracy compared to the SMAP conventional oversampling-based one, especially in areas exhibiting pronounced local variations in SSM patterns. The study therefore represents a substantial step forward, providing new insights into the design and use of a multifrequency satellite radiometer for global SSM mapping.

The derivation of ice water path (IWP) from microwave radiometer measurements is challenging. This study presents a deep learning framework for global retrieval of IWP using observations from the Microwave Humidity Sounder-II (MWHS-II) aboard the FengYun-3D (FY-3D) satellites. Two deep learning models, Deep Forest (DF21) and Quantile Regression Neural Network (QRNN) are constructed to detect ice cloud flags and retrieve IWP. By collocating MWHS-II observations with 2C-ICE, a joint product of CloudSat and CALIPSO, deep learning models learn the characteristics of IWP from MWHS-II brightness temperatures. The test results show that the MWHS-II channels provide more information on IWP than the MWHS channels, particularly the 89 GHz channel and the 118 GHz channels with an offset of ≥ 0.8 GHz. Combining the QRNN and DF21 models, the IWP retrieval results in an RMSE of 707.346 g/m2, MAPE of 65.122%, MBE of -104 g/m2, determination coefficient (R2) of 0.683, and Pearson correlation coefficient (PCC) of 0.831. Application of the models to MWHS-II observations of Tropical Cyclone CILIDA shows better agreement with 2C-ICE. All datasets exhibit a similar feature on the monthly mean scale, but the magnitudes of IWP differ. Compared to GMI-GPROF, MODIS, and ERA5 IWP products, MWHS-II results are closest to 2C-ICE. Similar results are also shown for the zonal mean data. These results show that deep learning methods efficiently and probabilistically retrieve IWP from long-term observation data of MWHS/MWHS-II.

The thermal infrared (TIR) emissivity and physical temperature of snow together determine the thermal radiation of snow. The modeling of snow and ice TIR emissivity is important for climate models and remote sensing. Previous snow and ice TIR emissivity models fail in predicting the sensitivity of emissivity to snow type and snow microstructure, which was measured in experiments. Empirical models were proposed to simulate such sensitivity but not in a unified theoretical framework. In this study, we propose a snow and ice TIR emissivity model based on photon tracking by assuming that the geometric optics approximation is still valid in TIR spectral region. It is proved that the proposed model can predict both the TIR emissivity’s sensitivity to grain size for small grain sizes and the TIR emissivity’s sensitivity to snow density. These features can fully explain the experiment observed features. Moreover, the proposed model simulates snow and ice TIR emissivity in a unified theoretical framework. We also explain that the observed emissivity’s sensitivity to snow type is actually caused by the sensitivity to snow density, not grain size. This proposed model can be further used in climate models and remote sensing.

In light of climate change, scaling up in situ eddy covariance (EC) fluxes with Earth observation data has been recognized as a viable strategy for estimating the global terrestrial ecosystem carbon uptake, specifically, gross primary productivity (GPP). Nevertheless, the significant uncertainty in estimation (100–150 PgCyr-1) necessitates the refinement of upscaling algorithms and the use of appropriate satellite data. This technological advancement is particularly sought after in underprivileged regions that are most susceptible to climate crises. Unfortunately, these regions are often constrained by insufficient financial resources and software engineering skills shortages. This study aims to evaluate satellite vegetation proxies [solar-induced fluorescence (SIF); near-infrared reflectance of vegetation (NIRv)] for upscaling GPP and to propose a cost-effective GPP estimation framework called unified FLUXes-GPP (UFLUX-GPP), which can be conveniently operated on a laptop while delivering outstanding performance. The results demonstrated that moderate resolution imaging spectroradiometer (MODIS) NIRv and OCO-2 CSIF exhibited superior performance in the upscaling of EC GPP, with a coefficient of determination ($R^{2}$) of 0.86 and a root mean square error (RMSE) of 1.55 gCm-2d-1. The integration of multiple satellite-derived vegetation proxies holds the potential to enhance the reliability of the model ($R^{2} =0.89$, RMSE =1.41 gCm-2d-1) with an uncertainty of 8 PgCyr-1, especially in tropical and polar regions. The UFLUX-GPP effectively preserved the ecological responses of GPP to the environment and showed promising potential for predicting future GPP. Although the spatiotemporal density of EC towers may occasionally impede the upscaling performance, UFLUX-GPP can convincingly advance a broader use of satellite remote sensing for GPP estimation.

Soil texture is a fundamental soil property and serve as a crucial input to many Land Surface Models (LSMs). However, current soil texture datasets used in LSMs are usually extrapolated from in-situ scale geological surveys, which may contain high uncertainties due to the mismatch in spatial scales. Here, we propose a method to optimize several currently existing soil texture datasets by using a long-term satellite soil moisture dataset. The optimized soil texture datasets may provide an opportunity to improve land surface simulations in LSMs.

In this work, we introduce a novel InSAR processing routine for handling the low-coherence data from long temporal baseline (~ 4 months) L-band ALOS-2 InSAR pairs in the 2016–2017 snow season over the mountainous regions of Colorado, mostly covered by sparse forest. A physical InSAR scattering model was exploited to simulate the InSAR sensitivity of SWE retrieval for forest-covered sites. InSAR-retrieved SWE results are compared with SNOTEL in-situ measurements with a correlation of 0.5 (p-value of 0.01), however, with a much shorter dynamic range of 80 mm versus the actual SWE range of 400 mm. This paper sheds light on using long temporal baseline repeat-pass L-band InSAR data for forest-covered SWE retrieval.

With the passive microwave remote sensing of snow, the high frequency (80-100 GHz) has the advantages of high resolution and fresh shallow snow detection, however, the application of high frequency for snow observation is often limited by atmosphere influence. Until now, few studies have quantified the atmospheric influence of high frequency in snow observations. The scattering and emission of the water vapor and cloud liquid water in the atmosphere cause the increment in Brightness Temperature (TB). To explore the influence of the atmosphere on snow cover observations, we utilize Nordic Snow Radar Experiment (NoSREx) data and the Microwave Emission Model of Layered Snowpacks (MEMLS) to quantify the influence of the atmospheric downward radiance in snow observation by using 90 GHz of the ground-based radiometer. The results show that, in Sodankylä of Finland, atmospheric contribution to ground-based radiometer at 90GHz 50-degree is up to 95.76K (H-pol) and 95.02K (V-pol), with an average of 25.36K (V-pol) and 25.95K (H-pol). The further calculated root mean square errors (RMSEs) of simulated TB with Tdown and observed TB are 41.40K (V-pol)/36.85(H-pol), and of simulated TB without Tdown are 41.25K (V-pol) and 36.72K (H-pol), respectively, the MEMLS slightly increased the simulation accuracy quantitatively of the snow emission without the influence of the atmosphere.

The carbon dioxide (CO2) fertilisation effect has captured worldwide attention, owing to its tremendous potential to challenge existing predictions of future climate. However, quantifying the CO2fertilisation effect has proven to be challenging, given that it is closely entangled with other ecological and environmental processes. Recent years have witnessed significant advances with breakthroughs using theoretical methods to infer the CO2fertilisation effect from eddy covariance tower measurements. Building on earlier findings, this study presents an innovative approach that utilises explainable machine learning techniques — describing the partial dependence of the response variable to each explanatory variable — to quantify the global CO2fertilisation effect from remote sensing platforms with an averaged R2of 0.85. This study provides the first data-driven evidence of the global CO2fertilisation effect and confirms the potential for extrapolation to the globe. The findings suggest that 1) the employment of satellite vegetation proxies contributed to more than 50% of the fitting of gross primary productivity (GPP); and 2) the manifestation of the CO2fertilisation impact demonstrated heterogeneity among various types of ecosystems, and in some cases, an adverse effect was detected in broadleaf forests. Our results have significant implications for preservation and protection of terrestrial ecosystems, particularly for a carbon-neutral future. This study, therefore, provides a valuable contribution to the growing body of knowledge in this area and highlights the potential of innovative analytical techniques to address complex ecological challenges.

The spatiotemporal distribution of snow depth (SD) has a significant impact on the energy and water balances of the Earth’s system. However, passive microwave remote sensing widely used for SD estimation has large uncertainties due to the variations in snow physical properties. In this study, we demonstrate a new method to minimize these uncertainties and to increase the accuracy of SD estimation. Our method is based on the synergy between the passive microwave AMSR-2 brightness temperature (TB) and a physical snow process model (SNTHERM) to estimate snow grain size, snow density and first-guess SD as priors. On one hand, we used TB from three frequencies and removed non-representative ground measurements at the stations to improve deep snow estimation. Then, we applied a machine learning (ML) algorithm based on both the AMSR-2 TB and the SNTHERM simulations to retrieve the global SD. The results showed that the root-mean-squared error (RMSE) of the retrieved SD was 12.4 cm at the meteorological stations. Independent validations showed that our method significantly reduced the SD and snow water equivalent (SWE) underestimation in the mountains compared to the current satellite products.

Recently, spatio-temporal fusion technologies have been rapidly developed and widely applied, which generally require one or more pairs of coarse- and fine-resolution images as reference data and a coarse-resolution image at the prediction time to produce a fine-resolution image at the forecast time. Consequently, most spatio-temporal fusion methods are phase-based and obtain temporal changing information from the coarse-resolution image pairs. Consequently, they usually have rigid constraints on reference data selection using the temporal interval criterion. However, due to the relatively long revisit cycle and cloud contamination, it is difficult to prepare adequate high-quality reference data with little change, especially for large-scale fusion tasks. Therefore, we propose a spatial-based fusion method, which only requires the coarse images at the prediction time and a fine reference image selected by a spatial-spectral-similarity criterion, named super-resolution based spatial fusion with the generative adversarial network (SRSF-GAN). SRSF-GAN uses a super-resolution (SR) module merely on the coarse images at the prediction time, and then perform a multi-scale fusion with the reference fine image. Moreover, the spatial attention mechanism is adopted to achieve dynamic weight tuning, i.e. assigning more weight to the SR image for changed areas and more weight to the fine reference image for unchanged areas. Comparison experiments based on three datasets show that our model can outperform the state-of-the-art methods, and the changes with different spatial and spectral ranges of variation can be recovered. The code will be uploaded to the following website: https://github.com/Zhaosir996/SRSFGAN.

Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ($\text{R}^{2}>$0.8 in polluted areas and uncertainty$\ll 5~\mu \text{g}/\text{m}^{3}$for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.

Real-time water deficit forecast is crucial for disaster mitigation and water management. The Global Forecast System (GFS) is a weather forecast model produced by the National Center for Environment Prediction which could predict future 384-hour atmosphere condition. In this study, we firstly examined 384-hour predicted precipitation, air temperature, relative humidity and wind speed in 2017. The result shows that daily mean temperature forecasts possess high correlation with daily observations from 695 national stations in China, but the total precipitation possess lowest correlation. Within 10 days ahead, GFS predicted maximum temperature shows underestimation bias (-0.82K) and minimum temperature shows overestimation bias (0.89K). Mean temperature has less bias (0.47K). Therefore, we estimated potential evapotranspiration$(\text{ET}_{0})$by using temperature based Thornthwaite method and evaluated the water deficit. The results show that with the forecast days advances, the RMSE of predicted monthly ET0ranges from 10.18mm to 16.67mm and the bias in spring and winter are smaller than those in summer and autumn; There is serious overestimation bias of average GFS predicted precipitation especially in humid zone. The bias of water deficit in humid regions is reduced due to bias of$\text{ET}_0$. Our study reveals that there is less bias of$\text{ET}_0$in cold season and less water deficit in dryer area, improvement of GFS precipitation forecasting accuracy is necessary for better water management.

In this letter, we propose a dual-temporal dual-channel (DTDC) algorithm for soil moisture retrieval by using time-series observations from the Sentinel-1 C-band synthetic aperture radar. This algorithm utilizes the ancillary information of vegetation water content derived from optical images and assumes no variation on the surface roughness during the two consecutive radar measurements. Therefore, with the DTDC backscatter observations, four equations could be established using forward models, while three unknowns (the two consecutive soil moisture values and one roughness parameter) could be solved simultaneously by minimizing a cost function. The algorithm was tested with a series of Sentinel-1 dual-channel (VV + VH) data over croplands (sugarcane and cassava) of Northeast Thailand with an upscaling resolution of 1 km. Results show that the proposed algorithm could well capture the temporal change of soil moisture with root-mean-square errors within 0.06 m3/m3when ignoring days with precipitation, and could achieve a similar spatial pattern of soil moisture as detected from the Soil Moisture Active Passive mission, indicating the Sentinel-1 might be a proper tool for agricultural water management.

Soil freeze-thaw cycles have a profound impact on heat and water fluxes at the land-atmosphere interface and transport in soils. Microwave remote sensing is a widely used technique to detect near-surface soil freeze/thaw states due to significant changes in dielectric properties associated with water phase transitions in soils, where uncertainty remains. This letter proposes a new parameterization scheme for the estimation of unfrozen water content to improve the modeling of soil dielectric properties during freeze-thaw transitions. Predictions from the new model referred to as Zhang-Zhao’s model were compared with dielectric measurements during thawing processes of soil samples collected from Baoding (silty clay soil), Zhangjiakou (loamy sandy soil), and Zhengzhou (clay loam soil) in China. The mean biases of the predictions were 3.25 (4.44 and 2.07 for the thawed value and frozen value, respectively) and 1.54 (2.22 and 0.88 for the thawed value and frozen value, respectively) for the real part and imaginary part, respectively. The model-predicted soil complex relative permittivity (CRP) was highly correlated with measurements, with correlation coefficients ranging from 0.7944 to 0.9865. The normalized root mean square errors of the predictions were 13.72% (real part) and 25.41% (imaginary part).

Snow depth or snow water equivalent in mountainous region is crucial for hydrology, water resources management, meteorological and climate research. Remote sensing can be used for snow depth monitoring in regional scale or global scale. However, the spaceborne remote sensing of snow depth in mountain is challenging because of the sensor sensitivity and spatial resolution problems. Recently, time series Sentinel-1 is used for snow depth retrieval in mountains, which shows encouraging accuracy. In this study, an algorithm to estimate the snow depth in mountainous regions using optical and passive microwave remote sensing observations is proposed, which can be applied in periods before and after the launch of Sentinel-1. The optical and passive microwave remote sensing observations and the Sentinel-1 derived snow depth in 2016-2021 are used to train the snow depth retrieval algorithm using the Extreme gradient boosting (XGBoost) machine learning algorithm. Validations are performed using cross validation method and independentin-situsnow depth data. The cross validation shows correlation coefficient of 0.81 and mean absolute error (MAE) of 0.17m. The correlation coefficient and MAE of predicted snow depth andin-situsnow depth in 2002-2016 are 0.61 and 0.33 m, respectively, which shows significant higher accuracy compared with AMSR-E/AMSR2 snow depth products. The site dependence of the machine learning method is also discussed. The machine learning based snow depth retrieval presented in this study can be applied to mountains globally to the optical and passive microwave remote sensing era.

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties (cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)). The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forests method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients (R) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, wherein the CTT R value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT R value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed herein capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia-Pacific region.

The snow water equivalent (SWE, a measurement of the amount of water contained in snow packs) is an important variable in earth systems. Microwave remote sensing provides a possible solution for estimating the SWE globally. To support radar SWE retrieval, the snow backscattering theory needs to be studied; the forward simulation model needs to be validated against natural snow observations. In this study, a one-winter experiment to observe the time series backscattering coefficient of snow-covered bare soil is reported. This is the first long time series snow-covered soil backscattering experiment that was measured by an imaging radar. The backscattering coefficient was observed at three frequencies covering the X-band and dual-Ku bands, which are of great interest to the snow remote sensing community and are used for SWE estimation in mountains. The calibration of the synthetic aperture radar (SAR) system was conducted manually and carefully to ensure high-quality radar observation data. The observations from our experiment show that in general, the time series backscattering signature of snow-covered terrain is mainly driven by soil freezing, snow grain size growth, and snow accumulation processes. The time series observations for dry snow are modeled by backscattering models with model inputs directly calculated from field measurements. Our simulation results indicate that the time series radar backscattering at three frequencies and four polarizations can be simulated with high accuracy, including the cross-polarization channels. This study provides some key understanding of the time series signature of radar backscattering from snow and provides some key implications for SWE retrieval from radar observations.

The high-frequency all-weather land surface temperature (LST) product generated from the thermal-infrared (TIR) observations of the geostationary meteorological satellite is of great significance to study the diurnal variations in the LST and the land surface energy balance. However, the TIR sensor cannot penetrate the clouds and obtain the desired LST under cloudy conditions. In this study, we developed a physical-based framework for generating high-frequency (hourly) all-weather LST data by synchronizing geostationary satellite TIR observations and simulations of the land surface model (LSM). There are three parts in the developed framework. First, the clear-sky LST was retrieved from the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 using our newly developed temperature and emissivity separation algorithm. Second, the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations were assimilated into the Noah land surface model with multiple parameterization options (Noah-MP) model to generate the all-weather LST. Finally, the retrieved clear-sky AHI LST and Noah-MP assimilated LST were fused using the Ensemble Kalman filter (EnKF) algorithm. In situ measurements from three networks were collected to evaluate the Noah-MP assimilated LST and EnKF fused LST. The bias/RMSE of the Noah-MP assimilated LST and EnKF fused LST were –0.16/3.01 K and 0.15/2.68 K, respectively, under all-weather conditions. Compared to the Noah-MP free-run LST, the absolute values of the bias were reduced by 0.64 K and 0.68 K for the Noah-MP assimilated LST and EnKF fused LST, while the RMSEs were reduced by 0.33 K and 0.65 K, respectively. In addition, the spatial distribution of EnKF fused LST was in good agreement with the retrieved clear-sky AHI LST. The proposed framework in this study was demonstrated to be capable of obtaining accurate high-frequency (hourly) all-weather LST data.

Continuously monitoring surface ozone (O3) spatial distribution and forecasting its variations are beneficial to improving air quality and ensuring public health in China, although achieving this goal faces challenges from currently available observations and retrieval techniques. Hence, we introduce a coupled surface O3estimation framework (LESO) to address these challenges by integrating ground-level observing networks and satellite remote sensing. LESO features easy-to-use deep learning algorithms, independence on chemical transportation models (CTMs), and consistent performance using data from different satellites. LESO includes a Deep Forest 21 (DF21) model to interpolate O3concentration by learning spatial patterns and a Long Short-Term Memory (LSTM) model to forecast O3concentration by learning data from the past. We used sites of city-levelin-situnetworks as the control sites to manifest short-distance O3transportation. Satellite-based observations of O3precursor indicators were incorporated to capture O3photochemical reactions. DF21 explained a larger fraction of O3variability (90 %) with a mean bias error of smaller than 1 μg/m3. We also investigated the impact of the number of training sites on the DF21 performance, which suggested that five training sites could ensure a good DF21 performance for the most areas (R2> 0.85 and bias < 2 μg/m3). The forecasted O3concentration via LSTM showed a good and stable agreement (R2≈ 0.85 and bias < 5 μg/m3) with ground-based measurements for 8-hour, 24-hour, 28-hour, and 72-hour time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O3estimates for broad users.

The moderate resolution imaging spectroradiometer (MODIS) snow algorithm has been used to generate global fractional snow cover (FSC) at a pixel size of 500 m using a linear regression relationship (called “FRA6T”) between FSC and the normalized difference snow index (NDSI). However, the linear relationship is problematic because of the considerable NDSI variation in nonsnow conditions. In this letter, we propose a universal ratio snow index (URSI), which is the ratio of the visible reflectance and the sum of the near infrared and shortwave infrared reflectances. It is called “universal” because it has weak sensitivity under snow-free ground conditions and, therefore, can improve the stability of the linear snow index methodology. A comparison between NDSI and URSI with regard to estimate FSC using the linear snow index methodology is carried out for the Tibetan Plateau. The scatter plots of MODIS NDSI/URSI and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) FSC indicate that a linear relationship can be assumed for both NDSI and URSI for barren land conditions and is more appropriate for URSI than it is for NDSI in forested areas. Validation efforts show that the linear relationship using URSI (designated “FracURSI”) achieves fewer errors in FSC estimation compared with the developed NDSI method (“FracNDSI”), particularly for forested areas and for moderate FSC values. Averaged over all comparisons, the root-mean-square error (RMSE) of FSC estimates for FRA6T is 0.13, and for FracNDSI is 0.12, whereas FracURSI RMSE is 0.11.

In this letter, we modify the extended advanced integral equation model (EAIEM) for electromagnetic backscattering and bistatic scattering from rough surfaces with small to moderate heights. We extend the first-order approximation of the error function as introduced in the EAIEM model to the second order, in a hope to be more suitable for large roughness and high frequency. In addition, a new transition model for the reflection coefficient is proposed to make the dependences explicit on the mean surface curvature, frequency, and dielectric constant, whereas making no use of the complementary term, the effect of inadequate evaluation of this term is mitigated. Comparison with POLARSCAT data for backscattering and with European Microwave Signature Laboratory (EMSL) measurements for bistatic scattering demonstrates the validity of the updated model.

The FengYun-3 (FY-3) series satellite is the second generation of Chinese polar-orbiting meteorological satellite missions. The FY-3D satellite was launched on November 2017 and has been providing valuable data for meteorological applications, including brightness temperature ( TB) data from the MicroWave Radiation Imager (MWRI). In this study, we developed a global soil moisture retrieval algorithm, based on the radiative transfer equation (RTE) for using the FY-3D MWRI TBto continue the soil moisture record from FY-3 satellites. We adopted a new empirical model to compute vegetation water content (VWC) based on the leaf area index (LAI) and canopy height ( H) for vegetation effects correction. The Qpmodel, which addresses the soil surface roughness effects using dual-polarization information, is then used for soil moisture retrieval. Validation of the FY-3D soil moisture was conducted with the in-situ data and the validation results showed encouraging accuracy over a variety of landcovers, with bias and unbiased root-mean-squared difference (ubRMSE) at or below the level of 0.06 m3·m-3. Monthly averaged soil moisture products generated from FY-3D could represent the seasonal changes in soil moisture and show reasonable spatial distribution of soil moisture at a global scale.

Variable atmospheric conditions are typically ignored in the retrieval of geophysical parameters of the Earth’s surface when using spaceborne passive microwave observations. However, high frequencies, for example, 91.7 GHz, are sensitive to variable atmospheric absorption, even in winter’s dry conditions. In this article, the influence of variable atmospheric absorption on snow cover extent (SCE) mapping was quantitatively investigated. A physical method was derived to enable atmospheric correction for variable atmospheric conditions. The total column precipitable water vapor (TPWV) from Moderate Resolution Imaging Spectroradiometer (MODIS) was parametrized into transmittances in this correction method. The corrected brightness temperature at 19 and 91.7 GHz from the Special Sensor Microwave Imager Sounder (SSMIS) was applied to the threshold algorithm for snow mapping over China. Compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) data in winter from 2012 to 2013, for Qinghai–Tibet plateau (QTP), a significant improvement after correction was obtained from February to March over ephemeral and shallow snow, where the largest daily improvement of accuracy is up to 20%. The accuracy (incl. precision, recall, and F1 index) improved on average is from 0.77 (0.60, 0.68, and 0.63) to 0.79 (0.69, 0.7, and 0.68) over the full winter time from December to March. Over forest-rich Northeast China, where snow in winter is thicker, small improvement was observed at the onset of the snow season and over snow margin area. It was evidenced that high frequency is a promising way of snow cover mapping with the proposed atmospheric correction method.

Soil moisture is a key variable for land-atmosphere water and heat energy exchange, crop growth, and the global water cycle. In this study, a method for soil moisture retrieval by utilizing both of the multi-angular and multitemporal features of brightness temperature from SMOS (Soil Moisture and Ocean Salinity) was proposed: first, vegetation optical depth (VOD) and single scattering albedo (ω) were retrieved using SMOS Horizontal-polarized multiangular and multi-temporal observations, then we applied the single-channel retrieval algorithm (SCA) to derive soil moisture. Results from this study showed that the consistency between the retrieved soil moisture by the new algorithm and the in-situ data was better than those from SMOSL3 and SMOS-IC through the comparison with insitu soil moisture from several observation networks with smaller value of unbiased root mean square error (ubRMSE).

Optical and thermal infrared remote sensing images highly integrate spatial heterogeneity information (land surface soil, vegetation and water). This paper evaluated the capacity of Landsat-8 and Moderate-resolution Imaging Spectroradiometer (MODIS) remote sensing indices and empirical relationship models for soil moisture estimations at different depths. The results show that (1) compared with other Landsat-8 indices, shortwave infrared based Surface Water Capacity Index (SWCI) has higher correlation with 10-50 cm depth soil moisture. The comparison based on MODIS daily indices confirms that SWCI can monitor 20 cm soil moisture with more stability; (2) The quadratic polynomial model based on Land Surface Temperature (LST) and SWCI possessed highest accuracy among all empirical models. The average coefficient of determination (R2) increases to 0.257 from 0.150 based on LST-NDVI linear model and 0.176 based on LST-SWCI linear model. Soil moisture analysis at both 30 m and 1 km spatial scale suggest that optical remote sensing could indirectly reflect soil moisture variation with higher precise and more stability in root layer rather than top-most layer.

Tropical monsoon climate in IndoChina Peninsula features dry and rainy season. Microwave remote sensing offers emerging capabilities for hydrological simulation. This paper aims to clarify whether the contributions of remote sensing precipitation on runoff simulation will change due to terrains and model algorithms. We simulated runoff in mountainous area-Yuan River Basin based on remote sensing early version precipitation products by using Soil & Water Assessment Tool (SWAT) model. We also simulated runoff in flat terrain area-Mun-chi River Basin based on remote sensing final version precipitation products by Variable Infiltration Capacity Model (VIC) model. We compared the runoff results against gauge-based CMORPH-AWS and World Meteorological Organization (WMO) interpolated precipitation, and also estimated flood inundation areas in Mun-chi River from 2005 to 2014 based on runoff simulations. The results show that (1) gauge-based precipitation products CMORPH-AWS and WMO precipitation have largest NSE and smallest RMSE for runoff simulation in these two basins. The runoff simulation by VIC model and SWAT model based on TRMM Multi-satellite Preciptiation Analysis (TMPA) remote sensing product have higher correlation with the observations; (2) Runoff simulations based on TMPA can be used for flood inundation area estimation in larger river basin rather than smaller basin or subbasin. Our study reveals that high-quality precipitation products significantly improved runoff simulation accuracy in these two basins. Remote sensing precipitation product TMPA has potential on runoff simulation and flood assessment in remote or observation lacking area in IndoChina Peninsula.

Parameterization schemes for estimating clear-sky surface downward longwave radiation (SDLR) are well recognized for their simplicity and acceptable accuracy, especially at the local scale. The near-surface temperature and/or water vapor are usually used to predict the clear-sky SDLR in a parameterization scheme. Air temperature inversion (ATI) alters the atmospheric state at the near-surface boundary layer and affects the accuracy of the clear-sky SDLR estimation. However, few studies have investigated the impact of ATI on the estimate of the clear-sky SDLR. This article investigated the impact of ATI on the estimate of the clear-sky SDLR using six widely used parameterization schemes. According to the evaluation results using ATI profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database and the Surface Radiation Budget Network (SURFRAD) sites, all the parameterization schemes are sensitive to ATI, and their accuracy is degraded greatly as a whole. The SDLR is underestimated for the ATI profile both in the TIGR database and SURFRAD sites. The best three schemes can achieve the accuracy with bias values of approximately -10 W/m2and root-mean-square errors (RMSEs) less than 20 W/m2for the ATI profiles in the TIGR database. The reason the SDLR is underestimated for the ATI profiles is provided by a simulation study. An empirical method is proposed to correct the impact of ATI. The accuracy of all the parameterization schemes is remarkably improved at SURFRAD sites after correcting the impact of ATI, with the absolute values of bias and RMSEs less than 10 and 20 W/m2at SURFRAD sites.

In this article, we developed a hybrid method to estimate surface shortwave radiation (SSR) for the new-generation Himawari-8 geostationary satellite. This hybrid method combines the advantages of a deep neural network (DNN) with high speed and radiative transfer model (RTM) to achieve high accuracy: the RTM provides training data for the DNN under various cloud and aerosol conditions (including heavy aerosol loadings). Moreover, our hybrid method can simultaneously output the byproducts of photosynthetically active radiation (PAR), ultraviolet A (UVA), and Ultraviolet B (UVB), the direct and diffuse components at the surface, and the upward solar radiation at the top-of-atmosphere (TOA). The trained DNN was applied to the Himawari-8 satellite atmospheric products for 2016 and comprehensively validated using a total of 118 stations from four networks located in the full-disk regions of Himawari-8. The results showed an RMSE of 125.9 Wm-2for instantaneous SSR, 105.4 Wm-2for hourly SSR, 31.9 Wm-2for daily SSR, and respective mean bias error (MBE) scores of 8.1, 27.6, and 12.3 Wm-2. The hybrid method developed in this study performed well, achieving high accuracy and high speed, and it is capable of providing near-real-time SSR estimates for many applied energy fields.

The knowledge and understanding of intra- and inter annual characteristics of canal water is crucial for agricultural water management. The narrow shape of canal greatly limits the application of moderate-resolution remote sensing technologies. Based on newly available Sentinel-2 Multispectral Instrument (MSI) imagery with frequent revisit and higher spatial resolution, we identified variation of water/bank boundary by Roberts, Sobel, Prewitt, Laplacian of Gaussian and Canny 5 edge detectors and furtherly compared the water width results with estimation by ground measurement. The preliminary results show that all detectors can successfully monitor seasonal variation of canal water surface. Canny detector is most stable among 5 methods for time series monitoring, although overestimated the water width during dry period. Our methods and results reveal the great potential of Sentinel-2 imagery for canal water utilization and irrigation management.

We discussed atmospheric correction of passive microwave over China in the snow covered area. SSM/I brightness temperature, radiosonde observation database and MODIS water vapor product (MOD05) were used to estimate the atmospheric influence. The traditional gradient SWE algorithm before and after the atmospheric correction was compared, and results showed atmospheric influence was bigger over snow cover area. It is about 4-5K over the whole China. In the QTP (Qinghai-Tibet Plateau) area, the atmospheric influence was less than other areas in China; also QTP always had thin snow, so the traditional gradient SWE algorithm could not perform well. The gradient algorithm at high frequency was tested, and the results showed even under the original brightness temperature difference at 19 GHz and 85.5 GHz, the gradient (19 GHz-85.5 GHz) performed good consistency with IMS products.

Cloud plays a significant role in the study of the Earth's radiation balance. Particularly, it is still a big challenge to derive longwave radiation under cloudy-sky conditions for the community for a long time. In this paper, a scheme to estimate the upward longwave radiation (LWUR) under cloudy skies is proposed by accounting for the solar-cloud-satellite geometry (SCSG) effect. The application of the new method to MODIS data indicates that the new scheme can work well and the LWUR under the cloud layers can be successfully recovered. And accordingly, the fraction of valid LWUR for a typical image is correspondingly improved. Although MODIS data was employed here, this method can be applied to any optical remote sensing images as long as the required parameters for correcting the SCSG effect are provided.

Land surface shortwave downward radiation (SWDR) is generally defined as incident solar energy over land surfaces in the shortwave spectrum (300-3000nm). As one of important parameter of the land surface radiation budget (SRB) and many land process models, it is usually required as part of the input variables to address a large variety of scientific application issues in fields of agricultural management, climate trends, ecological forecasting, reusable energy production, public health and so on. Currently, regional or global SWDR can be estimated from polar-orbiting or geostationary satellite observations based on empirical or physical-based retrieval models. These remote-sensed SWDR products with fine spatial resolution provide us valuable information about a series of critical Earth science problems and enable us to gain more insight into the planet we live on. In this paper, an improved method was proposed based on a look-up table approach via MODTRAN-5 simulations to estimate clear-sky instantaneous SWDR and its direct component from TOA radiance of Moderate Resolution Imaging Spectrometer (MODIS) observation. Ground measurements from seven SURFRAD sites are used for validating the algorithm and the result shows good accuracy. This approach is suitable for producing clear-sky instantaneous SWDR and its direct component at the spatial resolution of finer than 5 km.

The Soil Moisture Experiment in the Luan River (SMELR) in 2018 was carried out toward developing of new satellite mission opportunities in China. It was designed to explore several scientific and technical questions regarding to soil moisture remote sensing and its application. Various passive, active microwave and optical observations were collected by both airborne and satellite platforms. Ground-sampling of soil moisture/temperature, vegetation and roughness were conducted close in time to the airborne acquisitions at 200 to 2000 m scales. Ground-based measurements of microwave emission and scattering, emissivity and reflectance spectra, evapotranspiration and radiation were conducted along the flight areas. Moreover, two in-situ networks that cover the Shandian (100×100 km) and Xiaoluan (25×25 km) river basins were established to provide continuous measurements of soil moisture and temperature profiles (3-50 cm). This paper describes the overview of the experimental design, obtained data sets and initial results.

The ground-based microwave radiometer has a strong ability to observe the earth's surface throughout all-time and allweather conditions, which is widely used in the experiments of soil moisture, freeze-thaw and other surface parameters of microwave remote sensing. The observed data could not only be used to establish and verify microwave radiation model and interpret the transmission process and mechanism, but also could be used to improve the retrieval algorithm of surface parameters by optimizing different target parameters. Base on the observation experiment of soil moisture, the design of its experimental scheme was described, and the multi-frequency microwave radiation characteristics of soil moisture were analyzed under different land-cover types in this paper.

The Microwave Radiation Imager (MWRI) on board FY-3B is a passive microwave instrument. Since the launch of FY-3B, MWRI has provided large amount of data and successfully supported applications on land surface parameters estimation. These achievements indicate that FY-3B/MWRI has the potential in vegetation monitoring. In this study, we extended our algorithm for deriving MVIs from AMSR-E to that under FY-3B/MWRI sensor configuration. We hope that this will also contribute to long-term vegetation monitoring combining AMSR-E and AMSR2 data.

Active and passive microwave remote sensing have their particular characteristics. Active microwave is more sensitive to vegetation cover and surface soil roughness, while passive microwave is more sensitive to the surface soil moisture. A new retrieval algorithm has been proposed by using Aquarius and SMAP satellites' active and passive microwave observations to retrieve soil moisture products in different spatial scales. The retrieval results of soil moisture have been verified with the ground observations of soil moisture and temperature measurement (SMTM) stations in Naqu, China. The advantages and disadvantages of the algorithm have also been evaluated to analyze the practical value of the new soil moisture retrieval algorithm.

In this study, a model investigation of a ground-based active and passive microwave experiment for snow and frozen soil is presented. The experiment is carried out from October 2017 to March 2018 in Xinjiang, China. Ground based SAR and microwave radiometers are used to measure the multiple frequency and multiple polarization backscattering coefficient and brightness temperature of snow covered soil. Microwave scattering and emission model of snow and soil are used to study the measurement results, and the microwave signature of snow and frozen soil are studied by model simulations, and this is the fundamental of snow parameter retrieval from active and passive microwave observations.

Land surface shortwave downward radiation (SWDR), as one of major components of the surface radiation budget (SRB), plays an important role in the fields of atmospheric, oceanic, and land processes, and ultimately influences the Earth's climate as well as the matter and energy cycle of the earth system. Currently, regional or global SWDR can be obtained either from reanalysis products or from satellite observations based on statistical or physical-based retrieval models. Although great efforts have been made to assess the applicability and accuracy of those different SWDR datasets, few studies have been conducted to evaluate the performance of the Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN) Edition 3a and Himawari-8 SWDR datasets over the Tibetan Plateau. In this study, the both SWDR datasets are validated against in-situ data at 11 ground sites from the China Meteorological Administration (CMA). It is found that the Himawari-8 SWDR product has a slightly higher accuracy in these two SWDR datasets but with a significantly higher spatial resolution (5km). The mean bias is 1.7 W/m2for CERES-SYN and -1.6 W/m2for Himawari-8, respectively, the root mean square errors (RMSE) are 31.3 W/m2for CERES-SYN and 31.2 W/m2for Himawari-8, respectively. Mean coefficient of determination (R2) of the two datasets are both over 0.8. It is clearly that CERES-SYN tends to overestimate SWDR somewhat while the Himawari-8 has slight underestimation over the Tibetan Plateau. The findings in this paper can be valuable for hydrological, ecological, agrometeorological and biogeochemical applications and researches.

In this study, a quantitative Freeze/thaw (F/T) index from passive microwave observations is defined, and is assumed to be linearly correlated with land surface temperature from thermal infrared observations. Thus, a linear regression method is proposed and verified to be effective over a multiscale network of Naqu of the Tibetan Plateau. Then, we implement and test the proposed approach to generate daily F/T state maps at a 5-km spatial resolution through the fusion of AMSR2 and MODIS data. It is found the high resolution F/T maps agreed well with ground reference observations of 0-cm soil temperature, with an overall accuracy of ~86.6%. This study provides new insights for high-resolution F/T mapping beyond the (Soil Moisture Active Passive) SMAP mission.

Land surface temperature (LST) is a key variable in the processes of energy and water balance between Earth's surface and atmosphere. To date, considerable researches have been focused on this issue, especially the thermal infrared (TIR) methods. Whereas TIR measurements are only limited to clear-sky condition, no observation is possible under cloudy conditions. While, passive microwave (PMW) as an alternative to the TIR measurements can penetrate the clouds. In this paper, the optical LST was derived from AMSR-E brightness temperatures by building a strong linear relationship over the continental United States. Unlike the previous studies, the algorithm was conducted by further using the corresponding ground measured LSTs which consist of both clear and cloudy conditions. The linear relationships were built on three sub-regions which is defined by NDVIs. The results show that the root-mean-square error (RMSE) of derived LST ranges from 2.50K to 3.23K for ascending overpass and 1.54K to 2.71K for descending track. This accuracy is proven to be better than existing work.

Surface radiation balance plays a vital role in the earth surface system and affects many biogeophysical processes. As one of components of surface energy balance, longwave downward radiation (LWDR) is considered as the most poorly estimated radiation component, and its uncertainty is regarded as substantially larger than other terms of surface energy budget. In this paper, we applied the multivariate adaptive regression splines (MARS) method to derive LWDR based on MODIS thermal infrared bands top of atmosphere radiances and ground-based LWDR measurements. In model fitting process, the RMSE, bias and R-square value are 25.49 W/m2, -0.000 W/m2and 0.88, respectively; and in model validation stage, the RMSE, bias and R-square value are 25.63 W/m2, 0.481 W/m2and 0.87, respectively. The newly proposed model demonstrates comparable accuracy with other LWDR estimating methods and proves that MARS method is very useful in remote sensing based LWDR estimation.

The matrix doubling (MD) model is a numerical solution to the radiative transfer equation. It can achieve better accuracy in simulating microwave signals from vegetated terrain by considering multiple-scattering effects. However, it is difficult to apply the MD model to retrieving work due to its high complexity. This letter presents a case study performed on corn to demonstrate a multiangular (5°-65°), multiband (1.4/6.925/10.65 GHz) microwave emission model considering multiple-scattering effects by parameterizing the MD model. The simulated emissivity differences between the theoretical model and parameterized model are small. The mean absolute percent errors are all less than 1%, and the root mean square errors (RMSEs) are all within the range of 10-3. Validations using airborne polarimetric L-band microwave radiometer data and ground-based trunk-mounted multifrequency microwave radiometer data indicate that the parameterized model achieves good accuracy with overall RMSEs within 8K at all three bands.

Water Cycle Observation Mission (WCOM) is a mission dedicated to synergetic observations of global water cycle parameters, with emphasis on soil moisture, ocean surface salinity, snow water equivalent and frozen/thaw. WCOM implements its observation requirements by measurement of microwave emission/scattering of both the frequencies sensitive to the key parameters and also the auxiliary frequencies providing necessary atmospheric and surface roughness corrections. In order to satisfy this measurement requirements, WCOM is equipped with payloads with combination of active and passive microwave sounding capabilities of frequency from L-band to W-band. Dual Frequency Polarized Scatterometer (DFPSCAT) is one of the three payloads onboard the satellite of Water Cycle Observation Mission (WCOM). DFPSCAT is an X/Ku band rotating pencil beam scatterometer with 2-5 km resolution and 1000km swath for mapping of snow water equivalent (SWE) and freeze-thaw process. DFPSCAT achieves fine resolution by linear frequency modulation pulse compression along the elevation direction, and by unfocused synthetic aperture processing (a technique where the Doppler effect is exploited to synthesize a longer aperture to achieve an improved resolution), as well as super-resolution reconstruction by oversampling in the direction of the azimuth. Based on the payloads of WCOM mission, especially with X/Ku scatterometer and L/Ku/Ka radiometer active/passive observations, there are obvious advantages in snow water equivalent retrieval. The atmospheric correction of active and passive data should be performed before the retrieval. The estimation of SWE mainly rely on the high resolution X and Ku band scatterometer, and combined active/passive retrieval can provide more reliable SWE product. The retrieval method of SWE from X/Ku scatterometer is described, and combined active/passive retrieval is also briefly described.

There are two passive microwave sensors onboard the Water Cycle Observation Mission (WCOM), which includes a synthetic aperture radiometer operating at L-S-C bands and a scanning microwave radiometer operating from C- to W-bands. It provides a unique opportunity to disaggregate L-band brightness temperature (soil moisture) with S-band C-bands radiometer. In this study, passive-only downscaling methodologies are developed and evaluated. Based on the radiative transfer modeling, it was found that the TBs (brightness temperature) between the L- and S-bands exhibit a linear relationship, and there is an exponential relationship between L- and C-bands. The downscaling method with L-S bands with the same incident angle was first evaluated. The RMSE are 3.19 K and 1.98 K for H and V polarization respectively. The downscaling method with L-C bands is developed with different incident angles. The RMSE are 2.97 K and 2.68 K for H and V polarization respectively. These results showed that high-resolution L-band brightness temperature and soil moisture products could be generated from the future WCOM passive-only observations.

In hydrological investigation, the liquid water content in snow pack is required which is important for modeling and forecasting snow melt runoff. Active microwave remote sensing has the potential of estimating snow parameters. In this study, we estimates snow wetness based on quasi-crystalline approximation - dense media radiative transfer (QCA-DMRT) model at X (10.2 GHz) and Ku (16.7 GHz) bands and at dual-polarization (VV and VH). At first, snow volume backscattering and air-snow surface backscattering were decomposed from wet snow backscattering by analyzing X-band and Ku-band radar wet snow database generated from QCA-DMRT model. The database covers the most possible wet snow and air-snow surface physical properties conditions. Then using the surface scattering component to estimation snow wetness based on the relationship between the surface scattering and snow wetness.

Atmospheric water vapor is a key parameter in the study of global water cycle and climate change. As water vapor is also an important kind of greenhouse gas, knowledge of decades of total precipitable water (TPW) is very important in understanding the effect of atmospheric water vapor on global water cycle and climate change. In this study, a decade of daily and monthly TPW product with a spatial resolution of 0.25°×0.25° is produced in all-weather condition based on the combination of TPW from MODIS in clear sky condition, TPW retrieved from AMSR-E in all-weather condition over land, and TPW derived from AMSR-E L2 Ocean product in all-weather. As a validation source, TPW obtained from globally distributed SuomiNet GPS network will be used to validate the accuracy of the ten years TPW product in daily and monthly scale.

The effective vegetation optical depth (EVOD), plays an important role in vegetation monitoring and land key parameters retrieval. In this study, we attempt to retrieve the EVOD using time-series AMSR-E data at SNOTEL[839] site for a case study. It is found that at site scale the retrieved EVOD can capture the overall trend of the vegetation growth. However, phase differences are found between EVOD and NDVI. Different overpass times can also affect the exhibition of EVOD.

In this study we examined the linear decomposition and relationship between the SMAP observed Polarization Ratio into surface soil moisture and vegetation. Temporal linear regression, per each SMAP pixel, is performed to estimate the decomposition coefficients. Variances (explained variance) in PR is predominantly dominated by dynamics of surface soil moisture and degrades with increasing vegetation amount. Although PR, by itself, is high in arid and semi-arid regions, due to lack of moisture and vegetation dynamics, the explained variance is very small.

Land surface longwave (LW) radiation (or longwave radiative flux), including longwave upwelling (LWUR), downward (LWDR) and net radiation (LWNR), are key components of the total energy that drives the surface energy balance at the interface between the earth's surface and the atmosphere. The importance of LW radiation in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a tremendous attempts have been made to derive LW radiation from space only valid under clear-sky conditions leading to difficulty of utility of remote sensing-based LW radiation products in most land models due to their spatial discontinuity. Although few studies focused on LW radiation estimation under cloudy-sky conditions, while their global application are still problematic. In this paper, novel strategies are proposed aiming to derive high resolution cloudy-sky LWDR and LWUR by fusing collocated optical and microwave satellite data. The results reveal that the new approaches work rather well, thus, more importantly, providing unprecedented possibilities for generating high resolution global LW radiation datasets.

Surface mass fluxes and ice flow mainly governed topography and layering of the PLD. A new type satellite-borne Mars penetrating radar is designed and will be launched in 2020. The radar system transmits linear polarization wave and receives by two dipole antennas mutually perpendicular. In this paper, the model of PLD with an anisotropic COF is build. Then, a simulation experiment is done to verify the feasibility of this method. The result shows that the Mars penetrating radar can be used to detect the depolarization effect caused by the anisotropic COF.

Light scattering models of snow are very important for remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient, and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of grain shape and microstructure modeling in light scattering models of snow is confirmed by the simulation results' comparisons. Using the new model, snow surface grain size and pollution concentration can be retrieved using MODIS land surface reflectance, the retrieved snow grain size and snow surface area is validated using ground observations.

With the current and planned scientific missions related to ocean observation, there is a need to understand the relevant scattering physics and sensitivity so as to provide a physical basis for retrieving geophysical information from ocean-scattered signals. Sea spectrum is one of the factors to be determined in the retrieval. Since difference in sea spectrum manifests itself mostly at sea waves of intermediate and large scales, it is desirable to include sea waves of all scales (large, intermediate, and small scales) at the same numerical simulation. In this study, we extend the stochastic second degree iterative algorithm with sparse matrix and Chebyshev approximation (SSD-SM-Cheby) method that we previously developed to the analysis of sensitivity of bistatic scattering pattern upon sea spectrum at both L and C bands at low wind speed. Four popular sea spectra are considered. Results are provided and discussed.

Ground-based microwave radiometric measurements were carried out in 2016 by using a multi-frequency microwave radiometer at L, C and X bands (1.4, 6.925 and 10.65 GHz). The aim of the experiments was to explore multi-frequency microwave emission characteristics of the soil freeze-thaw process for model and algorithm development for the future Water Cycle Observation Mission (WCOM). Measurements were carried out on pastureland in Chengde, Hebei Province, which belongs to seasonally frozen ground of China. Soil temperature and soil moisture profiles, the frost depth, and meteorological observations were synchronously collected. It has been found that microwave radiation has different responses to soil freezing and thawing process at different frequencies.

Atmospheric transmittance is an important factor for atmospheric correction in the inversion of land surface parameters. Under nonprecipitating conditions, microwave atmospheric transmittance in the X-, Ku-, and Ka-bands is mainly determined by oxygen, water vapor, and cloud liquid water content. In this letter, radiosoundings from 119 stations in China, performed twice a day from January 2011 to July 2014, were used in the Salonen-Uppala cloud detection algorithm to distinguish cloud layers from layered atmospheric profiles and to estimate the cloud liquid water content therein. The resulting atmospheric transmittances at frequencies of the Advanced Microwave Scanning Radiometer-Earth Observing System over China were estimated using Liebe's millimeter-wave propagation model and Mie theory. Atmospheric transmittance maps were obtained by interpolating the results from individual sites, driving a climatological database for over China, which may be used to correct for atmospheric influence in surface parameter retrievals. The simulated transmittances were validated through a series of on-site field experiments in the North of China. We compared simulated atmospheric brightness temperature with measurements performed in situ using a ground-based radiometer system. The correlation coefficients between the measured and simulated values were 0.97, 0.99, and 0.98, in the X-, Ku-, and Ka-bands, respectively, with a root-mean-square error of 0.6, 1.6, and 9.5 K, respectively.

Active microwave remote sensing is a promising tool for global snow water equivalent (SWE) mapping. However, many studies have shown that more information is needed to estimate the SWE accurately. A very important problem is characterizing the snow grain size and quantitatively separating the effects of grain size and snow mass on the backscattering magnitude. In this letter, QuikScat backscattering coefficient data are used to estimate snow depth, with the snow grain size, density, and temperature estimated from the snow thermal model, driven by the Global Land Data Assimilation System forcing data. Considering the spatial resolution and the incident angle of the enhanced resolution QuikScat data, the estimation is applied to flat farm land and grass land. The snow thermal simulated snow grain size was found to be well correlated with the QuikScat measurements of the effective scattering coefficient, and the relationship between them is calibrated using data from one site in 2008-2009. Then, this calibrated relationship is used to estimate the snow depth at other sites. The results show that the snow thermal model simulated grain size can be used to improve the snow depth estimation from active microwave remote sensing.

The snow in high-mountain Asia (HMA) is of great importance, as it is very sensitive to the climate change. Air temperature and precipitation shifts/increases will be reflected in the timing of snowmelt onset. In this letter, a new algorithm is proposed to determine the snowmelt onset date from active and passive microwave remote sensing data, and the spatial and temporal pattern of snowmelt onset in HMA is studied using active and passive microwave remote sensing for the first time. Over 35 years of passive microwave data and ten years of active microwave data are used to derive the melt onset date in HMA. The active microwave data has 4.5-km resolution so that more detailed spatial pattern of snowmelt onset date can be derived compared to the 25-km resolution passive microwave data. Under climate change background, time series analyses of the snowmelt onset date in HMA are conducted to study the snowmelt onset time changes in recent 35 years. This letter provides an objective evidence of climate change impact on the cryospheric system. Time series analysis shows that the snowmelt onset date is becoming earlier in HMA region during 1988–2015, except the Karakorum Mountains and part of the western Kunlun Mountains. Mean air temperature is compared with the time series snowmelt onset date and the results show that there is strong correlation between mean air temperature and average snowmelt onset date. A 4.5 days/degree rate of snowmelt onset date advancing is found.

In this paper, a physically based numerical electromagnetic approach, by solving Maxwell's equations, is developed to investigate the scattering and the emission from double-layered media, where both the top and bottom interfaces are random sastrugi surfaces with a random horizontal shift between the two corresponding negative-slope facets of both interfaces. Numerical simulations are illustrated for bistatic scattering and four Stokes parameters at L-, C-, X-, and K-bands. Results indicate that the bistatic scattering and Stokes parameters are asymmetric for the double-sastrugi media, whereas for the other two structures, sastrugi surface alone and sastrugi surface with a planar bottom boundary, their scattering and the first two Stokes parameters are even symmetric relative to the azimuthal angle of 90°, and the third and fourth Stokes parameters are odd symmetric. In particular, the Stokes results no longer have the strong coherent fluctuations in angular variations shown in periodic double-layered structures because the random double-layered structure eliminates the coherent interference. It is interesting to observe that the internal total reflection may cancel if the coupled interactions between the two sastrugi interfaces are very strong, which results in decreasing scattering at the L-band, and its Stokes parameters are similar to those of the sastrugi alone. Numerical results also reveal that the maxima of cross-polarized specular scattering from double-sastrugi structures can be observed at cross incidence; however, they are at a deep dip for the latter two statistical symmetric structures. The sastrugi-sastrugi structure, being geometrically anisotropic, is capable of generating strong cross-polarized scattering and, subsequently, significant amounts of the third and fourth Stokes. The azimuthal patterns, at a viewing angle of 55°, of four Stokes parameters, although more complex, are feature rich where two striking extrema of third and fourth Stokes are presented. Compared with the L-band, the C-band presents strong azimuthal dependence of four Stokes. The simulation results offer deeper insights into the scattering and emission process in sastrugi surface and may lead to better retrieval of surface parameters from radar or radiometric measurements.

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies. The WCOM is expected to be implemented during the 13thfive-year-plan period (2016–2020).

Global mapping methods of soil moisture are developed in this study using WCOM (Water Cycle Observation Mission) active/passive multichannel observations. Based on three payloads of WCOM mission with L-S bands passive observations and X/Ku active/passive observations, there are obvious advantages in soil moisture retrieval. Through evaluations of the Advanced Integral Equation Model (AIEM) simulating dataset, it was found that the bare surface emission signals of L-S bands are essentially equal. Accounting for the vegetation effects, the method of soil moisture retrieval with L/S bands can increase soil moisture estimation accuracy, compared with using single L band observations. The RMSE of soil moisture estimated from PALS(Passive Active L- and S-band Sensor) dual frequency radiometer data is 0.048m3/m3with L band, and RMSE is raised to 0.035m3/m3with L/S bands. According to the active/passive configuration of WCOM, we developed two soil moisture downscaling methods with active/passive measurements and L-S bands passive measurements, and obtain high resolution soil moisture products. Based on zeroth order approximation model, it was found that there are linear relationship between the L-S band TBs (brightness temperature), and also between the active/passive observations. The RMSE of the spectral active/passive downscaling soil moisture is 0.0459m3/m3. The RMSE of downscaling results of L/S band TB are 2.8 K and 1.7 K for V/H polarization respectively. These results showed that we can get high accurate and high resolution soil moisture products from WCOM and then benefit to various applications.

A new method was proposed in this paper to estimate optical depth (τ) and single scattering albedo (ω) of short vegetation over north China plain based on the refined physical expressions of Microwave Vegetation Indices derived from the parameterized first-order emission model. Comparisons with MODIS 16-day Normalized Difference Vegetation Index (NDVI) showed that, although there were some differences between the variations of single scattering albedo, optical depth and NDVI, the variation trends of the three parameters are very similar to each other. They all mainly expressed two regular variations during the whole year of 2010 that firstly increased and then decreased. The first one happened during February to late June, and the second one happened during early July to late September. They are closely consisted with the phenology of winter wheat and summer corn, which are the mainly two crop types in north China plain.

Snow cover is one of the most important components in predicting global water and influence the global heat budget. In this study, we reported the spatial and temporal distribution of seasonal wet snow cover derived from enhanced resolution (4.45 km/pix) QuikSCAT Ku band backscatter measurements in the winters of 2002-2009 of China. A threshold method was used to detect melt events. The main melt event was identified by the longest of melt duration. The wet snow map derived from satellite data over China was compared with in situ snow and air temperature measurements from Global Historical Climatology Network.

Snow water equivalent is a key parameter in hydrology and climatology. In this study, we estimates snow water equivalent based on bi-continuous vector radiative transfer (VRT) model at X (9.6 GHz) and Ku (17.2 GHz) bands radar scatter. First, the relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from bi-continuous VRT model. Then, cost function with constraints is used to solve effective albedo and optical thickness and absorption part of optical depth can be obtained from these two parameters. The backscattering signals before snowfall are regarded as ground backscattering signals under snow cover. We finally retrieve snow water equivalent from backscattering signals with X and Ku bands at VV and VH polarizations. The retrieval algorithm is validated utilizing ground measurements from NoSREx (Nordic Snow Radar Experiment) campaign.

Water Cycle Observation Mission (WCOM) is a mission dedicated to synergetic observations of global water cycle parameters, with emphasis on soil moisture, ocean surface salinity, snow water equivalent and frozen/thaw. WCOM implements its observation requirements by measurement of microwave emission/scattering of both the frequencies sensitive to the key parameters and also the auxiliary frequencies providing necessary atmospheric and surface roughness corrections. In order to satisfy this measurement requirements, WCOM is equipped with payloads with combination of active and passive microwave sounding capabilities of frequency from L-band to W-band. In addition to the multiple frequency requirement, the requirement for spatial resolutions and swath width leads to large aperture antennas and scanning of antenna beams, the accommodation of the three payloads becomes the major challenge for the design of the WCOM satellite. In order to satisfy the temporal resolution requirement, the design of data downlink and ground segment are also discussed. In this presentation, a preliminary design for WCOM satellite is provided, some technical issues need further investigation are also discussed.

Sea surface salinity (SSS) has a profounding influence on the the exchanges of matter and energy at the air-ocean interface. It is also a driving force for ocean circulations. The capability of accurate measurement of SSS with high spatial and temporal resolution shall be a desirable boost to the global climate models. The scientific missions Soil Moisture and Ocean Salinity (SMOS) [1] and Aquarius [2] are specific to this end.

Soil moisture is an important parameter for the terrestrial water, energy and carbon cycles. Measurement of the dielectric properties is critical for near-surface soil moisture estimation using microwave remote sensing. A number of empirical and semi-empirical models have been derived to describe the relationship. In this study, four widely-used soil dielectric models for soil moisture retrieval, the Wang-Schmugge model, the Dobson model, the Hallikainen model, and the GRMDM (generalized refractive mixing dielectric model) model were compared and the effect of uncertainties of them on soil retrievals were also investigated. Theoretical values of soil dielectric constant were calculated for three soil textures (60% sand, 20% clay; 30% sand, 30% clay; and 20% sand, 60% clay) with the four soil dielectric constant models. The effective soil dielectric constants calculated by models are seen to differ more as the sand content of soil increases. Errors induced by wrong soil texture may exceed 10% for the worst case. The results indicate that the errors induced by the alternative dielectric mixing model may be over the standard accuracy requirement (4% volumetric soil moisture) in soil moisture retrievals.

Water vapor plays an important roles in the Earth's energy and water cycles. Compared to optical remote sensing, microwave remote sensing has the advantage to acquire information of atmosphere under cloudy condition. Up to now, there is no published reliable total precipitable water product over land from AMSR2 due to effect of high land surface emissivity in microwave band. In this study, an improved total precipitable water retrieved algorithm for AMSR2 will be developed based on previous studies. In the retrieval algorithm, a land surface emissivity parameter estimation model is developed using combination of AMSR2 and MODIS observation. The precisely estimated surface emissivity parameter is the key parameter in the retrieval of total precipitable water. Finally, the total precipitable water was retrieved using a look-up table, and it is validated using total precipitable water observed from global distributed GPS.

So far, the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) are the only two missions designed to measure the column-averaged CO2dry air mole fraction (XCO2). To improve our understanding of global carbon source and sink, these two XCO2products are compared in this study. The result reveals that the OCO-2 XCO2product show the wider spatial coverage than those of GOSAT from 30°S ∼ 90°N latitude. At the same time, GOSAT and OCO-2 XCO2products shows a good agreement with correlation coefficient (R2) of 0.69 and bias of −1.0 ppm. However, the discrepancy is still existed in some region. The discrepancy between these two products implies that it is necessary to make them complement each other to better improve our knowledge of global carbon cycle or even climate change.

Infrared Difference Dust Index (IDDI) is often used as a satellite dust product to detect the change of mineral dust aerosols in the atmosphere. And aerosol optical depth (AOD) is also a main measurement for mineral dust aerosol. To qualify dust loading on the regional or global scale, it is very necessary to understand the relation between IDDI and AOD. Therefore, this study investigates the impact of sensitivity factors including surface temperature and surface type to the IDDI by using MODTRAN (Moderate Resolution Transmittance code) model to better evaluate the relation between IDDI and AOD. The result shows that the simulated IDDI from MODTRAN are extremely sensitive to the surface temperature and surface type. The IDDI is growing with the increased surface temperature. And the sensitivity of farm and forest type to IDDI is similar and their difference is very small. But the sensitivity of desert type and ocean to IDDI is obviously different from other two types and the surface type is also a key parameter to IDDI. The result also implies that there is an exponential relationship between IDDI and AOD. These results will be very helpful to further establish the relation between IDDI and AOD.

Effective vegeation optical depth (VOD) is widely used for land surface parameters retrieval. It is necessary to reseach the relationship between the effective value and the theoretical value of VOD. In this paper, we have used a least square method to obtain effective VOD using simulation data and explore the relationship between effective VOD and theoretical VOD. This research might contribute to land surface parameter retrieval using passive microwave remote sensing.

Land data assimilation system (LDAS) has been a powerful tool to optimally combine the advantages of microwave remote sensing and land surface model together and then improve the accuracy of land surface fluxes and status estimation. In this study, we developed a land data assimilation system, in which the water imbalance is constrained by using a multiply times assimilation approach. The first step is a standard variational assimilation operating at normal assimilation windows. And then the LDAS will run at an optimal assimilation window for several times to minimize the water imbalance accumulated in the first step. The LDAS is tested over CEOP Mongolia network. The microwave brightness temperature (TB) observation of AMSR-E are assimilated into the LDAS. Sensitivity test which considering the impacts of assimilation frequency and length of assimilation window are conducted. The results show that for soil moisture assimilation 48 hours window with 2 times assimilation could control the accumulated residual into the acceptable region.

The seasonal snow over the Tibetan Plateau is an important parameter for the regional energy and water cycle. The snow there experience a different trend compared to that of the Northern Hemisphere in the past decades. Because of the shallow and patchy snow with a fast precipitation process and cloud occurrence, the traditional snow algorithm (18GHz and 27GHz gradient brightness temperature) experiences an insufficient accuracy there when employed the global coefficients. In this paper, the atmosphere condition was assumed to play a key role. Through the simulation, and the realistic atmospheric balloon measurement, the transmittance difference between Plateau and low land area was calculated, and the correspondent cloud influence was analyzed. As a result, we need to consider the cloud and water vapor effect, the method over the Tibetan Plateau needs to consider these two components, i.e. the existence of clouds over snow cover areas, and the atmospheric water vapor content, which typically separates high elevation regions from low lying landscapes.

Three years radiosoundings measurement at China were inputted in the Salonen-Uppala cloud detection algorithm to distinguish cloud layers, and to estimate cloud liquid water content therein, then atmospheric transmittances of China at frequencies of the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) were estimated using MPM and Mie theory. The validation expressed a good agreement with the field experimental observations. A comprehensive temporal and spatial analysis has been executed, appeared that microwave atmospheric transmittances were much dependence on water vapor distribution, and transmittances were relatively stable from October to March, but fluctuated obviously in summer. Transmittances of northwest China were higher than those of south China, and the highest transmittances were in Qinghai-Tibet Plateau region. The correlation coefficient between microwave atmospheric transmittance and MODIS total column precipitable water vapor (MYD05) in January at 18.7, 23.7, 36.5 and 89 GHz was 0.904, 0.923, 0.847 and 0.879 respectively. The relationship between monthly averaged microwave atmospheric transmittances and MYD05 could be used to estimate the high resolution microwave atmospheric transmittances products. The results prepared to ensure atmospheric effect in the applications of passive microwave remote sensing.

Soil moisture retrieval is important in microwave remote sensing, but its high dimensional and non-linearity make it difficult to realize. In this paper, an improved Fruit Fly Optimization Algorithm (IFOA) to optimize the Least Squares Support Vector Machine (LSSVM) Model is used for soil moisture retrieval. The sampling data for training and test is generated by using Advanced Integral Equation Model (AIEM). To verify the performance of IFOA optimized LSSVM (IFOA-LSSVM), we apply it to the soil moisture retrieval in L and C bands and compare the results with those of FOA-LSSVM and LSSVM.

In this study, a novel approach is proposed to simultaneously detect clouds and cloud shadows for remotely sensed images. Unlike the existing methods that based on spectral tests, it is based on the simulated band radiance, so that it can be applied to any remotely sensed images. The results showed that it very effective compared to existing algorithms.

Global mapping methods of snow water equivalent (SWE) are developed in this study using WCOM (Water Cycle Observation Mission) active/passive multichannel observations. Based on the payloads of WCOM mission, especially with X/Ku scatterometer and L/Ku/Ka radiometer active/passive observations, there are obvious advantages in snow water equivalent retrieval. The estimation of SWE mainly rely on the high resolution X and Ku band scatterometer, and combined active/passive retrieval can provide more reliable SWE product. The retrieval method of SWE from X/Ku scatterometer is described in this study, and combined active/passive retrieval is also briefly described. These validation of SWE retrieval from X/Ku band scatterometer showed that we can get high accurate and high resolution SWE products from WCOM and then meet the science requirement of WCOM for water cycle studies.

With the rising interest in exploring the possibility of estimating geophysical parameters of interest to oceanographers by global navigation satellite system reflectometry (GNSS-R) of signals scattered from the ocean surface, and the advent of the Soil Moisture Active Passive (SMAP) [1] and the Soil Moisture and Ocean Salinity (SMOS) [2] scientific missions, there is a need to understand the relevant scattering physics at L band so as to provide a physical basis for retrieving geophysical information from ocean-scattered signals.

There has been growing interest in the investigation of vegetation using polarimetric remote sensing techniques. During the past several decades, a number of theoretical models have been proposed to study the scattering mechanisms in the vegetation medium and are very useful for forest stand or short crops [1]-[4].

Frozen ground is soil or rock in which part or all of the pore water has turned into ice. Freeze/thaw state is simply water-ice phase change, but it is an important sign like a giant on-off “switch” of the land surface processes. The freezing of soil greatly reduces the water infiltration and migration in the soil, and in consequence generates a substantial increase in snowmelt runoff. The seasonal cycles of freezing and thawing significantly influence the surface energy exchanges with atmosphere. Therefore, freeze/thaw state monitoring is becoming essential under the context of global changes. The WCOM integrates all the advantages of previous satellites, and is expected to provide more accurate information of freeze/thaw state through the synergy use of active and passive, high and low resolution measurements.

Estimate surface air temperature (Ta) accurately in fine scale is very necessary for hydrological simulation, especial in glacierized basins. The purpose of this paper is to present a framework to mapping the Ta using multi-source remote sensing data and reanalysis dataset. The main content includes two parts: (a) filling the gaps in remotely sensed land surface temperature (LST) using spatial-temporal Kriging method and (b) developing a semi-empirical method to relate Ta and LST that is applicable in glacierized basins. The framework is further tested in the Langtang valley, Nepal which is a glacierized basin in the central Hindu-Kush-Himalaya (HKH) region. The validation results show that the estimated Ta has generally good spatial and temporal variations. The RMSE of Ta at Langtang Kyangjin station is 9.1K and 7.7K at 10:30 and 13:30, respectly.

This letter presents an algorithm for retrieving soil moisture using only H-polarized multiangular brightness temperature at L-band. We developed a parameterized surface model based on a simple-empirical model, the Hpmodel, for this retrieval algorithm. By analyzing a simulated database using the advanced integral equation model (AIEM), it was found that the roughness variable Hhcan be parameterized as a function of an effective roughness parameter Sr = (kL· s)2-N(s/l)N. Influences of three surface roughness parameters (e.g., rms height, correlation length, and type of autocorrelation function) required to describe a rough surface on surface reflectivity were all considered in this parameterized model. Comparison with AIEM simulations over a wide range of soil conditions indicates a good performance of this model. Then, based on the ω - τ model, this algorithm is applied on refined SMOS H-polarized multiangular brightness temperature. Retrieved soil moisture in Africa exhibits reasonable patterns and temporal changes. Validation using in situ soil moisture from Little Washita watershed and Yanco over 2010-2011 showed fine accuracy with root-mean-square errors of 0.031 and 0.045 m3/m3 for two areas, respectively.

This letter investigates the influence of row wheat for azimuthal look angles on radar backscatter at L-, S-, C-, and X-bands. The radar backscatter was collected with full polarization (HH, HV, VH, and VV) and incidence angles (10°-70°) at different wheat phenological stages. Simultaneously, wheat parameters (biomass, canopy height, stem density, leaf inclination, etc.) and soil parameters (moisture and roughness) were measured to explain the influence based on radiative transfer theory. The research results show that, when the contribution of soil scattering dominates in total backscatter, the influence of row wheat on radar backscatter is varied with the electromagnetic wavelength and the visible soil contained in radar footprint area. When volume scattering contributes more in total backscatter, the influence of row wheat on radar backscatter mainly comes from leaf parameters and wheat geometric shape. Moreover, research results also show that azimuthal look angles affect radar backscatter much mainly for variable scattering cross section and leaf parameters. The research is helpful for cereal monitoring, modeling, and cereal parameter inversion from synthetic aperture radar images.

The electromagnetic scattering by a dielectric cylinder of finite length is important in many applications, particularly for microwave remote sensing of vegetated terrain. Yet, not only a unified analytical solution is still elusive in providing the scattering cross sections but also other important aspects, such as the phase of the scattering amplitude function, energy conservation, and reciprocity relation, have been scantly touched in the literature. The treatment of a dielectric inhomogeneous cylinder of finite length brings forth new challenges. Taking on such challenges is the focus of this paper. The main plan of attack is to extend the virtual partition method, which is a T-matrix-based semi-analytical model, that we have previously proposed to treat scattering from a homogeneous dielectric cylinder of finite length, to the inhomogeneous cases. The effectiveness of the proposed method is validated numerically, including: 1) high-fidelity prediction of the copolarized and cross-polarized cross sections for arbitrary bistatic scattering configuration; 2) high-fidelity predictions of the phase of the scattering amplitude function; 3) verification of energy conservation; and 4) verification of the reciprocity theorem.

In recent years, global warming caused by increased atmospheric CO2has greatly drawn widespread attention from the public. Although satellite observations and model-simulation offer us two effective approaches to monitor and assess the global atmospheric CO2, quantification of the differences between these two different CO2data is not fully investigated yet. In this paper, these CO2products including satellite observations and model-simulation are inter-compared in terms of magnitude and their spatiotemporal distributions. The results reveal that these CO2data from different data source show a good agreement all over the world, whereas many discrepancies still exist between satellite observations and model-simulation, especially in the Northern Sphere.

Using time series Aquarius middle beam scatterometer observations, the two vegetation parameters C and D in water cloud model were estimated. Vegetation backscatter was derived using two models: Oh model was used to describe the scattering from bare soil surface, while water cloud model was implemented to account for the effect of vegetation canopy. The vegetation parameters were estimated by minimizing the deviations between the Aquarius scatterometer observations and backscatter coefficients simulated by the water cloud model. The retrieved vegetation parameters are vegetation-specific, which are assumed constant for each vegetation types. By using the retrieved parameters to simulate the scatterometer observations, it was found that the error of the simulation (RMSE) was less than 3 dB in most areas. This research demonstrated that the water cloud model could be applied to global scatterometer observations if the vegetation parameters are appropriately set.

L-band radiometry is a promising pathway for soil moisture estimation at global scale. An observation system simulation experiment was conducted for LEWIS over the SMOSREX bare soil site in 2006 through coupling the Variable Infiltration Capacity(VIC) land surface model and a Multi-Option L-band Microwave Emission Model(MOLMEM) in this study. Impacts from different dielectric constant models and roughness correction schemes on brightness temperature simulation were analyzed.

Passive microwave remote sensing has all-weather work capabilities, but atmospheric media have different influences on satellite microwave brightness temperature under different atmospheric conditions and environments. In order to clarify atmospheric influences on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), atmospheric radiation were simulated based on AMSR-E configuration under clear sky and cloudy conditions, by using radiative transfer model and atmospheric conditions data. Results showed that atmospheric water vapor was the major factor for atmospheric radiation under clear sky condition. Atmospheric transmittances were almost above 0.98 at AMSR-E's low frequencies (<18.7GHz) and the microwave brightness temperature changes caused by atmosphere can be ignored in clear sky condition. Atmospheric transmittances at 36.5GHz and 89GHz were 0.896 and 0.756 respectively. The effects of atmospheric water vapor needed to be corrected when using microwave high-frequency channels to inverse land surface parameters in clear sky condition. But under cloud covered conditions, cloud liquid water was the key factor to cause atmospheric radiation. When sky was covered by typical stratus cloud, atmospheric transmittances at 10.7GHz, 18.7GHz and 36.5GHz were 0.942, 0.828 and 0.605 respectively. Comparing with the clear sky condition, the down-welling atmospheric radiation caused by cloud liquid water increased up to 75.365K at 36.5GHz. It showed that the atmospheric correction under clouds covered condition was the primary work to improve the accuracy of land surface parameters inversion of passive microwave remote sensing. The results also provided the basis for microwave atmospheric correction algorithm development. Finally, the atmospheric sounding data was utilized to calculate the atmospheric transmittance of Hailaer Region, Inner Mongolia province, China, in July 2013. The results indicated that atmospheric transmittances were close to 1 at C-band and X-band. 89GHz was greatly influenced by water vapor and its atmospheric transmittance was not more than 0.7. Atmospheric transmittances in Hailaer Region had a relatively stable value at low frequencies(<18.7GHz) in summer, but had about 0.1 fluctuations with the local water vapor changes at high frequencies.

We develop a new method of multilayer simulations for interpreting accurately the real radar observations of Martian subsurface radar sounder in the insight of electromagnetic (EM) wave radiation and propagation inside the stratified mediums with multilayer rough interfaces. The multilayer radar echo simulator is developed by employing the Huygens theory, Kirchhoff approximation (KA) of rough surface scattering and the ray tracing of geometric optics (GO) theory to calculate accurately the EM scattering from real Martian topography and subsurface rough boundaries. A clear physical meaning of EM wave radiation and propagation in rough layered mediums is provided for achieving more accurate interpretations of real radar data. The presented method can be utilized to reduce effectively the strong Martian surface off-nadir clutters for discerning the weak subsurface nadir echo masked by surface clutters, which can maximize the ability for detecting the Martian deep subsurface information by improving the signal-to-clutter radio (SCR) and signal-to-noise ratio (SNR). Finally, the proposed method is utilized to test and validate the controversial hypotheses of actual Martian radar sounding data and give more accurate geological interpretations.

In this study we simulate the fully polarimetric bistatic scattering behavior resulted from the interaction of electromagnetic wave with all scales of ocean waves, with a focus on the dependence of such behavior on wind direction and incidence angle. The numerical approach is our recently developed second stochastic degree iterative algorithm with sparse matrix and Chebyshev approximation. The illuminated area is 86 wavelength × 86 wavelength, which represents about 2.5 dominant wavelengths for a 3m/s Elfouhaily spectrum at L band, implying that the effect of gravity wave has been adequately included. The total number of surface unknowns is 2,130,048. Numerical results for both angular backscattering behavior and bistatic patterns are provided.

Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results.

In this paper, the effects of multilayer structure of snowpack and its temporal evolution on backscattering are investigated by model simulations. The study is focused on layering structures of dry snow that may represent a risk of avalanches in Alpine regions. The implemented model has been validated using X-band Cosmo SkyMed (CSK ®) acquisitions collected in the winters between 2009 and 2013 on a test area located in the Eastern part of the Italian Alps, and corresponding direct measurements of the main snow parameters. After the validation, the models are applied to simulate the backscattering from snow profiles typical of snow covers characterized by a high risk of avalanches.

WCOM, the Water Cycle Observation Mission, is proposed to improve the capability of synergetic observation of key water cycle variables. By developing innovative active-passive and multi-frequency combined sensors and retrieval models and techniques, the scientific objectives of this mission is to deepen the understanding on global water distribution, transportation and phase conversion by synergistic observations; and based on the improved model and data, to rebuilt long-term data series for revealing of the responses and feedbacks of water cycle to global changes.

Accurate microwave brightness temperature is important for the retrieval of land surface parameter. However, the existence of atmosphere affect acquisition of brightness temperature by microwave sensor onboard satellite. In this paper, atmosphere sensitivity of each band of AMSR-E is analyzed and an atmosphere correction method is developed with ancillary water vapor and cloud liquid water data for both clear and cloudy condition. As a validation, time series of microwave vegetation index is used to qualitatively verify the atmosphere corrected brightness temperature, and it shows that the atmosphere correction method make a good improvement on microwave vegetation index.

Currently, ascertaining and quantifying the global distribution of carbon dioxide from space-based measurements are greatly valuable for understanding the causes of global warming and predicting the tendency of climate change. Nevertheless, the number of valid XCO2data points from a single space-based sensor is generally limited on the earth. Based on this problem, a fused XCO2dataset is used to generate a continuous spatio-temporal distribution of global CO2concentration by combining GOSAT with SCIAMACHY in this study. And this dataset is also compared with a data assimilation system Carbon Tracker as well as ground-based TCCON sites. The results reveal that the spatial coverage of the fused data is wider than individual space-based XCO2measurements (GOSAT or SCIAMCHY) on the global scale. Meanwhile, compared to that of GOSAT or SCIAMACHY, the correlation between the fused data and Carbon Tracker is relatively better. In addition, the fused data show a good agreement with CO2retrieval of ACOS and BESD as well as that of TCCON sites although a little biases exist.

In our study, we proposed a gap-filled method based on ordinary kriging to generate a global land distribution map of carbon dioxide (CO2) by modeling the spatial correlation structures of column-averaged CO2dry air mole fractions (XCO2) on the global scale, using a fused data by combining GOSAT and SCIMACHY. The relationship between the distance and semi-variogram of XCO2is estimated and modeled by an exponential model with a nugget-effect component. The semi-variogram result indicates that there is a significant spatial correlation within the fused CO2data set. The prediction of XCO2using semi-variogram model is conducted within 1 degree×1 degree grids over the world. The results reveal that the global distribution of XCO2based on kriging method is the most extensive compared with other CO2products. Moreover, the monthly map from the kriging approach has less predicted uncertainties, most of which are less than 0.5% of XCO2value.

The Aquarius mission aiming at providing global map of sea surface salinity(SSS) is a collaboration between NASA and Argentina's space agency, Comisión Nacional de Actividades Espaciales (CONAE). The mission has been providing L-band brightness temperature observations since its launch in June, 2011. Simultaneously, significant level of radio frequency interference (RFI) are present in the land observation. A wide range of RFI contaminations are present over China, which are found to coincide with the locations of airports. This may indicate that the RFI sources in China are mainly caused by cities' air-traffic control radars.

Obtaining reliable vegetation optical depth (τ, tau) is essential for vegetation parameter estimation and soil moisture retrieval. In this paper, a lookup table method was developed to retrieve optical depth using MVIs, which can derive the optical depth and single scattering albedo simultaneously without any auxiliary data. The lookup table method is based on the relationship of the single scattering albedo and optical depth at two adjacent frequencies, respectively. The relationships are explored by using a large simulation database generated using a physical model. Then, a lookup table will be established according to the relationships. Following this, we will validate this method and analyze the source of errors.

Validation is important for any satellite-based remote sensing products. In this paper, in situ soil moisture observations from 38 stations over the 1 °×1 ° domain from the central Tibetan Plateau Soil Moisture/Temperature Monitoring Network (CTP-SMTMN) and 152 stations from the Soil Climate Analysis Network (SCAN) over continental U.S. are used to determine the reliability of Aquarius level-2 soil moisture products. Evaluation of the time series in CTP-SMTMN shows good performances of the products to capture surface soil moisture annual cycle with the correlation coefficient of 0.767 and RMSD of 0.078m3m-3. The evaluation results in SCAN suggest that the average correlation is 0.58 and 71.83% sites have correlation larger than 0.5 but differences are observed over many other sites and need to be addressed. The evaluation results also show that the retrieval results performed better for descending orbits (6 AM overpass time).

Land surface emissivity is a critical factor controlling the energy budget on earth surface. However, this important parameter is poorly represented utilizing the “constant-ε” assumption in the state-of-the-art land surface models as well as climate models due to lack of observations. Satellite sensors such as the Advanced Very High Resolution Radiometer(AVHRR) and Moderate-resolution Imaging Spectrometer(MODIS) can provide Narrow Band Emissivity (NBE) products. These NBE products need to be preprocessed to produce reliable Broad Band Emissivity (BBE) which can be then assimilated into land surface models. This paper presents a preliminary sensitivity study of land surface energy balance simulation utilizing the long-term Global Land Surface Satellite (GLASS) BBE product in the arid and semiarid regions of northwestern China. We find that the GLASS-based land surface emissivities in the study region show great spatial and temporal variabilities. Satellite derived emissivity for bare soil ranges from 0.90 to 0.985 and more than half of bare soil grids over our study region have emissivity values less than 0.94. Decreased emissivity would lead to increased surface temperature and sensible heat flux. In-situ simulation results indicate that the ground surface temperature and heat fluxes simulations can be improved when satellite derived emissivity is assimilated.

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies.

Longwave radiation is a key component of total energy that drives surface energy balance at the interface between the surface and atmosphere. To date, a number of algorithms have been developed toward accurately estimating surface longwave radiation from remotely sensed data. While most of these existing algorithms can only derive longwave radiation under clear-sky conditions due to the limited penetration of optical remote sensing thus leading to spatial incontinuity in derived radiation map. Wherein the land surface temperature (LST) play a key role in longwave radiation estimation, especially for surface emitted (upwelling) and net longwave flux. If LSTs under cloudy area can be recovered, the derivation of surface longwave ration under cloudy conditions would be straightforward. To this end, in this paper, a fusing strategy is proposed to combine the LST measurements from MODIS and AMSR-E. The results show that the proposed fusing strategy for combining microwave and optical space-based measurements in recovering surface LST under cloudy conditions is very effective. By fusion, the spatial coverage of valid LSTs over the globe is highly improved.

Shortwave (SW) radiative flux (usually within 0.3∼3μm) is the dominant energy source of our planet, which drives the climate as well as the matter and energy cycle of the Earth system. It is an indispensable component of surface total energy balance. Considering the importance of SW radiation, during the past decades, more and more studies have conducted for estimating surface SW radiation using satellite-based data, such as MODIS, CERES, GOES etc. Although great effort has been made, most researches neglect the topographic effect and mainly focus on the retrieval of SW radiation over ideal horizontal surfaces for both instantaneous and time-integrated radiation. For this point, we propose a topographic SW radiation model based on the existing studies. Based on this, the SW radiative flux components are derived from MODIS data by fully accounting for the surface topographic effect. The results show that the errors induced in the retrieved daily SW radiation can reach up to 400W/m2at 1km scale. For instantaneous radiation, the uncertainties of derived SW radiation can reach up to 300W/m2even at 5km scale due to topographic effect. The findings of this paper prove the importance of topographic modeling of surface radiation over rugged terrain.

We develop a new method of reducing the strong ice surface clutters for imaging of subsurface stratified structure with larger roughness of ice-sheet margins. The surface clutter simulator developed by employing Kirchhoff approximation (KA) of rough surface scattering and the ray tracing of geometric optics (GO) theory can provide the properties of radar echoes from ice layered structure with multilayer rough interfaces. The radar echo simulation method can support the interpretation of radar real observations from ice layered mediums for obtaining good and accurate information of polar ice geological structure and evolutions. In addition, the numerical simulation method can provide an effective tool to identify whether deeper layers or ice-bed nadir echoes masked by strong surface off nadir echo still return to radar receiver or not, which is helpful for radar system design and configuration parameters. The digital filter technique is developed for suppressing the strong surface clutters from ice surface interference targets which can be identified by Synthetic Aperture Radar (SAR) analysis. The proposed ice data processing techniques are demonstrated by ice data processing results of certain outlet glaciers topography with steep slopes.

Precise and reliable land cover data is of fundamental importance in a weather and climate model. Accurate representation of land surface properties directly affects the calculation precision of simulation through energy interactive between land surface and atmosphere. In this study, the impacts of urbanization on precipitation during a heavy rain process are investigated and analyzed in Beijing area of China. Five land use-land cover (LULC) data are compared and used to simulate the precipitation during the rainstorm. The results from the simulations in WRF model suggest that, land cover change has little impact on average temperature of land surface during rainfall event, but differences in the average hourly precipitation over urban area resulted from urbanization are simulated; urban expansion can increase the total precipitation over the area of urban.

Snow water equivalent is an important parameter for natural science studies. One promising sensor configuration for quantitative snow water equivalent remote sensing is the X and Ku band dual-polarization SAR, such as the CoreH2O mission. The retrieval algorithm of terrestrial snow water equivalent from this sensor configuration suffers two major problems which are the decomposition of volume and surface backscattering, and the calibration of snow grain size effect. In previous study, we proposed a preliminary algorithm for snow water equivalent retrieval using X and Ku band dual-polarization radar. In the meantime, in the past years there has been progress in both ground experiment techniques and electromagnetic scattering modeling of snow cover. This enables us to make a further refinement and improvement of the retrieval algorithm based on the new experimental data and newly developed electromagnetic scattering models. In this study, we propose a refined version snow water equivalent inversion algorithm based on X and Ku band dual-polarization radar. The volume backscattering of snow is decomposed using the depolarization ratio, and the single scattering albedo and the optical depth is retrieved by the dual-frequency volume backscattering signal. Then the snow water equivalent is retrieved using the absorption part of the optical depth. The retrieval algorithm is tested using the Nosrex experiment carried out in Finland.

Endmember variability is one of reasons causing errors of model fits in spectral mixture analysis (SMA). The endmembers used in endmember matrix should be higher intra-class similarity and lower inter-class similarity, which depends on spectral wavelengths. The quantification of spectral similarity of intra- and inter-class helps to determine the appropriate endmember classes and optimize the endmember selection. In this study, five spectral matching algorithms were used to quantify the spectral similarity, and three spectral class-matching metrics based on the matching algorithms were used to analyze the spectral similarity of intra- and inter-class. The spectral class-matching metrics of a measured spectral library (ENVI spectral libraries) and MODIS image spectra were quantified and assessed to determine the appropriate the endmember classes for MODIS reflectance channels.

Current and future satellite missions with L-band passive microwave radiometers could provide useful information for monitoring the soil moisture and freeze/thaw state at a global scale. The soil surface roughness plays a significant role in microwave emission from land surfaces. In this study, a simple parameterized model from exponentially correlated surface was developed. Results indicated the model can be very useful in understanding the effects of surface roughness on microwave emission.

A soil moisture retrieval algorithm is proposed that takes advantage of the simultaneous radar and radiometer measurements by the forthcoming NASA Soil Moisture Active Passive (SMAP) mission. The algorithm is designed to downscale SMAP L-band brightness temperature measurements at low resolution ( ~ 40 km) to 9-km brightness temperature by using SMAP's L-band synthetic aperture radar (SAR) backscatter measurements at high resolution (1-3 km) in order to estimate soil moisture at 9-km resolution. The SMAP L-band SAR and radiometer instruments are designed to provide coincident observations at constant incidence angle, but at different spatial resolutions, across a wide swath. The algorithm described here takes advantage of the correlation between temporal fluctuations of brightness temperature and backscatter observed when viewing targets simultaneously at the same angle. Surface characteristics that affect the brightness temperature and backscatter measurements influence the signals at different time scales. This feature is applied in an approach in which fine-scale spatial heterogeneity detected by SAR observations is applied on coarser-scale radiometer measurements to produce an intermediate-resolution disaggregated brightness temperature field. These brightness temperatures are then used with established radiometer-based algorithms to retrieve soil moisture at the intermediate resolution. The capability of the overall algorithm is demonstrated using data acquired by the airborne passive and active L-band system from field campaigns and also by simulated global dataset. Results indicate that the algorithm has the potential to retrieve soil moisture at 9-km resolution, with the accuracy required for SMAP, over regions having vegetation up to 5- kg/m2vegetation water content. The results show a reduction in root mean square error of volumetric soil moisture (40% improvement in the statistics) from the minimum performance defined as the soil moisture retrieved using radiometer measurements re-sampled to the intermediate scale.

In this paper, we apply the right preconditioned generalized minimal residue (GMRES-RP) method for the analysis of electromagnetic scattering from 1-D ocean surfaces. We first verify that the GMRES-RP method has the desirable features of high accuracy, robustness, and efficiency, and, hence, is well suited to such analysis. We then carry out this analysis systematically by examining the effects of wind speed, incidence angle, ocean spectrum, and the degree of inclusion of the gravity and intermediate waves. With a large surface length of 16384 λ and a noise floor pushed down below -70 dB, we have found more refined structures than suggested in the literature: At near-normal incidence angle (θi=20°), both VV and HH results remain sensitive to wind speed. Across the incidence angle range, the sensitivity is higher at small wind speed than at medium to high wind speed. Plant argued that the contribution from intermediate waves separately, not as a part of the large wave spectrum, becomes increasingly important with increasing wind speed and, accordingly, a multiscale model is desired. Our numerical analysis validates this viewpoint by showing that a normalized bistatic scattering coefficient value as large as 4 dB at certain angles for H-pol at L band can be attributed to the intermediate wave and the tilt modulation provided by large waves. The viewpoint is further validated by a comparison of the two-scale model and three-scale model against numerical results where the two-scale model shows appreciable discrepancy in the forward scattering angles whereas the three scale model shows very good agreement with MoM, hence manifesting the necessity of advancing multiscale models beyond the conventional two-scale model. This finding also implies the need for numerical studies to use adequately large surface length, at least to cover the intermediate waves, even for below intermediate incidence cases.

In this paper, a multilayer version of the Dense Medium Radiative Transfer (DMRT) model has been implemented for the active remote sensing. The effects of multilayer structure of snowpack and its temporal evolution on backscattering have been investigated. The study has been focused on the effect of layering structure of snowpack typical of the Alpine regions. The ground measurements used as model inputs have been collected on the Italian Alps during the 2009–2010 winter season.

An approach for retrieving surface soil moisture using microwave vegetation indices (MVIs) based on SMOS data is presented in this paper. The vegetation optical depth is analytically derived from simplified multi-angular MVIs and the soil dielectric constant to correct the vegetation effect. By minimizing the difference between modeled and observed brightness temperature at six different fixed angles, the only unknown soil moisture can be retrieved. Validations using ground measured soil moisture from Yanco study area in Australia indicated that this new approach improve the accuracy of SMOS L2 soil moisture products, with the RMSE increased from 0.072(m3/m3) to 0.053(m3/m3) and the correlation coefficients increased from 0.690 to 0.696.

In this study, a downscaling algorithm to disaggregate the radiometer Brightness Temperature (TB) using the radar backscatter observations for SMAP (Soil Moisture Active and Passive) was developed. The algorithm is based on the spectral downscaling which combines both phase and amplitude information in Fourier domain. Using the information from radar measurements at finer resolution, a new way to estimate the Fourier phase was proposed. The algorithm has been successfully applied to the PALS datasets from SMEX02 producing better results than radiometer-only inversions. The RMSE (Root-Mean-Square-Error) of the downscaling Brightness Temperature are 3.26K and 6.12K for V and H polarization, respectively. Then medium resolution soil moisture was retrieved from disaggregated/downscaled TB. The accuracy (RMSE) of the downscaling soil moisture retrievals is 0.0459m3/m3, which is very close to SMAP science requirement of 0.04. The results indicate that the downscaling algorithm presented in this study is a promising approach to achieve finer resolution and more accurate soil moisture retrievals for the future SMAP mission.

The paper proposed a soil moisture retrieval model with S-band field experimental data and volumetric water content measured in field. Focused on the problem that antenna irradiated region may not meet the minimum radar resolution pixel, the research applied the multiple independent samples measuring method and analyzed the relevance between soil moisture and backscattering coefficient of S-band VV polarization. The inversion equation obtained was applied to inverse soil moisture from SAR (Synthetic Aperture Radar) S-band images of HJ-1C (a satellite designed for environment and disaster monitoring). The inversion results were verified by the multiple independent samples data measured and agreed well with the experimental data, which shows the S-band VV polarization data can be used to monitor the soil moisture in a large scale.

The vegetation optical depth (τ, tau), which describes microwave attenuation properties of vegetation, is a key parameter for vegetation biomass and soil moisture estimation. In this paper, we have implemented five semi-empirical/empirical methods for vegetation optical depth estimation. The Advanced Microwave Scanning Radiometer- Earth Observing System (AMSR-E) data and field experimental data provided by Climate Change Initiative (CCI) project were used to evaluate the estimated vegetation optical depth. Its dynamic ranges and time series trends were analyzed at one site located in USA. Correlations between optical depth estimated using the five methods and MVIs_B, MPDT, MDPI, and NDVI were calculated in order to explore the relationship between vegetation indices and optical depths. With exception of the Radiative Model (RT) method, the vegetation optical depths from other four methods exhibit a similar trend with time. The dynamic ranges and the correlation coefficients are significantly different from each other. This study would further help us to study the uncertainty of a variety of current soil moisture products.

The paper studied the model relation of soil moisture and spectral index by using the data field measured spectral reflectance and soil moisture, and analyzed the correlation between the soil moisture and spectral index. Furthermore, based on analyzing the data measured, the research selected the sensitive band and built the optimal inversion model. The soil moisture of the studied area (Maoergai area of Minjiang upriver) was inversed, and then the inversed results were analyzed and evaluated. By analyzing the different inversed results accuracy, the research obtained the optimal method of soil moisture inversion for ecological water information index remote sensing quantitative model.

This paper describes a new algorithm for the retrieval of bare surface soil moisture using dual-polarization time-series radar data. The roughness index is used to describe the soil surface roughness condition. The new algorithm assumes the roughness condition is constant over a shot time, so that the roughness index retrieval accuracy can be improved by using temporal data to minimizing the effect of radar speckle noise. The uncertainty of the roughness index is predicted by using an error propagation theory. By applying the retrieved roughness index and corresponding uncertainty as a constraint, a Bayesian approach, which takes account the uncertainties of radar observation, is implemented. The algorithm is validated with a field ground dataset at 1.25 Ghz and 40° incidence angle. The result shows an rms error (RMSE) of 0.06 cm3/cm3for soil moisture. The correlation coefficient between retrieved soil moisture and in situ data is 0.81. Surface rms height estimates are found with RMSE of 0.41 cm and correlation coefficient of 0.99. It is shown that the new algorithm using time-series data outperforms the Bayesian approach without using temporal information and snapshot method.

Wetlands' soil hydrology and temperature changes greatly affect methane emissions, and further affect climate. Wetland processes are not modeled well yet in the community land model (CLM) and further study is needed. Wetlands are represented as saturated organic soil, instead of being treated as water bodies only without soil and vegetation in CLM. Wetland soils are discretized to fibric, hemic, and sapric layers accounting for the typical variations in hydraulic characteristics of organic soils in the newly parameterized model. Spin-up is achieved by repeating the full range of available years 3 times (3 spin-up cycles). Daily model output is averaged to monthly output for analysis. The original model (CLM3_cntrl) and the modified wetland scheme model (CLM3_wet) are run independently. Sensible and latent heat fluxes and soil temperatures are validated. The results show that the state variables are improved through the wetland soils parameterization compared to the original CLM model.

Satellite based High Resolution Precipitation Product (HRPP) (generally with a temporal resolution of 3h and spatial resolution of 0.25°×0.25°) is a valuable data source in global and regional hydroclimatologic applications. However uncertainty information for most of the satellite-based HRPPs is unavailable up to present. This paper presents a statistical evaluation study of daily TMPA research time precipitation products at an arid-semiarid basin with complex terrain properties in the northwest of China. Both local scale and regional scale evaluation studies are conducted utilizing gauge based datasets. Total 15 evaluation indices are selected for this study including 5 traditional quantitative statistical indices (CC, RMSE, ME, MAE and BIAS) and 5 categorical statistical indices (POD, FAR, PFB, CSI, and ETS) as well as 5 indices for bias/error decomposition evaluation(HB, MB, FB, RMSEs and RMSEr). New findings about the uncertainty characteristics of TMPA research time precipitation products can be concluded into the following three aspects: (1) both local and regional evaluation results demonstrate that the performance of daily TMPA products is climatology-dependent. For the middle-upper reaches of Heihe river basin, better performance is observed at the southern mountainous area where the climatology is wetter even with consideration of significant terrain effect which is caused by complex topographical variation there. Different bias/error characteristics would appear in different climatology. For the study basin, TMPA products overestimate precipitation in wetter region while make underestimation estimation in drier area. This implies nonuniform bias correction schemes for TMPA products may lead to accuracy improvement. (2) Performance of TMPA products is much worse in snowfall estimation than rainfall estimation and thus one should be cautious to use TMPA daily products in cold region or winter time. (3) little improvement in the performance of TMPA products on daily scale has been made by transition from version 6 to version 7. This study can serve as a reference of the uncertainty in TMPA research time precipitation products in hydrometeorogical applications on watershed scale.

Microwave Vegetation Indices(MVIs) are newly developed vegetation indices using microwave remote sensing data (SMOS, WindSat, and AMSR-E, respectively). SMOS operates at L-band, in dual polarization and a range of viewing angles. WindSat is a satellite-based multi-frequency and multi-angle polarimetric microwave radiometer with the same transit time as SMOS. Establishing a synergetic vegetation index with WindSat and SMOS data will provide multi-angle and multi-frequency information in vegetation monitoring. As the deducations of previous indices are based on single senor configuration, it is necessary to demonstrate the applicability of the synergy for these two sensors. From the previous research, we found that bare soil emissivities at adjacent frequencies or angles exhibited a good linear relationship under Gaussian correlation function. Whether this relationship exists for L-band and C-band and for Exponential correlation function has to be deeply explored. With this objective, we have built a simulation database using the Advanced Integral Equation Model (AIEM) at L-band (SMOS, 1.4GHz, multi-angle) and C-band ( WindSat, 6.8GHz, 53.5°) in dual-polarization (v and h), respectively. The relationships of the surface emissivities are significantly different, while the fresnel reflectivity exhibits linear relationship. Hence, we consider using fresnel reflectivity to establish the bridge between different sensors. The radiation of vegetated land surface can be described by a 0th-order radiative transfer model. The total emission from ground can be rearranged into a linear model with the vegetation emission component as the intercept and the vegetation transmission component as the slope. With this linear relationship, the Fresnel reflectivity at one frequency can be expressed as a linear function of that at another frequency. And the intercept and the slope are the newly derived vegetation indices. They are mainly affected by vegetation properties and have a consistent spatial pattern with NDVI.

Remote sensing of atmospheric CO2is essential to study global warming. To date, there are many instruments to detect CO2from space, such as, AIRS, GOSAT, SCIAMACHY and IASI etc., while quantification of the differences among these CO2products has not been fully investigated yet. In this study, the differences between CO2products from AIRS, GOSAT, SCIAMAMCHY (totally four products) have been compared. The results showed that although these CO2products are derived from different instruments, the complementarity in spatial coverage and relatively high correlation among them make it potentially possible to combine them, especially for GOSAT and SCIAMACHY.

Space-based monitoring of atmospheric CO2is very crucial for global carbon cycle studies and even global change. In this study, a method for physically fusing SCIAMACHY and GOSAT CO2measurements has been proposed by fully considering the averaging kernel and spatio-temporal variations as well as the CO2retrieval errors. The results revealed that the average global coverage of ACOS and BESD is around about 0.56% and 0.27% respectively at a daily scale. The monthly-mean coverage of such products accounts about 5.66% and 4.62% respectively. While spatial coverage of fused XCO2can reach up to 0.76% and 8.51 % on daily and monthly scale respectively. These findings in this paper proved the effectiveness of the proposed method.

Dry snow is a two-phase random medium with air and ice, which can be modeled as either sphere particles or random medium. The newly developed bicontinuous scattering model greatly enhanced the ability to model the snow scattering characteristics based on microstructure with most similarity to real snowpack. In this study, the geometric equivalent parameters of bicontinuous scattering model are derived and validated by using stereological method. By combining with dense media radiative transfer equations, the snow covered ground backscattering coefficient and brightness temperature are simulated. The bicontinuous-DMRT model is compared with sphere based model - QCA-DMRT model. The snow scattering characteristics predicted by the two models are studied. The models are validated with scatterometer and radiometer measurements.

Snow cover information is crucial to global climate change research and hydrological applications. Snow cover over the Tibetan Plateau is important to water resources and Asian climate. Based on high temporal resolution of geostationary satellite data, snow cover map with less cloud obscuration can be obtained daily. In this paper, geostationary meteorological satellite FY2E VISSR data is used to obtain the snow cover information over the Tibetan Plateau in year 2010 and 2011 winter seasons. Meteorological station observations are used to evaluate the performance of snow cover maps. In addition, MODIS and IMS snow cover products are used for comparison and validation. Results indicate VISSR snow cover maps show good performance in reducing cloud obscuration. MODIS snow cover maps present highest overall accuracy, followed by VISSR and IMS. VISSR and IMS snow cover maps show slight over-estimation of snow cover over the Tibetan Plateau.

The Soil Moisture Ocean Salinity (SMOS) mission has been providing L-band multi-angular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio frequency interference issues and aliasing at lower look angles increases the uncertainty of observations and thereby affects the soil moisture retrieval that utilizes observations at specific angles. In this study, we propose a processing chain that uses a mixed objective function based on SMOS L1c data products to refine the characteristics of multi-angular observations. The approach was validated using simulations from a radiative transfer model and analyzed over three external targets: Amazon rainforest, Sahara desert, and Antarctic ice. These results could provide insights for selecting and utilizing external targets as part of the upcoming Soil Moisture Active Passive (SMAP) mission.

Knowledge of the vegetation phenological dynamics is crucial to the understanding of the Earth ecosystems and carbon cycles. A dual-frequency dual-polarization microwave vegetation index (MVI) has been developed recently for the Advanced Microwave Scanning Radiometer for the Earth Observing System; however, the noisy behavior of MVI limits its applications in the studies on terrestrial vegetation. In this letter, a method based on Fourier analysis is proposed to refine MVI. By excluding the high-frequency variations, the method helps to recover the trend of vegetation seasonal changes inherently contained in the raw MVI data series. Comparisons between the refined MVI and the normalized difference vegetation index (NDVI) data sets from years 2002 to 2005 show that the correlation between the refined MVI and NDVI is significantly increased for the three study sites. The refined MVI along with the optical vegetation indices provides complementary information on vegetation and can be served as a useful tool to monitor regional and global vegetation dynamics.

In this letter, land surface temperature (LST) is derived from Geostationary Operational Environmental Satellite (GOES)-East (GOES-E) and GOES-West (GOES-W) using a revised dual-window LST algorithm developed by Sun and Pinker in 2004. LST derived from GOES is also evaluated against ground observations. The results show that the LSTs from GOES-E are warmer than those from GOES-W in the morning but lower in the afternoon, while there is no big difference around noon and during night. It is found that the original brightness temperatures from GOES-E and GOES-W show similar patterns to LSTs over time. The discrepancy in LSTs is most probably due to the fact that the Earth surface is warmer in GOES-E than in GOES-W in the morning but cooler in the afternoon. Some other factors may include the difference in satellite viewing geometry, image navigation and registration, calibration, and spectral response functions. It is expected that these effects should be small as those demonstrated in nighttime difference. When evaluated against the ground observations, over the overlap region, the LST bias error is positive from GOES-E but negative from GOES-W in the morning, leading to a positive LST difference, while the bias from GOES-W is close to zero but negative from GOES-E in the afternoon, resulting in a negative LST difference. Nevertheless, the LST root-mean-square errors from GOES-E and GOES-W are very close and reach the maximum around noontime. To synergistically use LST from different satellite sensors, we suggest using nighttime data; further study is needed to best use daytime LSTs.

In the above paper (ibid., vol. 49, no. 4, pp. 1177-1189, Apr. 2011), there is an error in equation (7). The explanation and corrected equation are presented here.

Monitoring of snow cover is crucial in water resource management and hydrological risk prevention. Experiments have shown the ability of C-band SAR in mapping the extent of wet snow. But, detection of dry snow at this frequency is difficult due to the high transmissivity of the snowpack. A model sensitivity study, corroborated by experimental data, has demonstrated that COSMO-Skymed X-band data can give significant information for generating maps of SWE for snow depth higher than about 50-60 cm.

This study demonstrates a technique of estimating soil moisture using the L-band dual-polarization measurements and no soil roughness information. The development is based on the analysis of the simulated database using Advanced Integral Equation Model (AIEM) under SMAP sensor configurations. Through analyzing the current surface emission models the Hp model was selected as the basic model for development. The inversion technique is validated with both simulated data and ground experimental data. The inversion accuracy with RMSE (root-mean-square error) for simulated data and ground experimental data is 1.4% and 3.6%, respectively, which can meet the requirement of SMAP (soil moisture active and passive).

As an important vegetation indicator, vegetation indices have become a widely used tool in vegetation parameters retrieval and condition monitoring. A newly developed MVI was deduced and evaluated using WindSat data. During the deduction, the w-τ model was utilized as the theory foundation. The emission from ground can be rearranged into a two component model including the vegetation emission component and the vegetation transmission component. In order to characterize the frequency dependence of surface emission signals on the objective of minimizing the effects of the ground surface emission signals, we have built a simulation database for the configurations of WindSat using the Advanced Integral Equation Model (AIEM) at 6.8, 10.7, and 18.7 GHz, dual-polarization (v and h) and the corresponding incidence angles. Unlike previous MVIs, this simulation contains both Gaussian and Exponential correlation surfaces. On the basis of simulation data analysis, we found that bare soil emissivity at two adjacent WindSat frequencies has a linear relationship, which makes it possible to minimize the surface emission signal and maximize the vegetation signal. As a result, brightness temperature at a higher frequency can be a function of the adjacent lower frequency at the same polarization, whose slope and intercept are the newly developed Microwave Vegetation Index (MVI) from WindSat data. The new MVI shared the same vegetation distribution pattern as AMSR-E based MVIs and was also negative to NDVI.

In this paper, a hybrid algorithm of retrieving soil moisture is developed. This method assembles the single channel algorithm (SCA) and the Qp-model based soil moisture inversion algorithm. The new algorithm does not require soil surface roughness parameter. The accuracy evaluation of algorithm shows that the new method can provide accurate soil moisture estimate in Tibetan Plateau (TP). Based on eight years (2003~2010) soil moisture data, multi-year changing trend of soil moisture of the TP is analyzed. The spatial pattern of soil moisture changing trend basically coincides with the trend of precipitation of the meteorological sites.

Based on linear relationship between L band brightness temperature and L band backscattering coefficient, a time-series method is proposed to disaggregate brightness temperature at coarse resolution. The algorithm was validated with both in situ PALS dataset from SMEX02 and simulated dataset based on PALS observation. It was found that accuracy of brightness temperature for V polarization (RMSE equal 5.2K) is better than for H polarization (RMSE equal 10.03K). The results reveal a potential possibility that the disaggregated brightness temperature could be used for soil moisture retrieval.

Land surface emissivity can be used for several purposes including land surface characterization and atmospheric retrieval over land. It is quite challengeable to simulate passive microwave radiances over land. This paper focuses on land surface emissivity retrieval and radiance simulation under snow-free conditions on a global scale for AMSR-E sensor configurations. A surface emission model (Qp) is coupled within Community Radiative Transfer Model (CRTM) which takes volumetric scattering of dense medium into consideration. The Qp model has been proved that it has higher accuracy and more suitable for the high-frequency and high-incidence AMSR-E data analysis. The results show that estimated radiances are comparable to passive microwave observations from satellite for different land surface vegetation types. The Root Mean Square Errors (RMSEs) are less than 20K and the mean errors are generally less than 10K.

Land Surface temperature (LST) is a critical parameter in water and energy circle supporting the global climate research. The traditional optical/thermal remote sensing estimates LST under clear-sky conditions, while the microwave remote sensing provides the all-weather LST estimation capability. In this paper, we derivate a new LST retrieval algorithm based on the emissivity parameterization using the Advanced Microwave Scanning Radiometer - Earth Observation (AMSR-E), considering the atmospheric influence, to tackle the LST retrieval under the non-scattering rain and cloud condition. The new derived LST algorithm has a physical basis and the RMSE is 1.8K with the station validation over boreal forest area at Sodänkylä, Finland.

Monitoring global vegetation can be of importance in understanding land surface processes and their interactions with the atmosphere, biogeochemical cycle, and primary productivity. Previous research has shown that vegetation indices have become essential tools in this field. In this study, we will explore and demonstrate a new technique for deriving Microwave Vegetation Indices (MVIs) using the passive microwave radiometer SMOS data. It provides the global microwave brightness temperature observations at L-band (1.4 GHz) with dual polarizations (V, H) and a range of viewing angles [2]. The SMOS MVIs A parameter was negatively related to NDVI while the B parameter is positively related to NDVI. Compared with WindSat MVIs, SMOS MVIs have similar global distribution patterns but can indicate different vegetation information mainly because of penetrability and incidence angle variation. Based on analysis using BARC 1980 data, SMOS MVI_B is linearly related to LAI.

A bistatic scatter model developed based on the Michigan grassland coherent model was used to simulate bistatic scattering coefficients from wheat field at L and C band. And the wheat field emissivities at V and H polarization were also obtained by integrating the bistatic coefficients over all scattering angles above ground. An experiment was carried out over a flat agriculture area located at Baoding, in the Hebei province of China. The plant and soil parameters were collected over a growing season, and the emissivities were also obtained using the ground-based microwave radiometer at the C-, X-, Ku-, and Ka- bands, in H and V polarizations. The radiometer model values are in good agreement with the measurements at some full growth stages.

Surface soil moisture is a key variable in many hydrological, climatological and ecological processes. Different types of remote sensing systems are currently used to infer soil moisture at different spatial and temporal scales, each with its specific characteristics and limitations. Using AMSR-E soil moisture and MODIS surface temperature (Ts) product, the authors discuss the relationship between the variation rate of land surface temperature and surface soil moisture. Selecting the plains region of Central United States as the study area, the authors propose the distribution triangle of the variation rate of land surface temperature and soil moisture, which is learned from the Ts-NDVI feature space theory. The range of soil moisture is lessening as the increase of instantaneous variation rate of land surface temperature, and the soil moisture value is going down as well. In this paper, Temperature Variation and Vegetation Index (TVVI), a new index containing the information of temperature variation and vegetation, is introduced. The authors also prove that TVVI and soil moisture show a steady relationship of exponential function, and build a quantitative model of soil moisture(SM) and instantaneous surface temperature variation(VTs).

Quantifications of the differences among currently available CO2products are very necessary for deeply understanding each product and their joint use. A spatio-temporal matching strategy has been proposed in this work to allow the CO2products from AIRS, IASI, GOSAT and SCIAMACHY to be physically comparable by accounting for the a priori CO2profiles employed in retrieval stage, averaging kernel functions and atmospheric pressure profiles etc. Based on this, these CO2products are intercompared in terms of magnitudes of CO2concentrations and their spatio-temporal distributions. The results show that relative large discrepancies are detected among these products, both in specific values of CO2concentrations and the spatio-temporal distributions, implying more efforts should be made to fully understand the differences of such measurements and to better constrain the uncertainties in CO2retrievals from space in the future.

The development of bi-continuous scattering model greatly enhanced the ability to model the snow scattering characteristics based on microstructure with most similarity to real snowpack. In this study, snow section images of snow microstructure were used to study the scattering characteristics of snow by using the reconstructed snow 3D microstructure. The equivalent grain size of the continuous random structure is derived by using stereological method. By combining with dense media radiative transfer equations, the snow backscattering is simulated. The polarimetric and frequency characteristics were studied.

Snow cover is an important variable for global climate change research and hydrological application. Recent years, the snowfall events in southern China indicate the limitation of using optical or passive microwave remote sensing respectively. In this paper, China's first generation of geostationary meteorological satellite Fengyun-2E and the second generation of polar-orbit meteorological satellite Fengyun-3B are used to monitor snow cover in China from Jan 1 to 31, 2011. In order to monitor snow cover in real time and make use of China's meteorological satellites, FY-2E VISSR and FY-3B MWRI are mainly used. AMSR-E is also used, since MWRI can't completely cover China daily. The main purpose of this study is to propose an effective method of monitoring snow cover daily mainly using China's meteorological satellites.

It is very crucial for developing satellite land data assimilation system by directly assimilating the gridded satellite brightness temperature (TB) data to simulate gridded satellite observation data. A dual-phase satellite data simulation system framework is developed, which consists of the Community Land Model (CLM), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution algorithm (SCE-UA) and the gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data, and it is implemented in two phases: the parameter optimization and calibration phase and the satellite data simulation phase. The SCE-UA algorithm is used to optimize the LandEM parameters and calibrate microwave wetland surface emissivity by minimizing the difference between the simulated and observed BT. In this paper, the monthly mean microwave wetland surface emissivity calibrated at HeFei in 2003 are transferred to East Asia region, the dual-phase satellite data simulation system is mainly evaluated over China region. Experimental results indicated that the vertically polarized TBs (6.925 GHz and 10.65 GHz) simulated by the dual-phase satellite data simulation system are basically matched with those observed by AMSR-E sensor and the differences between the simulated and observed TBs are less than 15 K, which indicates that the calibrated microwave wetland surface emissivity possesses excellent transportability and the dual-phase satellite data simulation system is feasible and practical over China. This study provides reference for developing China satellite land data assimilation system by directly assimilating the gridded AMSR-E TB data (low-frequency and vertical polarization) for model grids contained various land cover types, especially for those model grids including wetland cover type, which will greatly improve land data assimilation study.

Vegetation indices are indicators for analyzing the properties of vegetation. The Normalized Difference Vegetation Index (NDVI) from optical remote sensing data is one of the most commonly used vegetation indices, which can exhibit the ecological characteristics of leafy materials, but lacks the ability to directly provide information on the woody materials. In this paper, we developed Microwave Vegetation Indices (MVIs) from the L-band Soil Moisture and Ocean Salinity (SMOS) data, which is an effective means to detect the information of branches and trunks. The theory of MVIs is derived from the tau-omega model. To minimize the influence from the uncertain soil surface radiation, a parameterized database was built by the Advanced Integral Equation Model (AIEM). We selected two incidence angles (40° and 50°) and combined them to compute the MVIs. A simple method to evaluate the L-band optical thickness efficiently based on the MVI b-parameter (MVI-B). The MVI optical thickness(MVI-τ) was compared with the optical thickness retrieved from SMOS(SMOS-τ), and the results showed that the overall pattern was very similar.

Knowledge of the spatial distribution of global soil moisture for a long period of time is crucial to the understanding of climate changes and land surface processes. The observations from spaceborne passive microwave sensors for more than 30 years are very valuable for generating historical soil moisture maps. Except those from the Soil Moisture and Ocean Salinity (SMOS) mission, which was launched in November 2009, most of the observations available are from sensors with frequencies much higher than L-band and are more sensitive to vegetation conditions. To rebuild long-term soil moisture data sets with an improved accuracy, a method to correct vegetation effects at X-band or higher frequencies using L-band soil moisture retrievals is explored in this study. Validations based on$b$-parameter values derived from both field-sampled soil moisture with noises added and the actual SMOS products indicate that the method can be applied to the observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System or similar sensors and help to rebuild historical soil moisture data sets with a root-mean-square error less than 0.04$\hbox{m}^{3}/\hbox{m}^{3}$.

SMOS observations provide an opportunity to develop a testbed for the evaluation of different SMAP algorithm options. The use of real-world global observations will help in the development and selection of different land surface parameters and ancillary observations needed for the soil moisture algorithms. In this study, SMOS observations were used with one soil moisture retrieval algorithm and the results were evaluated using in situ soil moisture measurements. The SMOS soil moisture product, which exploits multiple incidence angle observations, compares well with the ground-based observations (RMSE 0.043 m3/m3(ascending) and 0.047 m3/m3(descending)). The alternative SMAP compatible algorithm also performed well (RMSE 0.040 m3/m3(ascending) and 0.043 m3/m3(descending)). Although preliminary, these initial results are encouraging for the potential of SMAP to meet its required soil moisture accuracy.

Water vapor under cloud cover area was retrieved with the combination of AMSR-E Brightness temperature and MODIS atmospheric profile. In order to retrieve water vapor, surface emissivity in clear sky was first estimated using AMSR-E brightness temperature, MODIS atmospheric profiles product and 1-Dimension Microwave Radiative Transfer Model (1DMRTM). And then, surface emissivity under cloud cover area was estimated using 7 days average of that in clear sky. Finally, water vapor was retrieved using the estimated emissivity, AMSR-E brightness and look up table built by 1DMRTM. The finally retrieved water vapor was verified using SuomiNet GPS water vapor. The correlation coefficient of the two is 0.72, and the RMSE is 10.95mm.

A new method of retrieving soil moisture and surface roughness parameter concurrently is introduced in this paper. The improved approach is based on the innovated land parameter retrieval model (LPRM) and uses a nonlinear iterative procedure to retrieve the soil moisture and surface roughness. We incorporate the Qp model into the original LPRM, which makes up the weakness in the assumption of model. Through comparing the soil moisture retrieved with the in situ data, we evaluate the performance of the new algorithm and the root-mean-square error (rmse) is less than 0.1m3/m3.

Monitoring of snow cover is crucial to the study of global climate changes, for water resource management, as well flood and avalanche risk prevention. The sensitivity of X band backscattering of Cosmo-Skymed mission has been first exploited by using model simulation and experimental data. An algorithm for retrieving snow depth or snow water equivalent has been then developed and test with experimental data.

Over the western China, due to the influence from shallow snow, the passive microwave remote sensing algorithm show its imbecility when using the gradient brightness temperature (Tb) algorithm of 36.5Ghz-18.7Ghz. In this paper, we employee several ground time-series snow depth dataset and the corresponding satellite brightness temperature to evaluate the 36.5Ghz/l 8.7Ghz and high frequencies' (85Ghz or 89.0Ghz/18.7Ghz) ability for the shallow snow retrieval. From the analysis, we can get that the high frequencies has its potential for the shallow, which is suit for the snow situation over the high land, even with the atmosphere influence at high frequency. The result tell that the relative deep snow (>; 20cm) the Tbs at 36-18GHz are more reliable than that of high frequency, while over the shallow snow especially <;20cm), the pair 36-18 is insensitive, but the high frequency pair (89/85-18GHz) shows its obvious possibility.

The one of the greatest contributions of Professor Adrian K. Fung to the microwave remote sensing is the development of the integral equation model that has significantly improved and advanced our ability in modeling microwave surface emission and scattering signals. The surface emission and scattering model is one of the essential components in many applications of the microwave remote sensing of geophysical properties in the complex earth terrain. It is a direct component in monitoring soil moisture in the bare surfaces. The surface emission and scattering model serves as the boundary condition in studying snow, vegetation, and atmospheric properties. It has been found that the underground surface emission and scattering signals have a great impact on snow water equivalence and vegetation properties retrieval. How the multi-frequency and polarization measurements to be used correctly in deriving the surface geophysical and atmospheric properties is an important research issue in microwave remote sensing. It is heavily depending upon our understanding on the ability of modeling surface emission and scattering signals. In honor of Professor Adrian K. Fung, this paper demonstrates how the integral equation model has been used in the developing the retrieval algorithms for several geophysical parameters, including soil moisture and vegetation properties.

The study developed a dual-phase satellite data simulation system to simulate the gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data and calibrate the microwave wetland surface emissivity, which based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave land emissivity model (LandEM), Shuffled Complex Evolution algorithm (SCE-UA) and gridded AMSR-E BT data. The system was implemented in two phases: the parameters calibration phase and the AMSR-E BT simulation phase. It used the outputs of the CLM2.0 as the inputs of the LandEM, the LandEM parameters and the wetland emissivity were calibrated by the SCE-UA algorithm in the parameters calibration phase, and the calibrated parameters were used as the final model parameters of the LandEM in the AMSR-E BT simulation phase. The experimental results indicate that the SCE-UA algorithm can effectively calibrate the LandEM parameters and microwave wetland surface emissivity, and they possess excellent transportability. It provides a promising solution to obtain the microwave wetland surface emissivity through parameters calibration method, thus we can simulate the gridded AMSR-E BT data for various land cover types, such as bare soil, vegetation, snow, lake, and wetland, which will greatly improve land data assimilation study. In addition, we attempted to use the parameterized bare surface and snow emissivity model to substitute that of the LandEM, the simulation results can make great improvement.

This paper employs a method based on a parameterized first-order radiative transfer (RT) model at L-band to evaluate the aboveground biomass of forest area using Soil Moisture and Ocean Salinity (SMOS) data. A comprehensive database, including forest structure information based on L-system and the corresponding scattering properties, was established. Then the parameterized first-order RT retrieval model was used to acquire the forest parameters. The Look-Up Table method was used to find the proper biomass value. We retrieved the single scattering albedo and the optical thickness and then found the biomass value which approximated to the reference dataset.

Vegetation water content (VWC) is a useful parameter in agriculture, forestry and hydrology studies. It is particularly valuable in accounting for vegetation effects in retrieving soil moisture using microwave radiometers. Microwave vegetation indices (MVIs) reflect information of the whole vegetation canopy. They may provide a mean for estimating VWC. In this study, a methodology for retrieving VWC using MVIs is presented. Coefficients of the relationship were found to be dependent only on a vegetation structure parameter. The methodology was tested with brightness temperature observations at C and X bands collected over a growing season of cotton. It was found that results compared well with field observations of VWC measured during the early growing season. The methodology should be useful for vegetation monitoring and soil moisture retrieval over low vegetated areas.

In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.

In the forward model [L-band microwave emission of the biosphere (L-MEB)] used in the Soil Moisture and Ocean Salinity level-2 retrieval algorithm, modeling of the roughness effects is based on a simple semiempirical approach using three main “roughness” model parameters:$H_{R}$,$Q_{R}$, and$N_{R}$. In many studies, the two parameters$Q_{R}$and$N_{R}$are set to zero. However, recent results in the literature showed that this is too approximate to accurately simulate the microwave emission of the rough soil surfaces at L-band. To investigate this, a reanalysis of the PORTOS-93 data set was carried out in this paper, considering a large range of roughness conditions. First, the results confirmed that$Q_{R}$could be set to zero. Second, a refinement of the L-MEB soil model, considering values of$N_{R}$for both polarizations (namely,$N_{\rm RV}$and$N_{\rm RH}$), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters$H_{R}$and$(N_{\rm RH} - N_{\rm RV})$to the standard deviation of the surface height were developed. This new calibration of L-MEB provided a good accuracy (better than 5 K) over a large range of soil roughness and moisture conditions of the PORTOS-93 data set. Conversely, the calibrations of the roughness effects based on the Choudhury approach, which is still widely used, provided unrealistic values of surface emissivities for medium or large roughness conditions.

Vegetation is the most important part of the terrestrial ecosystems which results in a large proportion of studies on vegetation parameters, such as coverage, biomass, water content and so on. Since the ultimate goal of remote sensing is to accurately and efficiently inverse land surface parameters, it is of great significance to find a good forward vegetation model with simple form and high accuracy for the inversion. Though the zeroth-order model is good for fast inversion with its simple form, it always underestimates the total emission at high frequency or for dense vegetation. The first-order model has higher accuracy due to the consideration of volume scattering contribution, but it is complex and computationally intensive. In this regard, we developed a parameterized model base on emissivity simulations from the first-order model for short vegetation covered ground in this paper. This parameterized model takes a similar form as that of the zeroth-order model. It is of great significance for accurate and efficient inversion.

Active microwave sensors, especially high-frequency radar systems, are highly sensitive to snow pack parameters, including Snow Water Equivalent (SWE). With the availability of several X-band space-borne SAR systems, the study attempts to make use of multiple-angle SAR observations and develop relevant SWE inversion algorithms. Analysis was carried out based on parameterized scattering models for both soil surface and snowpack. It is found that the backscattering signals at two incident angles are well correlated for both soil surface and snowpack; and snow optical thickness can be well defined and estimated through snow volume scattering at two different angles. The snow and soil parameters can be estimated through two pairs of adjacent observations. The technique was tested using theoretical simulated database. Initial analysis shows that current technique needs to be further improved and a better estimation of single scattering albedo is needed.

The resolution of current MODIS aerosol optical thickness (AOT) product is 10 km. This product is suitable for global research, but it faces difficulty in local area research, especially in a city. In order to get detail aerosol distribution in local area or a city, this article mainly discussed how to retrieve 1 km resolution AOT and how to estimate surface reflectance in the visible from archived MODIS surface reflectance product. The archived MODIS surface reflectance product is mainly used to build surface reflectance database that is used to estimate surface reflectance in the visible. Based on the database, the surface reflectance of band blue was first estimated and then the AOT of Beijing was retrieved using Dense Dark Vegetation (DDV) method and the surface reflectance estimated using the database.

Snow extent and snow depth (SD) are critical parameters in metro-hydrological models and are sensitive to the global climate change. Over the western China, due to the influence from shallow snow, changing seasonal permafrost and the sparse observation stations, the passive microwave remote sensing algorithm show its applicability when using the gradient brightness temperature (Tb) algorithm of 36.5Ghz-18.7Ghz. In this work, we employ one whole-winter Tb extracted from Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) L2A swath dataset and the ground measurements of snow depth (SD) to analyse the snow microwave emission and gradient algorithm ability. The time series analysis shows that the Tb differences (36.5-18.7) and (36.5-10.7) are sensitive to relatively deep snow (>20cm), while the Tb differences (89.0-18.7) are sensitive to the occurrence of the new snow, with a promising correlation with shallow snow (<;15cm) and quickly decreasing (melting) snow depth, which suggest that a high frequency Tb difference could potentially be a good snow monitoring signal for the shallow snow cover over western China.

In this study, we develop a combined active/passive technique to estimate surface soil moisture with the focus on the short vegetated surfaces. We first simulated a database for both active and passive signals under SMAP's sensor configurations using the radiative transfer model with a wide range of conditions for surface soil moisture, roughness and vegetation properties that we considered as the random orientated disks and cylinders. Using this database, we developed 1) the techniques to estimate surface backscattering and emission components and 2) the technique to estimate soil moisture with the estimated surface backscattering and emission components. We will demonstrate these techniques with the model simulated data and its validation with the airborne PALS image data from the soil moisture SGP'99 and SMEX'02 experiments.

In the article, the research of sub-pixel snow mapping was conducted using moderate resolution data of remote sensing for obtaining high-accuracy data of snow cover in Qinghai-Tibet Plateau. But the end-member database is very large in the research, so the amount of calculation is too great if all the end-members will be used in the unmixing of pixel. In light of the characteristics of end-member database, the method of combining use of typical and neighboring end-members was established for the pixel unmixing of MODIS data in Qinghai-Tibet Plateau. Through the method, the percentage data of snow cover have been obtained in the plateau. Based on ASTER data, the validation has been conducted for the unmixing result in the research, which is quite accurate and reliable.

The effects of soil surface roughness play a significant role in the microwave emission from the surface. Therefore, a good parameterization of the effects is a prerequisite for retrieving surface soil moisture information. With recent physical model developments, the advanced integral equation model (AIEM) has been proven to provide accurate representation over a wide range of surface-roughness conditions. We evaluated the capability of the AIEM model in simulating multiangular surface emission signals in comparison with a field experiment data set. A simplified multiangular surface emission model was developed based on simulated database using the AIEM model. Based on the parameterized model, an inversion procedure was developed using dual-polarization microwave brightness temperatures to retrieve soil moisture. Two data sets were used to test the inversion algorithm, and the accuracies in root-mean-square error were about 4% for incidence angles from 20° to 50°. This new simple model is suitable for soil moisture retrieval from future L-band satellite data.

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

Vegetation biomass is an important parameter in the carbon cycle study. In this paper, a new technique to estimate aboveground vegetation wet biomass based on the Microwave Vegetation Indices (MVIs), which are computed through the observed brightness temperature of AMSR-E/Aqua under two adjacent frequencies, has been developed. The MVIs can provide significant new information compared with the conventional optical vegetation indices since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy where the microwave sensor can ¿see¿ through. We know that the absorption effect of vegetation canopy is mostly controlled by the total wet biomass. In this technique, we first retrieve the single scattering albedo and the optical thickness based on model simulations under AMSR-E configuration. Then, the estimated above two properties are used to derive the absorption fraction of vegetation. Finally, it can be related to the aboveground vegetation wet biomass.

Snow depth (SD) is an important input parameter for snow cover hydrologic model and climate model. In China, the snow volume is affected by the plateau climate and different geographical situation, which shows specific rules and characteristics in space and time distribution. Consequently, it is very necessary to dynamically estimate the snow volume of China area. In this paper, we use passive microwave to estimate the snow depth in China, through the analysis on the characteristics of time, space and geographical environment of the snow zone in China, we added the impact of snow cover in pixel, high-frequency (89.0 GHz) on the accuracy of inversion and on the basis Chang's classical algorithm of inversion of snow water equivalent, considered that there were different responses to the microwave in different types of surface, improve the algorithm of inversion of snow water equivalent in China. The results show that new inversion algorithm can improve the precise of the inversion of snow depth in the area of China. However, the low spatial resolution of microwave, complex types of feature in the ground pixel and the changes of the snow status with time and space, which make it difficult to invert snow water equivalent, so need to further study.

Knowledge of the variability of microwave signatures of vegetation and roughness are necessary to separate these influences from those of soil moisture for remote sensing applications to global hydrology and climate. Conventional, vegetation indices are often limited by the effects of atmosphere, background soil conditions. The Soil Moisture and Ocean Salinity (SMOS) satellite will provide global multi-angular microwave brightness temperature observations at L-band, in dual polarization and multi-angles. Through the analysis of the simulations by the advanced integral equation model (AIEM), we found that the polarization difference for the bare surface emission signals at different view angles can be well characterized by a line function with parameters that are dependent on the pair of view angles to be used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-angular radiometer measurements. The developed microwave vegetation index (MVI) is independent of soil surface emission signals and can provide new vegetation information since the L-band microwave measurements are sensitive to the properties of the overall vegetation. We compared the developed MVI with the Leaf Area Index (LAI) with SMOSREX data in 2003. The results showed that the general distribution and the change patterns of the MVI are consistent with those of LAI and VWC derived by the field experiments. This method provides a new opportunity to monitor vegetation cover for future SMOS applications.

Snow Water Equivalent (SWE) is a crucial parameter in the studies of hydrology and climatology. Estimating SWE by using high-resolution radar systems, especially those capable of providing high-frequency observations, is an important task in the microwave studies. In this paper, a parameterized snow scattering model was first developed to provide the model basis for the snow inversion problems. A scheme based on the parameterized model, analysis on the depolarization factor as well as the scattering and extinction relationships between X and Ku-band snow backscattering signals was then developed for SWE inversion. Initial evaluation on the technique was made through theoretically simulated database. The estimated SWE is found to be well correlated with simulated SWE with an acceptable accuracy.

This paper investigated the emission behavior at 18.7 GHz, 36.5 GHz and 89 GHz over the snow-cover surface and after snow completely removed surface at the Local Scale Observation Site (LSOS) in Fraser, Colorado, USA with 55° incidence angle) using one-layer and two-layer emission model, which is based on the radiative transfer by Matrix Doubling approach with the dense media theory and the surface scattering Model. From the comparisons with the GBMR-7 observation on Feb. 21, both the two-layer emission model and one-layer emission model could predict the observed brightness temperature over snow-covered surface well, but the polarization difference predicted by two-layer emission model was relatively smaller than one-layer model did. In addition, we attempted to interpret the emission magnitude and polarization separation of snow-removed surface by incorporating a transition layer below the soil medium. We also demonstrated the effect of snow fraction on the brightness temperature difference at 18.7 GHz and 36.5 GHz over snow-cover surface with the field observation and model simulation.

In the context of the SMOS mission, a new numerical method based on the finite element method is presented which can be used to model the radiometric L-band emission of soil and litter layers in forests. Many different characteristics of these layers that affect the soil-litter emission can be incorporated into the model, including surface roughness, inclusions and volume effects. Soil moisture is incorporated into the model as a function of the dielectric permittivity constant. The model is validated for a single dielectric layer with a surface roughness of Gaussian autocorrelation function by comparing results of the backscattering coefficient with those calculated by the 2D method of moments. Good general agreement is obtained between these results. An emissivity calculation for a single layer rough surface is also presented and compared with the emissivity of a flat layer.

A simple linear regression algorithm to retrieve LST was presented in this paper. AvgSurfT from GLDAS was used to fill up the data gaps of MODIS LST due to cloudiness in the Tibet Plateau. The objective of our algorithm is to establish multi-liner regress equations between brightness temperature of AMSR-E and the combined temperature from MODIS and GLDAS under different land cover types, and then we retrieved LST using AMSR-E between brightness according to the coefficients obtained from regress equation. The retrieved LST and MODIS LST were examined to evaluate the algorithm. It was found that the retrieved results were good under the surface type categories of shrub, crop and grass due to the minimal variety of the land surface emissivity.

Snow plays an important role in meteorological and hydrological studies, so it makes sense to accurately predict the process of snow and the amount of snow. Exactly modeling snow properties is an important process for the combined snow process model and microwave model to simulate the amount of snow. In this paper, a mass and energy balance computer model-snow thermal model (SNTHERM.89) is used to simulate the snow properties combined with experimental data measured in Binggou basin, an alpine catchment in Gansu province, china during March 11th and April 7th in 2008. SNTHERM can simulate the snow properties well. In an attempt to make sure that the data for the input is with highest degree of confidence when some measurements are missing, sensitivity analysis of snow properties to forcing data was conducted. Through evaluating the sensitivity of SNTHERM to forcing data, a better understanding of the model and prediction can be obtained.

Moderate Resolution Imaging Spectroradiometer (MODIS) data is suitable for water mapping, easy to get and having high temporal and wide spatial coverage. This study describes a comprehensive method to produce routinely maps of water cover in Tibetan Plateau with MODIS Surface Reflectance products (MOD09A1) at 463.51 m resolution. Multi-index end-member selecting algorithm was applied to identify end-members including water, forest, grasslands, barren and cloud. Based on the character of land cover spatial distribution, a typical-and-near end-member selecting method and a fully constrained linear unmixing method were adopted to unmix the mixed pixels. The accuracy of the water maps and performance of the algorithm were assessed using 6 pairs of synchronic MODIS/ETM+ images. The method is well-suited to mountainous environment

In satellite passive microwave measurements, the sky brightness temperature is a function of frequencies, sensitive to parameters such as water vapor content, liquid water (cloud and precipitation), oxygen, hydrometeors and atmospheric temperature. In order to investigate the atmospheric influence to the retrieval of snow parameters quantitatively, firstly, we combined the HUT (Helsinki University of Technology) snow emission model (except the atmosphere parameterization) and an atmosphere model to do theoretical simulation estimations. We indicate that the C and X band atmospheric influence could be ignored, while the atmosphere is a non-negligible absorber and emitter of microwave radiation at frequencies higher than 19 GHz. We also launched a 13-day experimental measurement in winter time over Sodankyla¿, Finland, with synchronous satellite (AMSR-E) and tower-based radiometer measurements, together with extensive in-situ atmospheric measurement dataset. The evaluation result indicates that the atmosphere plays a relative positive contribution (about 20K for 36.5GHz and 89.0/94.0GHz). The difference between satellite observation and point experiment comparison suggests conducting more physical model work with atmosphere contribution.

This study demonstrates a new algorithm development for estimating bare surface soil moisture using dual-polarization L-band backscattering measurements. Through our analyses on the numerically simulated surface backscattering database by Advanced Integral Equation Model (AIEM) with a wide range of soil moisture and surface roughness conditions, we found that the relative difference of the overall surface roughness parameters at the different co-polarizations can be well estimated through a roughness index. This new finding leads to an algorithm on estimation of bare surface soil moisture. We will demonstrate the theory and techniques of this algorithm through the AIEM simulated database and validate it with two field ground scatterometer experimental data. The results indicate that bare surface soil moisture can be estimated quite well with only co-polarized backscattering signals. It provides a solid support for Soil Moisture Active and Passive mission (SMAP).

In order to evaluate the accuracy of snow water equivalent (SWE) inversion algorithm for passive microwave sensor Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) in Western China, we compared SWE got from AMSR-E daily SWE product with the ground measurements from 15 meteorological stations in Tibetan plateau. The results show AMSR-E overestimate SWE in this regions and the RMSE is 21mm Tibetan plateau. Through incorporating snow fraction factor, a new empirical algorithm estimate snow depth and SWE have been developed in Tibet. This new algorithm appeared higher accuracy than AMSR-E. Due to complex topography, shallow patchy snow and frozen grounds covered at the Tibetan Plateau, this technique didn't show good results. In future we will focus on how to evaluate and eliminate the effects of these factors quantitatively on SWE retrieval.

As the global warming intensifies, the environment changes in cold land receive more attention. In this paper, a combined microwave emission model is established for cold land researches. Through field observation experiment, the b-factor of winter wheat during winter is obtained to simulate radiation accurately from this typical ground object in China. Furthermore, the impacts of snow and vegetation cover on frozen soil radiation are investigated by sensitivity analysis.

In order to improve accuracy of monitoring drought using temperature condition index (TCI), TCI was calculated using the ground temperature which was inverted from the AVHRR data instead of the brightness temperature, and the TCI models for monitoring drought were established in the study. Based on the TCI models and the SHI criteria for classification of drought, the TCI criteria for monitoring drought can be obtained, and are used to monitor drought in China. The validation result of the research showed the TCI models are reliable, and TCI models of ground temperature are better than that of brightness temperature.

In this paper, we mainly studied of microwave emission behavior of shallow snow cover with the field experiments over Huabei Plain, China., The evaluation of microwave emission character over snow surface was using the data collected by a ground-based multi-frequency and dualpolarization microwave radiometer (RPG-8CH-DP) at 10.7 GHz, 18.7GHz and 36.5 GHz, with the incidence angles ranging from 20° to 60°. Through analysis of the observation brightness temperature, we found that the radiation behavior of thin snow cover is very different from that of deep snow, especially during melting and refreezing period of thin snow cover. One is that the emission over shallow snow surface increased as frequencies increase. Secondly, at the same frequency, when shallow snow was melt in diurnal refreezing-thaw cycle, the emission would decrease. These two emission behavior were caused by the attenuation of snow cover was weak than the increment of emission from the underground snow surface. From this study, it has been shown that the ground-based microwave radiometry provides a useful tool to investigate the radiation characteristics of thin snow cover and snow type identification. It also helps to evaluate snow emission models and develop retrieval algorithms of snow characteristics from space-borne microwave radiometer data.

Snow monitoring at regional and continental scale is an important task in Cryosphere studies. Space-borne active microwave sensors are capable of delivering long-term, all-weather observations of snow at large scale and providing information on key snow parameters, such as snow water equivalent (SWE). However, the complexity of snowpack makes it difficult to model the microwave scattering and retrieve snow parameters. A computationally efficient snow scattering model with simple terms is desirable for studying microwave interactions with snow and developing inversion techniques. In this study, a parameterized dry snow scattering model for analyzing X-band and Ku-band snow measurements is developed. The parameterized model is built from the microwave signal database simulated from a wide range of snowpack conditions by a theoretical model, which accounts for multiple-scattering effects and has been well-validated. The parameterized model is simple and reliable, with RMSEs 0.20 dB, 0.24 dB and 0.43 dB for VV, HH and VH polarizations, respectively.

The Moderate Resolution Imaging Spectra-radiometer (MODIS/Aqua) and the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) are two sensors aboard on satellite Aqua. Atmospheric parameters retrieved from MODIS/Aqua, such as the layered atmosphere temperature, humidity and pressure profile and land surface temperature (LST), are used to help the estimation of AMSR-E instantaneous microwave emissivity in clear sky conditions over land. As an example, a two-week (from 12-08-2006 to 25-08-2006) instantaneous emissivity over land has been calculated globally for 6.9-, 10.7-, 23.8-, 36.5-, and 89.0-GHz using both polarizations, ascending and descending orbit respectively. The calculated AMSR-E emissivities agree well with the other study [6] through comparison, and can provide more details. The frequency dependency and correlation analysis show the promising emissivity prediction with different channels. The time series analysis over different land covers reveal that the variation of emissivities do not exceed 0.05 in average and it is almost zero for polarization difference (PD) change, which all induce that the time extrapolation of emissivities could tackle cloud contamination issues (time series gap).

The purpose of this study is to evaluate whether the NASA/JPL dual frequency airborne system, Passive Active L-band and S-band (PALS), can provide a reliable soil moisture measurements so that they can be integrated to provide soil moisture data at the scales of the spaceborne coarse resolutions. Through evaluations of the AIEM simulated the random rough surface emissivities with a wide range of soil moisture and roughness conditions at both L-band and S-band, it was found that the bare surface emission signals at above two frequencies are essentially close to identical regardless of the surface soil moisture and roughness properties when the effect of soil properties (temperature and moisture) in the vertical profile on emission signals is minor. This makes it possible to further estimate the vegetation components in the omega-tau model at each frequency and polarization and to carry out the corrections for the vegetation effects without assumption on polarization dependence of the vegetation effects. Thus, the surface soil moisture can be inferred by the estimated surface emission signals. We will show the evaluation and validation of this technique with the airborne PALS measurements obtained during SMEX'02 soil moisture experiment with the intensive ground soil moisture measurements.

ERS Wind Scatterometer provides capability of the multiple angles by their three different look antennas, In this study, we evaluate whether the multi-incidence angle observations can help on improving surface soil moisture estimations. With the theoretical surface backscattering model - the Advanced Integral Equation Model (AIEM), we first simulated a surface backscattering database with a wide range of surface roughness and soil moisture properties at different incident angles. Then, a parameterized surface backscattering model is developed using the simulated database. The newly developed simple model has the roughness function that can be described by a single combined roughness parameter from the commonly used surface roughness descriptors (RMS height and correlation length). This makes it possible to be used as an inversion model. We will demonstrate this simple model development, its accuracy, and inversion test by using the ground measurements from the Intensive Observation Period (IOP'98) field campaign in 1998 of the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment Tibet (GAME/Tibet).

Effectively monitoring vegetation water is essential to improve our understanding of agriculture and hydrology. Vegetation water content is often estimated using vegetation indices derived from optical satellite sensors. In this study, we introduced the new microwave vegetation indices (MVIs) and derived the new MVIs using observation from the Advanced Microwave Scanning Radiometer (AMSR-E). To demonstrate the potential of the proposed MVIs, we compared them with vegetation water content which were obtained using ground based observations of vegetation water content and reflectance from a MultiSpectral Radiometer (MSR) during the National Airborne Field Experiment 2006 (NAFE'06), as well as the coincident Landsat 5 TM data. The estimated vegetation water content were averaged and compared with daily MVIs derived from AMSR-E on EASE-GRID pixels. The results of comparison between the MVIs and crop water content on EASE-GRID pixels demonstrated that the MVIs could be used to monitor the vegetation water content, which suggests additional all weather information and a potential linkage of the two data sources. In combination with vegetation indices derived from conventional optical sensor, MVIs provide a possible complementary dataset for monitoring global vegetation from space.

In this paper, we studied the atmospheric effects which were caused by non-precipitation cloud on soil moisture retrieved by AMSR-E brightness temperatures. The cloud liquid water was retrieved by using AMSR-E 89 GHz temperature brightness. Then the atmospheric effects were computed on X band which was used to retrieve soil moisture. Because of the cloud effect, the surface temperature can not be reliably estimated using infrared satellite data, we have used an algorithm to minimize the effect of surface temperature on soil moisture retrievals. Then the retrieved soil moisture was validated by the experimental data in CEOP. The results indicated that the retrieval of soil moisture was indeed impacted by atmospheric non-precipitation cloud though the effect was very small in the study area.

Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11, 12, 13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11, 12, 13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11, 12, 13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated.

In this paper, we study the bistatic scattering using an advanced integral equation model (AIEM). By keeping all the surface current terms in the Kirchhoff surface fields, the bistatic scattering coefficients are obtained. For simplification, the complete Kirchhoff field did not cast into the derivation of the complementary field. We compare varied updated versions of IEM-based models with the small perturbation model, geometrical optics model, and Kirchhoff approximation standard models at respective regions of validity. The results indicate that the new AIEM provides much more accurate predictions for bistatic scattering.

Microwave scattering from snow is difficult to model due to the complexity and heterogeneity of natural snow. In this paper, we developed a multi-layer, multi-scattering model based on recent theoretical advances in snow and surface modeling. In the proposed multi-layer model, Matrix Doubling method is used to account for scattering from each snow layer; and Advanced Integral Equation Model (AIEM) is incorporated into the model to describe surface scattering. Comparisons were made between the model predictions and field observations from truck-mounted L- and Ku-band scatterometers (frequencies are 1.25 GHz and 15.5 GHz) at Local-Scale Observation Site (LSOS) of NASA Cold- land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3). It was found that model predictions were in good agreement with field observations with proper particle size selected. Analysis on scatterer shape, multiple scattering and snow stratification effects were also made based on model simulations.

Surface temperature has significant impact on the microwave brightness temperature measurements. In this study, we evaluate the surface temperature effects on soil moisture estimation. Two soil moisture inversion methods for bare surface based on surface microwave emission model-advanced integral equation model (AIEM) were developed. A parameterized model-Qp model was used to cancel out the roughness effect and statistical method was used to cancel out the surface temperature effect. Finally, the in situ measurements from CEOP experiment were used to validate the retrieval results of both algorithms.

The IEM model proposed in 1992, for rough surface scattering has been extensively applied for microwave remote sensing of terrain. Model validation has been made by experimental measurements and by numerical simulations. However, estimation accuracy was verified mostly for like-polarizations in the monostatic configuration. Since then, much effort has been devoted to improving the model performance. A recent AIEM(Advance IEM) incorporated these improvements and demonstrated significant enhancement in accuracy. Analysis was also extended to bistatic scattering for a wide range of surface parameters. In this study, we recognized that both theoretically and experimentally, the modified Stokes vector (Kennaugh matrix), including polarization correlation terms, are very useful to retrieve surface parameters such as wind field. Consequently, we extend the AIEM model to include all polarization correlation terms in order to understand the dependence of four Stokes terms on geophysical surface parameters, such as roughness, correlation length, and dielectric constant. Sensitivity analysis is also performed. Another useful aspect is in the physical interpretation of polarimetric returns as a function of azimuthal angle. Potential applications to soil moisture estimation will be illustrated and discussed.

The IEM model proposed in 1992, for rough surface scattering has been extensively applied for microwave remote sensing of terrain. Model validation has been made by experimental measurements and by numerical simulations. However, estimation accuracy was verified mostly for like- polarizations in the monostatic configuration. Since then, much effort has been devoted to improving the model performance. A recent AIEM(Advance IEM) incorporated these improvements and demonstrated significant enhancement in accuracy. Analysis was also extended to bistatic scattering for a wide range of surface parameters. In this study, we recognized that both theoretically and experimentally, the modified Stokes vector (Kennaugh matrix), including polarization correlation terms, are very useful to retrieve surface parameters such as wind field. Consequently, we extend the AIEM model to include all polarization correlation terms in order to understand the dependence of four Stokes terms on geophysical surface parameters, such as roughness, correlation length, and dielectric constant. Sensitivity analysis is also performed. Another useful aspect is in the physical interpretation of polarimetric returns as a function of azimuthal angle. Potential applications to soil moisture estimation will be illustrated and discussed.

It is very difficult to retrieve the land surface temperature (LST) from passive microwave remote sensing because a single multi-frequency thermal measurement with N bands owns n equations in N+1unknowns (N emissivities and LST) which is a typical ill-posed inversion problem. However, the emissivity is mainly influenced by dielectric constant which is a function of physical temperature, salinity, water content, soil texture, and other factors (the structure and types of vegetation). These make it very difficult to develop a general physical algorithm. This paper intends to utilize the multiple- sensor/resolution and neural network to retrieve land surface temperature from AMSR-E data. MODIS LST product is made as ground data which overcomes the difficulty of obtaining large scale land surface temperature data. The retrieval result and analysis indicate that the neural network can be used to accurately retrieve land surface temperature from AMSR-E data.

This paper demonstrates a study to the atmospheric influence on the passive microwave Brightness Temperature (BT) in Tibetan Plateau area at clear-sky condition. The absorption and emission of dry air and water vapor are considered as the main contribution of atmosphere at the fact of cloud-free. We choose the day of Dec. 07, 2005 as an example, and calculated the atmosphere absorption factor and effective atmospheric temperature which are based on an updated atmospheric microwave absorption model. With the help of MODIS-Aqua land surface products (MYD11_L2) and MODIS atmospheric profile (MOD07_L2) products, which can decide a real atmospheric status, a simplified radiative transfer equation (RTE) is employed to estimate the AMSR-E frequencies surface emissivity over Tibetan Plateau. As a result, the surface actual microwave brightness temperature is obtained through the product of retrieved emissivity and MODIS LST, it can be found that the atmospheric contribution to the brightness temperature add up to about 5.56K at 89.0GHz and average 0.54K at 23.8GHz somewhere Tibetan Plateau in the cloud-free winter days, and the space variation of atmospheric effect to microwave BT has been further discussed.

Major uncertainties in deriving vegetation indices from satellite measurements are the effects of atmosphere and background soil conditions. Through numerical simulations by surface emission model - Advanced Integral Equation Model (AIEM), we found that bare surface emissivities at different frequencies can be well characterized by a linear function with parameters that are dependent on the pair of frequencies to be used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multifrequency radiometer measurements. Using the radiative transfer model (ω-τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. It can be shown that the microwave vegetation index derived by the intercept and slope of this linear function depends only on vegetation properties and can be derived from the dual-frequency and dual-polarization measurements. We will demonstrate the theoretical basis of this new microwave vegetation index and show comparisons of the microwave derived vegetation index with the optical sensor derived NDVI measurements.

Soil moisture is a key component in the hydrologic cycle and climate system. It is an important input parameter for many hydrologic and meteorological models. Taking the advantage of the multi-incident angles of the ERS Wind Scatterometer(WSC), a new soil moisture retrieving method, that significantly improves the surface backscattering presentation, is proposed in this study based on the Advanced Integral Equation Model (AIEM) and the Water-Cloud model. It utilizes the correlations in each backscattering components (bare soil and vegetation) for the simultaneous measurements of each incident angle pairs to reduce the effect of surface roughness and vegetation scattering on soil moisture estimation. The result is validated by using the ground measurements from the Intensive Observation Period (IOP’98) field campaign in 1998 of GAME/Tibet in the end of this paper, and the time series of the estimated soil moisture shows a consistent trend with those sampled on the ground.

To retrieve soil moisture at vegetated surface by microwave radiometry data, vegetation effect is important to remove. Usually the zero-order model, ω — τ model, is often used where vegetation is treated as an uniform layer. Values of both scattering and attenuation characteristics of vegetation are assigned from experience which there’re slight change at different frequencies or polarizations. As frequencies go higher than L-band, multiple-scattering effect inside vegetation layer and that between vegetation and soil surface is necessary to account for. In this paper, to accurately evaluate vegetation effect at C and X bands, a Matrix-Doubling algorithm is used to retrieve ω and τ of corn. The surface emission model is AIEM that could work well at large roughness and wider frequency range. The preliminary simulation results are presented in this paper.

We intend to propose a multiple-band algorithm which can simultaneously retrieve land surface temperature and emissivity from ASTER data. We build four radiance transfer equations for ASTER band 11, 12, 13, 14, which involve six unknown parameters (average atmosphere temperature, land surface temperature and four bands emissivity). We also analyze the emissivity characteristics of common objects about 160 kinds provided by JPL spectral database between thermal band 11, 12, 13, 14 and find that there is approximate linear relationship between them. For common 80 kinds terrors, the average emissivities error of band 11 and 14 are all under 0.01, the max emissivity error is under 0.0097 for band 11 and 14. So we can obtain six equations and six unknown parameters. In order to improve the accuracy, we can make some classification before retrieving land surface temperature. We can use three methods to resolve the equations. The first is that we make classification for image and get different equation, then resolve the equation. The second is Least-squares. The third is that, we can simulate database according to the characteristics of objects and utilize the neural network to resolve equations. The analysis indicates that the neural network can improve the practical and accuracy of algorithm.

In 2002 the Hydrosphere State Mission (Hydros) was selected by NASA as the alternate mission for a flight opportunity under its Earth System Science Pathfinder program. The Hydros mission objective was to collect the first global scale measurements of the Earth's soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. Although NASA cancelled the Hydros mission in December 2005 due to insufficient funding and its reversion back to alternate mission status, the development of accurate soil moisture retrieval algorithms and associated error analyses begun under the Hydros project are still relevant to SMOS and to other potential future soil moisture missions.

In this study, we evaluated the feasibility of using the dual frequency (X-band 9.6 GHz and Ku-band 17 GHz) and dual polarization (W and VH) radar to estimate snow water equivalence through numerical simulations.

This study demonstrates the impacts of land water, dry snow cover, and terrain at sub-pixel scale on soil estimation through numerical simulations. We will demonstrate the quantity of the above different factors contributing to the errors on the soil moisture estimation.

A novel method, which employs both a matrix doubling algorithm and the first-order solution of a radiative transfer (RT) equation for modeling microwave backscattering from forest, is presented in the paper. The method is based on the assumption that a forest canopy can be divided into a number of distinct horizontal vegetation layers over a dielectric half-space rough surface. The scattering phase matrix of each layer is calculated by either matrix doubling to account for the multiple-scattering effect or first-order solution of an RT equation, depending on the scattering characteristics of the layer. The first-order solution of the RT equation is used for the trunk layer while the matrix doubling technique is applied to both the crown layer and understory. The advanced integral equation model and reflectivity matrix are used to calculate the noncoherent and coherent surface boundary conditions. Comparisons between model predictions and field measurements on radar backscattering coefficients for a walnut orchard showed a good agreement at both L-band and X-band and for all three polarizations. Comparative analyses of model predictions for backscattering from a forest medium calculated using the combined model, first-order RT model, and the standard matrix doubling model were also presented. Understory effects, that can significantly change the weight of each scattering mechanism, were also evaluated by using the combined method.

A physically based bare-surface soil moisture inversion technique for application with passive microwave satellite measurements, including the Advanced Microwave-Scanning Radiometer-Earth Observing System, Special Sensor Microwave/Imager, Scanning Multichannel Microwave Radiometer, and Tropical Rainfall Measuring Mission Microwave Imager, was developed in this paper. The inversion technique is based on the concept of a simple parameterized surface emission model, the Qpmodel, which was developed using advanced integral equation model simulations of microwave emission. Through evaluation of the relationship between roughness parameters Qpat different polarizations, it was found that they could be described by a linear function. Using this relationship and the surface emissivities measured from two polarizations, the effect of the surface roughness is cancelled out. In other words, this approach consisted in adding different weights on the v and h polarization measurements so as to minimize the surface roughness effects. This method leads to a dual-polarization inversion technique for the estimation of the surface dielectric properties directly from the emissivity measurements. For validation, we compared the soil moisture estimates, derived from ground radiometer measurements at C- to Ka-band obtained from the Institute National de Recherches Agronomiques' field experimental data in 1993 and the Beltsville Agricultural Research Center's field experimental data at C- and X-band obtained in 1979-1982, with the field in situ soil moisture measurements. The accuracies [root-mean-square error (rmse)] are higher than 4% for the available experimental data at the incidence angles of 50deg and 60deg. The newly developed inversion technique should be very useful in monitoring global soil moisture properties using the currently available satellite instruments that commonly have incidence angles between 50deg and 55deg

Abstract : An important issue in the development of a dedicated space borne soil moisture sensor has been concern over the reliability of soil moisture retrievals in densely vegetated areas and the global extent over which retrievals will be possible. Errors in retrieved soil moisture can originate from a variety of sources within the measurement and retrieval process. In addition to instrument error, three key contributors to retrieval error are the masking of the soil microwave signal by vegetation, the interplay between nonlinear retrieval physics and the relatively poor spatial resolution of space borne sensors, and retrieval parameter uncertainty. Quantification of these errors requires the realistic specification of land surface soil moisture heterogeneity and spatial vegetation patterns. Since detailed soil moisture patterns are currently difficult to obtain from direct observations, an attractive alternative is the application of an observing system simulation experiment (OSSE) in which simulated land surface states are propagated through the sensor measurement and retrieval process to investigate and constrain expected levels of retrieval error. This manuscript describes results from an OSSE designed out to simulate the impact of land surface heterogeneity, instrument error, and retrieval parameter uncertainty on radiometer-only soil moisture products derived from the NASA ESSP Hydrosphere State (Hydros) mission.

Abstract—A microwave scattering model based on second-order solution of radiative transfer equation has been developed for dry snow. Advanced integral equation model (AIEM) and a semi-empirical model were included in the model to account for ground contribution. Also, ellipsoid grain shape was adopted to describe ice particle. This model was compared with the ground-based scatterometer data (frequencies are 1.25GHz and 15.5 GHz) from NASA Cold-land Processes Field Experiment (CLPX). Inputs to the model were from Local-Scale Observation Site (LSOS) snow pit measurements, except that particle size and shape were computed as free parameters. The comparison shows that the model agrees well with the field data. Also from the comparison, it could be seen that particle shape had a significant effect on the cross-polarization signals. Keywords- snow; second-order model; CLPX I.

This study describes a semi-empirical bare surface emission model for AMSR-E. Through evaluation of a bare surface emission database generated by the Advanced Integral Equation Model (AIEM) for a wide range of surface dielectric and roughness properties under AMSR-E, we developed a new semi-empirical multi-frequency-polarization surface emission model - the Qp model. This model relates the effects of the surface roughness on the emission signals through the roughness variable Qp at different polarization - p (v or h). The Qp can be simply described as a single surface roughness property of the random surface slope - S. The comparison of the simulations by the Qp and AIEM models indicated that the error is extremely small, its magnitude is only as 10 -3 . The evaluation of this model with the field experimental data also showed a very good agreement. We will show its validation with the field ground radiometer measurement and its application in estimation of soil moisture.

Abstract : Surface can be characterized in terms of its material (dielectric) and geometric properties. The dielectric properties of the surface are expressed primarily by its moisture content, while the roughness describes the geometric characteristics of surface. Various techniques for information retrieval from remotely sensed data have been proposed in a number of recent studies. Some of them are based on an empirical relationship between the measured return signals and the ground truth. Because of their development from a limited number of observations, these models are generally valid only for the conditions under which those measured data were taken. These models also appear that no dependence on the roughness parameter, l-correlation length. In this work, the potential of using the polarimetric SAR data over surface scatterers in order to invert surface parameters is investigated. The model-based and image-based inversion schemes are investigated and compared; the former is doing retrieval from a dynamic learning neural network trained with the Advanced Integral Equation Model, while the latter is schemed from a decomposition of coherency matrix. In model based approach, only the surface scattering term of total return is used in order to remove the vegetation effects. The image based approach accounts for non-zero cross-polarized, backscattering as well as depolarization by three polarimetric parameters, namely the scattering entropy(H), the scattering anisotropy(A), and the alpha angle(alpha). The features of these two schemes are discussed in terms of numerical aspects and physical implications of the surface parameters being inverted by using experimental E-SAR L-band data . We also show the performances of inversion and discuss the advantages and drawbacks of both schemes.

A regression analysis between brightness of all AMSR bands and MODIS land surface temperature product provided by NASA indicated good correlation, so retrieving land surface temperature from AMSR passive data is available without ground truth data (soil moisture and land surface type). The analysis results (North-Africa, North-East China, Tibet) indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately, the land surface at least are classified into two groups. For non-snow covered land surface, The average land surface temperature error is about2- 3℃ relative to the MODIS LST product. For snow covered land surface, The average land surface temperature error is about 3-4℃ relative to the MODIS LST product. On the other hand, the emissivity of passive microwave is very important parameter for retrieving soil moisture. We compute the emissivity through land surface temperature retrieved by statistical regression method and make some analysis.

This study demonstrates the capability of estimating soil moisture using multi-polarization L-band backscattering coefficients. It shows an algorithm development for estimation of bare surface soil moisture and roughness. It is found that the surface rms height can be estimated quite well with only co- polarized backscattering signals. For estimation of the surface dielectric properties, the accuracy can be significantly improved by using all three polarization signals.

Abstract – This study demonstrates a technique of estimating soil moisture using the combined passive/active L-band microwave measurements. It shows 1) evaluation of the small albedo assumption for using dual polarization passive measurements, 2) development of a synthesized technique to estimate soil moisture, and 3) evaluation with ground soil moisture measurements from the SMEX02 experiment data. I.

Abstract-Seasonal snow cover plays a significant role in regional, large even global scale climate processes, hydrological cycle and surface thermal balance processes. MODIS snow cover methods provide an operational model for mapping each pixel into snow or no-snow by using a normalized difference snow Index (NDSI) and its threshold test. But they cannot provide the fractional snow cover information. This paper presents an automated snow-mapping technique at sub-pixel resolution in Tibetan Plateau based on MODIS normalized difference snow fraction and normalized difference Vegetation fraction. This paper presents an automated snow-mapping technique at sub-pixel resolution based on MODIS normalized difference snow Index (NDSI) and normalized Difference Vegetation Index(NDVI) in order to avoid overestimations due to vegetation cover. And it takes the ASTER data as ground true data to verified this method. Keywords-MODIS, ASTER, sub pixel snow mapping, Tibetan plateau I.

Abstract –Accurate monitoring of snow cover extent is an important research goal in the science of Earth systems. Now mixture modelling is important tool in the remote sensing community as researchers attempt to resolve sub-pixel, area information. In the paper, I tested four different sub-pixel analysis methods: Linear Mixture Model (LMM), Fuzzy c-means Clustering (FCM), Back-Propagation Neural Network (BPNN), and Support Vector Machine (SVM). Overall, the LMM and SVM method provided better estimates of the snow cover components than others, and the results of this study provide comprehensive information of the utility of sub-pixel analysis for the estimation of snow cover components and suggest that the comparatively accurate snow cover estimation is attainable from medium resolution satellite imagery. I.

Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and Space Administration Hydrosphere State (Hydros) mission. Simulated retrieved soil moisture products are created using three distinct retrieval algorithms based on the characteristics of passive microwave measurements expected from Hydros. The accuracy of retrieval products is evaluated through comparisons with benchmark soil moisture fields obtained from direct aggregation of the original simulated soil moisture fields. The analysis provides a quantitative description of how land surface heterogeneity, instrument error, and inversion parameter uncertainty impacts propagate through the measurement and retrieval process to degrade the accuracy of Hydros soil moisture products. Results demonstrate that the discrete set of error sources captured by the OSSE induce root mean squared errors of between 2.0% and 4.5% volumetric in soil moisture retrievals within the basin. Algorithm robustness is also evaluated for the case of artificially enhanced vegetation water content (W) values within the basin. For large W(>3 kg/spl middot/m/sup -2/), a distinct positive bias, attributable to the impact of sub- footprint-scale landcover heterogeneity, is identified in soil moisture retrievals. Prospects for the removal of this bias via a correction strategy for inland water and/or the implementation of an alternative aggregation strategy for surface vegetation and roughness parameters are discussed.

This study develops a parameterized bare surface emission model for the applications in analyses of the passive microwave satellite measurements from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). We first evaluated the capability of the advanced integral equation model (AIEM) in simulating wide-band and high-incidence surface emission signals in comparison with INRA's field experimental data obtained in 1993. The evaluation results showed a very good agreement. With the confirmed confidence, we generated a bare surface emission database for a wide range of surface dielectric and roughness properties under AMSR-E sensor configurations using the AIEM model. Through the evaluations of the commonly used semiempirical models with both the AIEM simulated and the field experimental data, we developed a parameterized multifrequency-polarization surface emission model-the Qp model. This model relates the effects of the surface roughness on the emission signals through the roughness variable Qp at the polarization p. The Qp can be simply described as a single-surface roughness property-the ratio of the surface rms height and the correlation length. The comparison of the emissivity simulations by the Qp and AIEM models indicated that the absolute error is extremely small at the magnitude of 10/sup -3/. The newly developed surface emission model should be very useful in modeling, improving our understanding, analyses, and predictions of the AMSR-E measurements.

The ability to estimate soil moisture in the surface layer by microwave remote sensing has been demonstrated. But its application to hydrological and agricultural sciences has been hampered by the complexity of the vegetation canopy and surface roughness which significantly affect the sensitivity of radar backscattering to soil moisture. In this study, we used a simple vegetation model to estimate soil moisture and evaluated the results using multi-temporal L-band AirSAR data

We evaluate the capability of the microwave emission model that including the Dense Media Radiative Transfer Model (DMRT) and AIEM for simulation of dry snow emissivity. We compared the model predictions with the ground experimental measurements. The comparison shows our snow microwave emission model agrees quite well with the measurements.

The HYDROS mission objective is to collect global scale measurements of the Earth's soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. In order to examine how HYDROS soil moisture retrieval will be performed and how the retrieval accuracy will be impacted by vegetation water content and surface heterogeneity, an observing system simulation experiment (OSSE) was conducted using a modeled geophysical domain in the south-central United States centered on the Arkansas-Red River basin for a one-month period in 1994. Three separate radiometer retrieval algorithms were evaluated: (1) a single-channel algorithm (H polarization), (2) a two-channel iterative algorithm, and (3) a two-channel reflectivity ratio algorithm. Analysis indicates that the HYDROS accuracy goal of 4% volumetric soil moisture can be met anywhere in the test basin except woodland areas. Nonlinear scaling of higher resolution ancillary vegetation data can adversely affect algorithm retrieval accuracies, especially in heavy tree areas on the east side of the basin

Snow water equivalence is an important parameter for studies of the natural sciences, particularly in hydrology and climatology. This study demonstrates a concept of estimating snow water equivalence under the consideration of a dual frequency Ku band (13.4 GHz and 17 GHz) and a dual polarization system.

Through analyses of the model simulated database, we developed a technique to estimate surface soil moisture under HYDROS radar sensor (L-band multipolarizations and 40deg incidence) configuration. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12, 0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations

The ERS-1/2 wind scatterometer (WSC) has a resolution cell of about 50 km but provides a high repetition rate (less than four days) and makes measurements at multiple incidence angles. In order to estimate effective surface reflectivity (related to soil moisture content) over bare soil using this instrument, an original methodology based on the integral equation model (IEM) is presented that takes advantage of multiple view angular observations which means two independent observations at the same resolution cell and same time. The possibility of applying the inversion procedure to retrieve soil moisture is investigated using a set of data collected from the Intensive Observation Period (IOP '98) field campaign in 1998 of the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment Tibet (GAME/Tibet). The retrieved values obtained for the bare surface are consistent with ground measurements collected in these areas.

Radar backscattering response has the potential of retrieving desired snow parameters, such as snow water equivalence, snow depth, liquid water content which are important factors in hydrological investigation. The objective of this study is to develop an algorithm which can decompose the scattering of wet snow and also develop new description of surface scattering. There are two scattering sources - the volume scattering component from snow pack and the air-snow surface scattering component - for radar backscattering while observing wet snow. Depending upon which scattering component is dominant and then controls the response to snow wetness, an algorithm can be developed to quantitatively describe the relationship between this two scattering sources and snow wetness. We have established a model - simulated at C-bandata-base by using two scattering components. The database covers the most possible wet snow physical properties and surface roughness conditions. Using this data-base, an inversion algorithm can be developed for using C-band multi-polarization measurements. The newly developed algorithm mainly involved two steps: 1) decomposes the surface and volume scattering signals, and 2) then use each scattering component to estimate snow wetness

Lakes are valuable watersystems, used for production of drinking water, for fisheries and recreation, and can be important indirect or proxy indicator of climatic change. An important aspect to consider is the coverage of lakes, both temporal and spatial, which remote sensing satellites can provide. Modis data is suitable for regional to global operational observations, costing little and having high temporal coverage. But the mixed pixel is a common problem. The Tibetan Plateau is the most sensitive about climatic change and has hundreds of medium and small lakes whose cover is sensitive to the mixed pixels in image. The objective in our study is to develop high accuracy sub-pixel mapping algorithm for lakes cover monitoring in Tibetan Plateau. Firstly, we use the linear spectral unmixing technique to estimate the lake fraction in the mixed pixels. Secondly, we developed an algorithm, based on the concept of spatial dependence, to locate spatially the water within mixed pixel corresponding to the water proportion in the pixel. Finally, we used Aster data as "ground truth" to validate our algorithm. The algorithm produces a finer sub-pixel lake map comparative to the source Modis image.

For vegetated surfaces, the commonly used soil moisture (SM) retrieval algorithm is a zero-order model which might not be the best one for AMSR-E, since multiple scattering effects could not be ignored at higher frequencies. But due to the simplicity of the zero-order model, it still has the advantage in SM retrieval, except emission relationship between different frequencies of AMSR-E of both bare surface and vegetation exist. In this paper, a new method to retrieve SM for AMSR-E more accurately is proposed based on an emission simulation of a bare surface at C-, X- and Ku-band. The unknown variables elimination in retrieval is by finding the relationships of single scattering albedo (and optical opacity) of vegetation scatter between the same frequency ranges.

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

This study demonstrates the capability of estimating soil moisture using repeat-pass L-band radiometer. It shows (1) evaluation of the effects of the surface roughness and vegetation in the repeat-pass measurements and (2) development of a technique to estimate soil moisture.

The study of snow has become an important area of research in the natural sciences, particularly in hydrology and climatology. This study shows a concept of estimating snow water equivalence under the consideration of a dual frequency L- and Ku-band polarization system.

The research has been done to derive the soil moisture information at local scale by using single polarization, single frequency sensors such as ERS-1/2, Radarsat, and JERS-1. There is a need to develop a technique to estimate soil moisture information from these currently available data sources at both regional and local scales. In this study, a soil moisture change detection algorithm was developed for using the multi-temporal 50m resolution Radarsat SCANSAR image data. It was based on the theory model analysis results, with the correction of vegetation and incident angle effects. The relative soil moisture change value can be derived. The results were compared with in-situ measured soil moisture data from 3 different sampling sites at study area. The validation indicated our algorithm with RMSE error of 0.44 in estimating soil moisture change ratio.

Remote sensing of snow depth is of primary importance for accurate prediction of snowmelt runoff. In this study, we carried out the numerical simulations to evaluate the subsurface effects, including surface roughness and dielectric properties, on the brightness temperature differences of snow covered terrain at AMSR-E frequencies. We found that the ground dielectric constant and the surface roughness parameters such as RMS height and correlation length have a significant effect on the brightness temperature differences at AMSR-E frequencies. We will demonstrate the effects of the subsurface conditions on the brightness temperature differences and on the snow depth retrieval algorithm. We will also demonstrate the importance of each emission component at different frequency and polarizations under different snow and surface conditions.

A successful soil moisture algorithm using radar data must identify the soil moisture effect and the surface roughness dependence explicitly since rough surface scattering depends on both the roughness and the dielectric constant of an imaged surface. For bare surfaces, several algorithms have been developed to estimate soil moisture using polarimetric radar data. These algorithms were empirically derived from either experimental data or numerical data instead of starting from rough surface scattering theories. In this paper, we present a soil moisture algorithm theoretically derived using the tilted Bragg approximation. With appropriate approximations using the tilted Bragg theory, we have derived both co- and cross-polarization ratios. Then, a soil moisture algorithm is developed based on these two ratios. This new algorithm is compared with the existing empirical methods using input data from the IEM (Integral Equation Method) an experimental radar data. We also briefly discuss the effect of vegetation on this soil moisture algorithm.

The microwave remote sensing is a promising method for detecting the freezing and thawing of soil surface due to change of dielectric constant when frozen. In frozen soil, not all liquid water freezes at or below 0/spl deg/C. Based on the semi-empirical dielectric mixing model for soil water mixture, an extension of model has been made to describe the dielectric constant change of frozen soil as a function of temperature. An empirical function Wu=A/spl middot/|T-273.2|/sup -B/ has been used for estimating the fractions of liquid water and ice in frozen soil, where Wu is unfrozen water content. A and B are parameters related to soil texture, T is temperature in K. The item for calculation of dielectric constant of ice fraction is also added to the semi-empirical mixing model with extension of some related parameters adapted to temperature range below 0/spl deg/C. Thus, with obtaining of liquid water content and ice fraction, the dielectric constant of frozen soil may be estimated. The simulated data shows that the dielectric constant of soil has a sharp change while freezing or thawing occurring. The results could be used for analyzing the characteristics of emission and scattering of soil at microwave frequencies during freezing and thawing.

Many attempts have been made to retrieve soil surface parameters, such as soil moisture (SM), surface roughness parameters, by regressions or other statistical methods and some other techniques like neural networks (NNs) and genetic algorithms. The NN is proved to be an effective method for retrieval problems; much effort has been devoted to it. In this study, our goal is to estimate the bare surface soil moisture and surface roughness at Advanced Microwave Scanning Radiometer (AMSR/E) frequencies. First, a preliminary analysis was conducted based on a sensitivity analysis of surface parameters by simulating AMSR/E emissivity data of V, H polarizations, which were generated by the Integral Equation Model (IEM) for AMSR/E viewing angle of 55 degrees. We employed NNs to be first trained with part of the sensitive data determined by the above sensitivity analysis, then the trained NNs were used to retrieve the parameters that we need, especially soil moisture, from the simulated data. Analysis of the difference between the retrieved parameters and the simulated ones is presented. In addition, because the retrieval accuracy of NNs is supposed to be extremely sensitive to "noise" - the difference between the model and measurements, we introduced random noise to the simulated data. At the same time, we carried out a sensitive analysis of the input noise. We also selected the most sensitive frequencies, 6.9 and 10.7 GHz, to soil moisture in our retrieval scheme. This study demonstrates the great potential of NNs in estimating soil surface parameters from passive microwave remotely sensed data again.

This paper presents a model of microwave emissions from rough surfaces. We derive a more complete expression of the single-scattering terms in the integral equation method (IEM) surface scattering model. The complementary components for the scattered fields are rederived, based on the removal of a simplifying assumption in the spectral representation of Green's function. In addition, new but compact expressions for the complementary field coefficients can be obtained after quite lengthy mathematical manipulations. Three-dimensional Monte Carlo simulations of surface emission from Gaussian rough surfaces were used to examine the validity of the model. The results based on the new version (advanced IEM) indicate that significant improvements for emissivity prediction may be obtained for a wide range of roughness scales, in particular in the intermediate roughness regions. It is also shown that the original IEM produces larger errors that lead to tens of Kelvins in brightness temperature, which are unacceptable for passive remote sensing.

Based on the bistatic scattering theory a simulated database of bare soil surface emissivity with dual polarization is found for the configuration of AMSR using the Integral Equation Method (IEM). Parameters including soil moisture, correlation length and RMS height are mainly considered to evaluate the signature of the bare soil surface emissivity. Analysis has been done to investigate the relationship between the emissivity and the parameters. The results show that the influence of soil surface moisture and roughness parameters on the emissivity is quite complex. It is difficult to separate the coupled influence of the moisture and roughness factors through only statistical processing. An algorithm for estimation of soil surface moisture with AMSR at C-band is developed based on the method to extract a pair of functions corresponding to vertical and horizontal polarization and depending only on dielectric constant and incident angle. There is a good linear relation between the combinations of the extracted functions and the dual polarized emissivity. Hence, if we obtain the measurements of the dual polarized emissivity of a pixel the soil surface moisture can be estimated from the dielectric constant calculated from the statistical relations.

In this study, we evaluate the effect of the surface roughness on estimation of the relative soil moisture change in repeat-pass L-band radar measurements. It has found the surface roughness has a significant impact and a correction technique has been developed. I. INTRODUCTION During recent years, theoretical modeling and field experiments have established the fundamentals of active microwave remote sensing as an important tool in determining physical properties of soil. In attempt to use active microwave remote sensors in estimation of soil moisture, we are mainly facing two major problems: effects of surface roughness and vegetation cover. There are several algorithms developed for measurement of bare soil moisture quantitatively using dual or three polarization L-band SAR image data. A common idea beyond these algorithms is to separate the effects of the surface dielectric and roughness properties on the backscattering signals to present the model, which the inversion was based on, as a product of a dielectric function and a roughness function. They are first-order statistical inversion models. Depending on the data source, the selection of the surface roughness parameters and the backscattering measurements of the different polarizations or their linear combinations, the models have a great difference in terms of both the dielectric and roughness functions. The temporal variability of surface roughness is generally at much longer time scale than that of soil moisture, unless there was a human activity. Commonly, we can reasonably assume that the surface roughness is same at certain time interval. The change in SAR measurements between the repeat-passes, therefore, is resulted from the change of ground dielectric properties or soil moisture. Therefore, the repeat-pass measurements provide additional relative surface soil moisture change information and make it possible to directly estimate the relative moisture change and improving the accuracy of estimating the bare surface soil moisture. However, there has no quantitative algorithm being developed to estimate the relative soil moisture change using repeat-pass measurements. In this study, we evaluate 1) the effects of surface roughness in L-band repeat-pass measurements using IEM simulated data, 2) developing a quantitative algorithm to estimate relative soil moisture change, and 3) validating this technique with JPL/AIRSAR 92's experiment data over the little Washita test site.

This study demonstrates the capability of estimating the relative soil moisture change using repeat-pass L-band radiometer. It shows 1) evaluation of the effects of the surface roughness and vegetation in the repeat-pass measurements, 2) development of a technique to estimate the relative soil moisture change, and 3) validation with the ground soil moisture measurements from SGP99 experiment.

Two basic microwave approaches are used to measure soil moisture, one is passive which is based on radiometry and the other is active and uses radar. Both approaches utilize the large contrast between the dielectric constant of dry soil and water. A total backscattering amount for a vegetated surface include volume, surface, and surface-volume interaction scattering terms. The backscattering model here is based on without surface-volume interaction scattering terms. In attempt to use active microwave remote sensors in estimation of soil moisture, two major problems, effects of surface roughness and vegetation cover, are faced. For a given sensor, we assume the roughness under the condition of no change during data acquisitions. The main problem for retrieval of surface dielectric properties is separate the volume scattering item from total backscattering. With the time-serial soil moisture map from L band passive microwave radiometry, the Electronically Scanned Thinned Array Radiometer (ESTAR) at Southern Great Plains 1997 (SGP97), we calculated the surface reflectivity with 800 m resolution. The volume scattering items at 800 m resolution can be derived using multi-temporal resample calibration Radarsat SAR and surface reflectivity data. Weighting the ratio of NDVI at different resolution from NOAA/AVHRR and TM, the surface reflectivity change with 50 m resolution can be estimated according to the total backscattering and volume scattering, then soil moisture change be mapped at 50 m resolution.

The current MODIS snow-mapping algorithm uses a binary classification, i.e. a pixel will be identified either as snow or non-snow. A common problem with a binary classifier is the snow area changes a lot with the selection of the threshold value of pixel to be classified as snow. In this paper, we present a new way for 500 m MODIS fraction snow mapping based on the high resolution ASTER data. The ASTER and MODIS data are obtained simultaneously and have the similar illumination and sensor viewing geometry because the instruments are on the same platform. As to a series of temporal MODIS data, there is ASTER data capable of being the reference for the particular time period. First the MODIS and ASTER data received for the same date are co-registered with high accuracy, then each MODIS sub-pixel composition can be calculated using ASTER supervised classification results. The local snow, vegetation and rock end-members can be recognized from the MODIS data which is identified as the homogenous area with comparison to the classification result of the ASTER data. A group of end-members can be used for fraction snow mapping for the particular temporal MODIS data. In addition, snow library spectra is used for depressing the terrain effect and the bands selected for ASTER classification to avoid the problem of the sensor saturation.

A generalized power law spectrum is proposed to describe the random rough surfaces in this paper. The parameters of the spectrum are related to the traditional physical parameters of root mean square (rms) height and correlation length. It can naturally reduce to the spectra of Gaussian and exponential correlation functions. The corresponding correlation functions are also derived. It can provide wider range of spectra to describe the random rough surfaces than other spectra. Based on the proposed spectrum, backscattering of soil surfaces is studied by using the integral equation model (IEM). The simulation results are compared with the experimental measurements of real soil surfaces at L, C, and X bands for the different roughness scales and moisture conditions. The reasonably good agreements between the measurements and the simulations are observed for all three-frequency bands and different incidence angles with the same sets of the physical roughness parameters.

Soil moisture is an important parameter for hydrological and climatic investigations. Future satellite missions with L-band passive microwave radiometers will significantly increase the capability of monitoring Earth's soil moisture globally. Understanding the effects of surface roughness on microwave emission and developing quantitative bare-surface soil moisture retrieval algorithms is one of the essential components in many applications of geophysical properties in the complex Earth terrain by microwave remote sensing. We explore the use of the integral equation model (IEM) for modeling microwave emission. This model was validated using a three-dimensional Monte Carlo model. The results indicate that the IEM model can be used to simulate the surface emission quite well for a wide range of surface roughness conditions with high confidence. Several important characteristics of the effects of surface roughness on radiometer emission signals at L-band 1.4 GHz that have not been adequately addressed in the current semiempirical surface effective reflectivity models are demonstrated by using IEM-simulated data. Using an IEM-simulated database for a wide range of surface soil moisture and roughness properties, we developed a parameterized surface effective reflectivity model with three typically used correlation functions and an inversion model that puts different weights on the polarization measurements to minimize surface roughness effects and to estimate the surface dielectric properties directly from dual-polarization measurements. The inversion technique was validated with four years (1979-1982) of ground microwave radiometer experiment data over several bare-surface test sites at Beltsville, Maryland. The accuracies in random-mean-square error are within or about 3% for incidence angles from 20/spl deg/ to 50/spl deg/.

In the development of wave scattering models for randomly dielectric rough surfaces, it is usually assumed that the Fresnel reflection coefficients could be approximately evaluated at either the incident angle or the specular angle. However, these two considerations are only applicable to their respective regions of validity. A common question to ask is what are the conditions under which we would choose one or the other of these two approximations? Since these approximations are basically roughness-dependent, how can we handle the in-between cases where neither is appropriate? In this paper, a physical-based transition function that naturally connects these two approximations is proposed. The like-polarized backscattering coefficients are evaluated with the model and are compared with those calculated with a moment method simulation for both Gaussian and non-Gaussian correlated surfaces. It is found that the proposed transition function provides an excellent prediction for the backscattering coefficient in the frequency and angle trends.

The simulations of emissivities from a two-dimensional (2D) wet soil with random rough surfaces are studied with numerical solutions of three-dimensional (3D) Maxwell equations. The wet soils have large permittivity. For media with large permittivities, the surface fields can have large spatial variations on the surface. Thus, a dense discretization of the surface is required to implement the method of moment (MoM) for the surface integral equations. Such a dense discretization is also required to ensure that the emissivity can be calculated to the required accuracy of 0.01 for passive remote sensing applications. It has been shown that the physics-based two-grid method (PBTG) can efficiently compute the accurate surface fields on the dense grid. In this paper, the numerical results are calculated by using the PBTG in conjunction with the sparse-matrix canonical grid method (SMCG). The emissivities are illustrated for random rough surfaces with Gaussian spectrum for different soil moisture conditions. The results are calculated for L- and C-bands using the same physical roughness parameters. The numerical solutions of Maxwell's equations are also compared with the popular H and Q empirical model.

Algorithms for estimating dry snow density and the dielectric constant and roughness of the underlying soil or rock use backscattering measurements with VV and HH polarization at L-band frequency (1.25 GHz). Comparison with field measurements of snow density during the first SIR-C/X-SAR overpass shows absolute accuracy of 42 kg m/sup -3/ (13% relative error). For the underlying soil, comparisons with the ground scatterometer measurements showed errors of 4% by volume for soil moisture estimation and 4 mm for the surface root mean square (RMS) height. Values of snow density and the properties of the underlying soil are necessary for the estimation of snow water equivalence.

For pt.I see ibid., vol.38, no.6, p.2465-74 (2000). The relationship between snow water equivalence (SWE) and SAR backscattering coefficients at C- and X-band (5.5 and 9.6 GHz) can be either positive or negative. Therefore, discovery of the relationship with an empirical approach is unrealistic. Instead, the authors estimate snow depth and particle size using SIR-C/X-SAR imagery from a physically-based first order backscattering model through analyses of the importance of each scattering term and its sensitivity to snow properties. Using numerically simulated backscattering values, the authors develop semi-empirical models for characterizing the snow-ground interaction terms, the relationships between the ground surface backscattering components, and the snowpack extinction properties at C-band and X-band. With these relationships, snow depth and optical equivalent grain size can be estimated from SIR-C/X-SAR measurements. Validation using three SIR-C/X-SAR images shows that the algorithm performs usefully for incidence angles greater than 300, with root mean square errors (RMSEs) of 34 cm and 0.27 mm for estimating snow depth and ice optical equivalent particle radius, respectively.

An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.

Electromagnetic wave scattering in dense media, such as snow, depends on the three-dimensional (3D) pair distribution function of particle positions. In snow, two-dimensional (2D) stereological data can be obtained by analyzing planar sections. In this paper the authors calculate the volume 3D pair distribution functions from the 2D stereological data by solving Hanisch's integral equation. They first use Monte Carlo simulations for multisize particles to verify the procedure. Next they apply the procedure to available planar snow sections. A log-normal distribution of particle sizes is assumed for the ice grains in snow. To derive multisize pair functions, a least squares fit is used to recover pair functions for particles with sufficient number density and the hole correction approximation is assumed for the larger particles. A family of 3D pair distribution functions are derived. These are then substituted into dense media scattering theory to calculate scattering. It is found that the computed scattering rates are comparable to those calculated under the Percus-Yevick approximation of pair distribution functions of multiple sizes.

In hydrological investigations, modeling and forecasting of snow melt runoff require timely information about spatial variability of snow properties, among them the liquid water content-snow wetness-in the top layer of a snow pack. The authors' polarimetric model shows that scattering mechanisms control the relationship between snow wetness and the copolarization signals in data from a multi-parameter synthetic aperture radar. Along with snow wetness, the surface roughness and local incidence angle also affect the copolarization signals, making them either larger or smaller depending on the snow parameters, surface roughness, and incidence angle. The authors base their algorithm for retrieving snow wetness from SIR-C/X-SAR on a first-order scattering model that includes both surface and volume scattering. It is applicable for incidence angles from 25/spl deg/-70/spl deg/ and for surface roughness with rms height /spl les/7 mm and correlation length /spl les/25 cm. Comparison with ground measurements showed that the absolute error in snow wetness inferred from the imagery was within 2.5% at 95% confidence interval. Typically the free liquid water content of snow ranges from 0% to 15% by volume. The authors conclude that a C-band polarimetric SAR can provide useful estimates of the wetness of the top layers of seasonal snow packs.>

Active microwave sensors can discriminate snow from other surfaces in all weather conditions, and their spatial resolution is compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR in the Otztal Alps in 1989 and 1991, the authors examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. By comparing polarimetric SAR data to images from the Landsat Thematic Mapper obtained under clear conditions one week after the SAR flight, the authors found that SAR data at 5.3 GHz (C-band) can discriminate between areas covered by snow from those that are ice-free. However, they are less suited to discrimination of glacier ice from snow and rock. The overall pixel-by-pixel accuracies-74% from VV polarization alone with topographic information, 76% from polarimetric SAR without any topographic information, and 79% from polarimetric SAR with topographic information-are high enough to justify the use of SAR as the data source in areas that are too cloud-covered to obtain data from the Thematic Mapper. This is especially true for snow discrimination, where accuracies exceed 80%, because mapping of a transient snow cover during a cloudy melt season is often difficult with an optical sensor. The AIRSAR survey was carried out in summer during a heavy rainstorm, when the snow surfaces were unusually rough. Even better results for snow discrimination can be expected for mapping in the spring, when snow is usually smoother.>

During normal synthetic aperture radar (SAR) processing, a flat Earth is assumed when performing radiometric corrections such as antenna pattern and scattering area removal. The authors examine the effects of topographic variations on these corrections. Local slopes will cause the actual scattering area to be different from that calculated using the flat Earth assumption. It is shown that this effect may easily cause calibration errors larger than a decibel. Ignoring the topography during antenna pattern removal may also introduce errors of several decibels in the case of airborne systems. The effect of topography on antenna pattern removal is expected to be negligible for spaceborne SARs. The authors show how these effects can be taken into account if a digital elevation model is available for the imaged area. The errors are quantified for two different types of terrain, a moderate relief area near Tombstone, AZ, and a high relief area near Oetztal in the Austrian Alps. The authors show errors for two well-known radar systems, the C-band ERS-1 spaceborne radar system and the three frequency NASA/JPL airborne SAR system (AIRSAR).>