Accurate, high spatial-resolution snow depth is critical for regional hydrological modeling and water resource management, yet most long-term, large-scale products are limited to coarse resolutions (10–25 km), undermining their accuracy and utility. Particularly in mountainous regions, sparse station networks coupled with high spatial heterogeneity severely impede robust downscaling. To bridge this gap, we propose a self-adaptive geographically weighted regression model (SGWR) that downscales coarse snow depth products to 1 km without reliance on in-situ station data. SGWR dynamically adapts its parameters to local terrain and snow-cover characteristics, making it especially effective in mountainous regions with complex topography and fragmented snowfields. We demonstrate its performance over high-altitude areas in Xinjiang and neighboring Kazakhstan—regions characterized by deep snow and strong terrain heterogeneity, and evaluate it against four independent snow depth datasets, including ERA5-Land, Fengyun-3 (FY-3), spectral polarization difference (SPD) and machine-learning (ML) products. In the value-consistency assessment, SGWR preserves original data fidelity, achieving an R² of 0.95 for the ML product compared with values of 0.87 and 0.57 from two benchmark methods (the pixel-based time series and full-domain methods, respectively). Station-based validation yields an RMSE of 4.43 cm and R² of 0.82, outperforming the benchmark methods (with RMSEs of 5.99–8.19 cm and R² of 0.38–0.67). These results highlight SGWR’s capacity to produce accurate, high-resolution snow depth maps, particularly in data-scarce and topographically complex terrain. This offers a powerful tool for more reliable hydrological forecasts and informed water management.

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.

An accurate microwave emissions model is essential for the simulating satellite brightness temperature (TB) and developing snow depth retrieval algorithm. The dense media radiative transfer theory based on quasicrystalline approximation (DMRT-QCA) model and the DMRT with bicontinuous (DMRT-Bic) model are currently recognized as representative and highly accurate snow emission models. Meanwhile, the latest development of DMRT-Bic-NN (DMRT-Bic-neural network) model has improved computational efficiency based on DMRT-Bic model. In order to evaluate the performance of the above models in the simulation of brightness temperature, this study compares and validates the capabilities of DMRT-Bic-NN model and DMRT-QCA model to simulate TB at 18.7 GHz and 36.5 GHz with the same input parameters. The results show that the correlation coefficient (R) between the TB simulations of DMRT-Bic-NN and DMRT-QCA is higher at 18.7 GHz than at 36.5 GHz. Moreover, the validation of TB simulations by ground-based observations in the Altay region shows that the DMRT-Bic-NN model has higher simulation accuracy than DMRT-QCA model, and the R / RMSE between the DMRT-Bic-NN simulations and the ground-based microwave radiometer observations TB is 0.35~0.87, 20.46~18.17K at 18.7 GHz and 36.5 GHz with V polarization, respectively. However, during the snow melting season, DMRT-QCA exhibits higher simulation accuracy than DMRT-Bic-NN at 36.5 GHz. This work can provide important guidance for the snow depth retrieval.

One of the main features of the Asian Water Tower imbalance is the massive melting of snow, necessitating enhanced snow monitoring. However, the sensors aboard polar-orbiting satellites, such as MODIS, yield only one to two valid observations daily. This, coupled with the extensive cloud cover and prolonged duration over the Asian Water Tower, results in a significant number of data gaps. China's new generation of geostationary satellites FY-4A and FY-4B have high frequency observations, making it possible to monitor snow with high precision. In this study, we systematically analyze the image pixel size stretching of FY-4A and FY-4B in the Asian Water Tower region, based on their imaging geometry. This analysis offers a theoretical foundation for fractional snow cover retrieval in the subsequent integration of these two satellites. Concurrently, this study conducts fractional snow cover (FSC) retrieval for FY-4A and FY-4B, utilizing the multiple endmember spectral mixture analysis algorithm with automatic endmember extraction (MESMA-AGE). High spatial resolution Landsat-8 imagery serves as reference data for accuracy assessment. The results indicated that FY-4A's retrieval accuracy remained unaffected by pixel size stretching, achieving an Overall Accuracy (OA) of up to 0.97 and a Root Mean Square Error (RMSE) of less than 0.13. For FY-4B, the retrieval accuracy demonstrated higher snow identification precision, with an OA of up to 0.95. However, the RMSE varied significantly due to pixel size stretching, ranging from 0.12 to 0.21. FY-4A and FY-4B fusion enables high precision and near-real-time 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.

The frequency and intensity of droughts have increased rapidly up to now and will become more severe in the future. To better characterize the impacts of drought on vegetation productivity over large scales by remote-sensing-driven models, we should understand the responses of vegetation to historical drought events. In this study, the responses of vegetation gross primary productivity (GPP) to the droughts in 2004 and 2015 over tropical Asia were analyzed along with hydrometeorological data and compared with data-driven models. We found that the GPP anomalies in data-driven models are negative in both drought events. However, the simulation of light-use efficiency (LUE) models revealed the GPP anomalies are negative in 2004, while positive in 2015. We discussed its possible causation. A key factor for LUE models is how they represent the effect of water stresses on GPP, where soil moisture (SM) is an indicator that largely differed from other variables, e.g. saturated vapor pressure deficit and land surface water index in characterizing water stresses. The anomalies of SM are obviously different in 2004 and 2015, thus, the representation of SM stress could a crucial factor for improving the LUE models to better simulated GPP in characterizing responses of vegetation GPP to droughts over large scales.

Snow water equivalent (SWE) quantitatively describes water storage in snowpack. Satellite-based passive microwave (PMW) remote sensing is a valid tool for monitoring SWE in the Northern Hemisphere. However, the current operational SWE retrieval methods, especially those without assimilating near real-time station snow depth, still utilize globally-constant coefficients to build regression-based retrieval algorithms. In the context of the successful launch of the FY-3F satellites in 2023, we are attempting to work out a better Northern Hemisphere algorithm for the Micro-Wave Radiation Imager-II (FY-3F/MWRI-II), using a set of pixel-sensitive dynamic coefficients regressed based on a spatiotemporally continuous reference SWE dataset. We first utilize a method that couples random forest with the HUT snow emission model to calculate a high-accuracy SWE reference dataset. Then the linear-regression equations are used to fit the reference SWE data and satellite brightness temperature observations at each pixel to build the new operational FY-3F algorithm. Finally, the proposed FY-3F algorithm is validated extensively using four spatially independent datasets. The proposed FY-3F algorithm will improve the global monitoring capabilities for snow cover and enhance a complete and timely understanding of changes in SWE.

Accurate estimation of snow cover is vital for understanding climate dynamics and hydrological processes in cold regions. Recent advances in data fusion techniques have enabled the possibility of cloud-free, high-frequency snow observations by integrating data with high spatial and temporal resolution from multiple sensors. This study focuses on determining the optimal fusion days essential for accurately capturing the rapidly changing surface conditions of snow cover. We conducted a daily assessment of sensor coverage capabilities across various time-span windows, taking into account seasonal and spatial variations, and the impact of mixing high- and low-resolution sensor data on fusion accuracy and consistency. This analysis, spanning from April 2013 to September 2022, utilized extensive data from Landsat 9 OLI-2, Landsat 8 OLI, Sentinel-2A/B MSI, Terra/Aqua MODIS, and S-NPP VIIRS across the Qinghai-Tibet Plateau. Our findings indicate that a 4-day window is optimal, achieving over 90% data availability, where high-resolution data surpasses the availability of low-resolution data. Notably, optimal fusion days exhibit seasonal variations, necessitating longer fusion windows, up to 6 days or more, during summer months. Additionally, this study examined the geospatial factors influencing the optimal fusion days. This research offers a robust framework for data fusion in monitoring rapidly changing snow cover, providing valuable insights for related environmental and climatic studies.

This study investigated the indispensable role of Synthetic Aperture Radar (SAR) in the monitoring of aquatic ecological anomaly events. By exploring various Geophysical Model Functions (GMFs) at different frequencies and angles, this work indicates SAR’s pivotal significance in detecting and characterizing surface roughness changes associated with aquatic ecological anomaly events. Comparative analysis of GMFs simulation at L-band, C-band, X-band, and Ku-band demonstrates that the backscattering coefficient both at VV and HH polarizations are crucial parameters for detecting aquatic ecological anomaly events, with higher-frequency bands being more suitable for monitoring such events. This information can provide valuable insights for configuring Synthetic Aperture Radar (SAR) systems.

The freeze/thaw (F/T) transition of soil significantly affects water, energy, and carbon cycles at the land-atmosphere interface. The volumetric structure and vertical heterogeneity within the soil become apparent after soil freezing. This complicates the microwave radiative transfer process of frozen soil at different frequencies. In this study, a radiation transfer model, called SFS_DMRT, considering the volume scattering effects of seasonally frozen soil, is proposed based on dense media radiative transfer (DMRT) theory and the Mie spherical scattering model. The multiple scattering among discrete frozen soil clods is considered. This newly developed SFS_DMRT model is validated against ground radiometer measurements and compared with the advanced integral equation model (AIEM), a surface-scattering model, at three different experimental sites. Results show that in Sodankylä, where the soil is in a stable frozen state, the brightness temperature (Tb) simulated by SFS_DMRT has a higher agreement with observed Tb than that of AIEM. The emission of frozen soil is, moreover, better described by AIEM when the soil is undergoing diurnal F/T cycles in A’rou, in which the soil may freeze overnight and then thaw the next day. The Tb dependence on frequency was further examined, and results show that when simulating the passive microwave signature from the soil in a stable frozen state, which means the soil does not undergo intraday or diurnal F/T cycles, volume scattering effects can be ignored at the L-band; it should, however, be taken into consideration at Ku- and Ka-bands. The degree of volume scattering effects at C- and X-bands depends on the effective grain size of soil clods. The soil frost depth and microwave band penetration depth influence the attenuation of emissions from deeper unfrozen soil. The SFS_DMRT model developed in this study is vital for understanding the passive microwave signatures from frozen soil and can be used to obtain stratified profile information in layered soil.

Satellite passive microwave (PMW) remote sensing is widely used for monitoring the snow water equivalent (SWE) in the Northern Hemisphere. Existing operational SWE retrieval methods, especially those without assimilating ground-based snow depth priors, still utilize globally constant coefficients to construct regression-based retrieval algorithms. The current Fengyun-3 (FY-3) series of SWE product algorithms has made improvements in China, where biases have been significantly reduced locally but not in other regions. Within the context of the successful launch of the FY-3F satellites in 2023, we developed a better Northern Hemisphere algorithm for the Microwave Radiation Imager-II (FY-3F/MWRI-II) using pixel-sensitive coefficients regressed on a reference SWE dataset. We utilized the random forest model coupled with the snow emission model (HUT-RF) to obtain a high-accuracy SWE reference dataset. Then, we employed linear regression equations to fit the reference HUT-RF dataset at each pixel to construct the new operational FY-3F algorithms. We innovatively introduced the brightness temperature differences between 18.7 and 89 GHz and the polarization differences at 10.65 GHz in the regression after noting their sensitivity in deep snow estimation. The proposed FY-3F algorithm was extensively validated via four spatially independent datasets. The results demonstrated that the proposed FY-3F algorithm performed well in non-mountainous and sparsely forested areas, e.g., the overall unbiased root mean square error (unRMSE) values were 27.15 mm over Russia and 13.70 mm over China. High uncertainties still occurred in complex terrains and densely forested areas, e.g., the overall unRMSE values were 75.30 mm over Canada and 129.06 mm over western North America. The proposed FY-3F algorithm could improve global snow cover monitoring capabilities and enhance the complete and timely understanding of SWE changes.

Accurate simulation of the microwave signatures of snow using the emission models is of guiding significance to develop the snow parameters retrieval algorithm. This study based on reanalysis dataset ERA5-Land and auxiliary data to investigate the potential of the DMRT model combined with the τ –ω model for simulating passive microwave brightness temperature (TB) of snow cover at 10.65 GHz, 18.7 GHz, and 36.5 GHz. The results showed that the correlation coefficient (R) and bias between the simulations and the ground-based microwave radiometer observations at the Altay is 0.45~0.66, 8.21 k~13.3K at V polarization, and 0.44~0.63, 9.32K~14.68K at H polarization, respectively. In addition, the R and bias between the simulations and the AMSR2 and FY-3D TB is 0.61-0.81, 0.58~0.73, and 18.2K~20.75K, 18.25K~19.2K at V polarization, and 0.47~0.65, 0.51~0.69, and 19.79K~28.62K, 20.52K~26.8K at H polarization, respectively. In some forested areas, there is a significant increase in the simulation bias at 36 GHz, which could be attributed to an overestimation of vegetation influence at this frequency.

The detailed satellite mapping of seasonal snow cover, with many spectral bands from High-resolution remote sensors, is largely investigated recently. Snow cover maps can be extracted from optical data using relatively simple approaches given the difference in spectral reflectance. However, some high-resolution images of illuminated snow-covered surfaces suffered from limited detector saturation due to overexposure. Also, these detailed images involve large data volumes that prohibit complex analysis. This study developed and automated four algorithms, including Random Forest Model (RF), Maximum Likelihood Classifier (MLC), the Threshold method based on Water-resistant Snow Index (WSI), and the Blue Snow Threshold method (BST) respectively, for discriminating snow from other surface types with Gaofen-1 optical satellite images. To reduce the impact of overexposure on the extraction of snow-cover areas, a simple cross-calibration methodology has been utilized before estimation. Image pairs from the Operational Land Imager (OLI) on Landsat-8 and Wide Field of View (WFV) on Gaofen-1 were used to verify the radiometric calibration of WFV with respect to the well-calibrated OLI sensor. Different algorithms' performance was tested by utilizing Gaofen-2 Multi-Spectral (PMS) sensor data for validation. RF-derived estimates of Snow Cover Areas (SCA) did better in terms of overall accuracy. Due to simplicity and efficiency, these algorithms have the potential to be used to develop high spatiotemporal resolution maps of SCA.

Cloud detection is the first step in remote sensing surface parameter retrieval. Due to the similar spectral properties of cloud and snow, cloud products commonly used in snow monitoring sensors have a certain degree of cloud and snow misjudgment problem. With the launch of a new generation of geostationary satellite (such as China's FY-4A), its time resolution is 15 minutes, and high-frequency observations make accurate cloud and snow identification possible. This study utilizes the high-frequency and multispectral observation characteristics of FY4A, combining the multi band threshold method with deep learning algorithm, to fully explore the spectral and texture differences of cloud and snow, as well as the characteristics of rapid cloud changes, and achieve high-precision cloud detection. Then, the CALIPSO data is used to evaluate the accuracy of the new algorithm's detection results. From the results, the new algorithm for cloud and snow recognition is more accurate and consistent with CALIPSO observations. In terms of specific accuracy indicators, the cloud hit rate(CHR) increase 1.07% and the false alarm rate(FAR) decrease 5.15%. At the same time, both in terms of single scene or daily composite results, the proportion of cloud cover has decreased about 20%, and the proportion of snow cover can increase by up to about 15%. This laid a solid foundation for high-precision fractional snow cover retrieval and spatiotemporal reconstruction in the future.

Passive microwave (PMW) snow depth (SD) products currently face significant uncertainty due to their coarse spatial resolution, and existing downscaling algorithms that utilize optical data often encounter issues such as signal saturation and a lack of a foundational physical mechanism. We proposed a novel downscaling algorithm: Linear Unmixing-based Snow Depth Downscaling (LUSDD), which leverages fractional landcover, obtained from daily seamless optical remote sensing, to derive brightness temperature (BT) endmembers of different landcover classes. This facilitates the reconstruction of BT at 1-km spatial resolution, subsequently enabling the retrieval of downscaled SD through the 1-km BT. Validations conducted at 53 ground SD stations demonstrate that the LUSDD algorithm significantly enhances both the spatial resolution and the accuracy of the original FY-3D SD product, with its root-mean-square error (RMSE) reduced from 4.60 cm of the original product to 2.56 cm. As an efficient and reliable method capable of downscaling SD data to a moderate spatial resolution of 1-km, the LUSDD algorithm is valuable to meet the needs of applications requiring higher spatial resolution.

High spatial resolution soil moisture content (SMC) is of great significance in exploring the applications of ecological environmental protection, hydrological process prediction and agricultural resource management in the Tibetan Plateau. Sentinel-1 C-band synthetic aperture radar (SAR) provides an effective way for soil moisture retrieval with high spatial resolution. Here, based on the powerful online data processing Google Earth Engine (GEE) platform, a random forest (RF) SMC retrieval algorithm was proposed by combined model simulation through integrating the water cloud model for vegetation backscattering, the advanced integral equation model (AIEM), and the Oh model in this work. The results of the soil moisture retrieval from Sentinel-1 SAR show a correlation coefficient of 0.846 and an RMSE of 0.050 cm3/cm3, when compared with ground measurements in Naqu, Maqu and Ngari observation networks. The spatiotemporal pattern of RF predicted SSM was compared with the SMAP L2 Radiometer/Radar SMC Product soil moisture product. The results indicate that the RF SSM captures the spatial distribution and the daily variability in the Qinghai-Tibetan Plateau (QTP).

The normalized difference snow index (NDSI) is a fundamental spectral indicator of snow/ice in visible and shortwave-infrared imagery. The complex grain shapes in nature have well-known significant effects on the single-scattering properties (SSPs) and subsequently the bidirectional reflectance of snow. The shape effects on snow NDSI need to be further characterized as NDSI is a nonlinear combination of two reflectance bands. Considering the common snow grain shapes represented by sphere, spheroid, hexagonal plate, and Koch snowflake, we use the ray-tracing approach to simulate the SSPs of ice particles and the discrete ordinate algorithm to solve the bidirectional reflectance function and calculate NDSI of snow. According to simulating results, the angular pattern of snow NDSI is subject to snow grain shape, whereas the shape effects can be significantly weakened by the increasing surface roughness of ice particles. The shape of Koch snowflake causes an NDSI habit different from other three shapes for large snow grain size. Moreover, snow NDSI also has complex responses to aspect ratio (AR) for spheroid and hexagonal prism. The theoretical characterization of the snow NDSI responses to various grain shapes would enrich the knowledge of NDSI variation mechanism in snow-covered area mapping applications.

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.

Advanced Very High Resolution Radiometer(AVHRR) has accumulated nearly 40 years of data, making it essential for long time series snow monitoring. However, the majority of AVHRR snow algorithms and products are binary forms, which can have a significant impact on hydrological process simulations and estimates of water and energy cycles. Based on AVHRR data, three fractional snow cover retrieval algorithms are implemented in this study: the multiple endmember spectral mixture analysis algorithm based on automatic endmember extraction (MEAMA-AGE), the Snow Index method and the Snow/no-Snow Two-endmember model algorithm. In order to compare and verify the accuracy of the three fractional snow cover retrieval algorithms, we use the high spatial resolution Landsat-8 image snow cover retrieval results as the “ground truth”. The results show that all three algorithms can effectively retrieve fractional snow cover from AVHRR data, among which the Snow Index method and the Two-endmember model algorithm have higher accuracy, while the MESMA-AGE algorithm has lower precision due to the influence of the endmembers representation.

Fractional snow cover (FSC) mapping with high spatiotemporal resolution is of great significance to the study of surface hydrological processes, agricultural irrigation, and disaster monitoring. In this study, we use the spectral mixture analysis based on automatic endmember extraction (MESMA-AGE) algorithm to retrieve FSC from Landsat-5/7/8, Sentinel-2, and MODIS data using the Google Earth Engine (GEE) platform. The algorithm can produce FSC product with 30m spatial resolution and 4-day temporal resolution at regional scale, even globally with GEE. The result shows that the accuracy of the FSC product is high with the root mean square error (RMSE) being 0.18 with the comparison of Gaofen-2 imageries.

Synthetic aperture radar (SAR) sensors, such as Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1, provide significant opportunities for soil moisture content (SMC) retrieval with relatively high spatial resolutions (10~30 m). In this work, an artificial neural network (ANN) SMC retrieval algorithm combined with the water cloud model, the advanced integral equation model, and the Oh model database was proposed. The SAR copolarization backscatter, the local incidence angle (LIA), and the normalized difference vegetation index were used in input vectors for the ANN algorithm for the retrieval and mapping of the ALOS-2 and Sentinel-1 SMC at a 30-m resolution. The results of the comparison between the SMC retrievals and the measured SMC show that Sentinel-1 and ALOS-2 SMC retrievals with high accuracy correspond to low-vegetation areas (crop, grass, and shrub), with a root mean square error (RMSE) of 0.021 and 0.033 cm3/cm3, respectively. ALOS-2 SMC retrievals provide higher accuracy (RMSE = 0.076 cm3/cm3) than Sentinel-1 SMC retrievals at high vegetation (e.g., forest). However, it remains challenging for soil moisture retrieval in forest land. The C-band and L-band SMC retrievals have higher RMSE (up to 0.047 cm3/cm3) at low incidence angle (50°). In addition, by considering the impact of rainfall on the SMC, it appears that the Sentinel-1 and ALOS-2 SMC have a good response to the rainfall events. Finally, the results of the comparison between the SMC retrievals and the Soil Moisture Active Passive (SMAP) L2 SMC product show that the correlation coefficients between Sentinel-1, ALOS-2, and SMAP are higher in September when the vegetation is drying than in July when the vegetation is growing.

The spatial extent and duration of soil freeze/thaw (F/T) control water and heat exchange, the energy cycle, and climate change. Global warming causes permafrost thawing, which increases carbon emissions and in turn exacerbates climate change. Passive microwave remote sensing has been proven to be effective in monitoring land surface F/T. However, it was found that the applicability of existing passive microwave remote sensing-retrieved F/T products in large-scale areas (such as the Qinghai-Tibetan Plateau (QTP)) was influenced by some landscape factors, such as the arid climate type and terrain elevation gradient. FY-3 series satellites have accumulated nearly 10 years of passive microwave data, but there is little work based on FY-3 passive microwave data to see its potential in land surface F/T status monitoring. In this work, we proposed a dynamic method to determine the surface F/T status by combining the edge detection method and discriminant function algorithm from FY-3B X-and Ka-band microwave radiation imager (MWRI) data. Comparing the results against three F/T products based on in situ 5 cm soil temperature, we demonstrate that this algorithm performs best over different validation areas with an overall accuracy of 86.5%. More specifically, the new algorithm improved the accuracy of current F/T products in arid and semiarid regions from 73% to 90%. Additionally, the spatial distribution of frozen days over the QTP of 2018 based on the new algorithm has good consistency with the permafrost map. However, the accuracy is influenced by snowmelt and appears to be overestimated for thaw soil during the day. This algorithm performs well in QTP areas with complex topography and climate types and holds the promise of providing users with highly accurate F/T products on larger and even global scales.

The normalized difference snow index (NDSI) plays an important role in mapping snow cover with spaceborne visible and shortwave-infrared imagery. The NDSI variation depends on illuminating-viewing geometry and snow physical properties, including equivalent grain size (EGS), snow depth (SD) and impurity concentration, as well as fractional snow cover (FSC) within a mixed pixel; however, it is still not fully understood. To quantifiably characterize the pattern of snow NDSI variation, we use a light scattering model of snow to calculate bidirectional reflectance and consequent NDSI values for a wide range of illuminating-viewing geometries, SD, and EGS values. In the model designated bicontinuous snow model using Geometric Optics theory and Radiative Transfer Equation (bicontinuous-GO/RTE), snowpack is represented by a bicontinuous microstructure, and bidirectional reflectance is simulated based on geometric optics and vector radiative transfer equation. The discrete ordinates radiative transfer (DISORT) algorithm is used to simulate the soot concentration effect on snow NDSI. A soil spectral reflectance model (SOILSPECT) is utilized with the assumption of the linear spectral mixture of snow and soil to quantify the effect of FSC on NDSI. As for discontinuous forests, an analytical hybrid geometric-optical and radiative transfer (GORT) model in conjunction with the bicontinuous-GO/RTE model and the PROpriétésSPECTrales (PROSPECT) model is used to examine the effect of canopy cover, which is related to the maximal FSC viewable to satellites. Modeling results indicate that: 1) snow NDSI is comparably low at off-nadir viewing angles, and this effect is exacerbated by the decline in solar elevation but limited by large EGS; 2) snow NDSI increases with EGS yet becomes saturated at EGS of 500$\mu \text{m}$; 3) the effect of SD that is as low as 1 cm on NDSI is rarely noticeable; 4) the concentration of internally mixed soot up to 1 ppm has little reducing effect on snow NDSI ($> 20^{\circ }$in forests; and 8) forests complicate the nonlinear relationship between NDSI and canopy cover with fully snow-covered ground beneath canopies. These findings imply important uncertainty sources of binary and FSC mapping with NDSI.

Understanding the spatial variability and temporal stability of soil moisture can optimize the layout of ground monitoring, which is of great significance for validation of coarse resolution remote sensing soil moisture products and regional scale water resources management. In this paper, we took a forest-steppe ecotone zone in Genhe watershed as the study area, and analyzed the temporal stability of the Genhe Watershed Observation Network. Based on the time-stable characteristics of soil moisture within monitoring network, we discussed which sites were time-stable, and investigated the representative sites which could estimate watershed and SMAP L3_SM_P products pixel scale average soil moisture. Our work provided a scientific basis for the monitoring and management of soil moisture in Genhe watershed, and it would also be helpful for determining the spatial and temporal representative site in other regional or satellite scales.

Spatiotemporal continuous fractional snow cover(FSC) dataset is needed as an important input for the study of large-scale hydrological, meteorological and climate research. But optical data often have gap due to cloud cover. The widely used Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover dataset uses an eight-day composite approach to remove cloud effects, but it cannot meet the requirement of monitoring snow cover, a parameter with high temporal and spatial variability. This paper uses multiple endmember spectral mixture analysis (MESMA) algorithm to retrieve FSC from geostationary satellite (FY-4A and Himawari-8) and polar-orbiting satellite (MODIS) data; Then geostationary satellite retrieve results are used to fill the MODIS cloud cover pixels, which can reduce the cloud cover from 50% to 15%; Finally, the daily FSC product is obtained by using Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) algorithm. Landsat-8/OLI data processed by the MESMA algorithm is determined as “ground truth” to validate this product. The result shows that the accuracy of the daily cloud-free FSC product is high with the root mean square error is 0.1-0.15.

Many algorithms and products for snow cover have been developed. We built a high spatial and temporal resolution validation data set, which can be used as a unified standard for the validation of existing snow products. In recent years, Sentinel-2 MSI has been widely used because of its higher spatial and temporal resolution. In this study, we applied the linear spectral mixture analysis to Sentinel-2 MSI to obtain the fractional snow cover products, then we explored whether the Sentinel-2 MSI FSC products can meet the accuracy requirements with GF-2 data. The results show that the accuracy of Sentinel-2 MSI FSC can meet the requirements. Finally, we use Sentinel-2 MSI FSC to validate the MODAGE product.

The inversion of snow properties with optical remote sensing often relies on snow reflectance modelling. In this study, snow reflectance models including Mie-DISORT, Mie-Mishchenko and ART and the model based on bicontinuous microstructure are evaluated and compared with in situ measurements. Results show the bicontinuous microstructure based reflectance model can accurately simulate spectral albedo of clean and sooty snow, and have best performance on directional reflectance of snow surface compared with other three models.

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.

High-resolution soil moisture dataset is crucial for various application such as meteorology, climatology, hydrology and agriculture. Active microwave remote sensing sensors like radar provide earth observations at high spatial resolutions. This study based on physical model simulations (Advanced Integral Equation Method, AIEM, and Water Cloud Model, WCM) combined with the Artificial Neural Networks to investigate the potential of the ALOS-2 and Sentinel-1 radar images for estimating soil moisture at high spatial resolution. The results shows that the statistical parameters of the relationships between estimated and measured soil moisture, expressed in terms of R, bias, and RMSE, are 0.834~0.878, 1.59~3.65 vol% and 3.36~6.15 vol% for ALOS-2, and 0.722~0.896, 1.75~2.97 vol% and 3.24~6.86 vol%, for Sentinel-1. In densely vegetated area, RMSE significant increases, due to the limited penetration ability of L and C bands in high vegetation areas.

Many algorithms and products for snow cover have been developed. Then a unified set of “ground truth” data is important to validate snow cover products. In this study, Landsat-8/OLI data processed by linear unmixing algorithm was determined as “ground truth” to validate the moderate resolution snow products. In order to evaluate the cloud removing effect of the daily fractional snow cover (FSC) dataset of MODIS over High Asia, we use the MOD10A1 FSC product which is calculated by recommended equations as the before cloud removing data, then the Landsat-8/OLI FSC was used to validate both of the MODIS data. The results show that when the percentage of cloud pixels is less than 10%, the binary accuracy can reach 0.85 or more, the mean absolute error is less than 0.25, and the root mean square error is less than 0.35. These results suggest that the product has high credibility, despite there is still a small amount of cloud in the product.

Land surface freeze/thaw (F/T) has an important influence on the carbon cycle of the ecosystem, hydrological and meteorological. Affected by the frozen of water in soil, the frozen soil would become larger soil particles due to condensation, resulting in its soil particles being larger than those of the melting soil. The microwave radiation of frozen soil has a significant volume scattering effect in the high frequency band due to large particles of frozen soil. This paper developed a frozen soil microwave emission model considering the volume scattering effect of frozen soil particles based on dense media radiative transfer (DMRT) model [1] by combining experimental data and numerical simulation. The simulation results of the frozen soil microwave emission model, combined model for cold land [2] and the three-layer incoherent media model [3] were validated and compared with the experimental data. Results show that frozen soil microwave emission model which considering the volume scattering effect of frozen soil is better than the other two models. It is necessary to consider the volume scattering effect of frozen soil.

One of the key variables describing global seasonal snow cover is snow water equivalent (SWE). The GlobSnow-2 SWE product is widely used in in many research areas due to the high accuracy level and a long historical record (1979 to the present) in Globally. The satellite data used in GlobSnow-2 are mainly from the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS). However, there is no launching plan for these sensors in the future. To ensure a continuation of GlobSnow-2 product, this paper assesses the consistency of SWE estimates between the Microwave Radiation Imager (MWRI) and SSMIS in GlobSnow-2 retrieval scheme. The analysis is conducted in three regions (Finland, Russia and China) over the Northern Hemisphere. The results show that the SWE difference between MWRI and SSMIS is small and even can be negligible. This study provides a scientific basis for treating the MWRI dataset as one continuous record.

High-resolution soil moisture dataset is important for studying cold and humid temperate forest climates, estimating forest carbon emissions and storage, and identifying the influence of water circulation and global change in Genhe area. Leaf Area Index (LAI) is an important vegetation biophysical variable and has been widely used for analysis of the vegetation biomass, land-surface process simulation, and many other global change studies. This paper based on LAI from Global LAnd Surface Satellite (GLASS), microwave polarization difference index (MPDI) from Soil Moisture Active Passive (SMAP) L band Brightness Temperature (TB), and synthetic Land Surface Temperature (LST) from combine AMSR-2 TB and MODIS LST data proposed a downscaling method for SMAP L3 soil moisture product. Using the multiple linear regression method, we obtained 1km spatial resolution of soil moisture data in Genhe area. The results showed that downscaling SMAP soil moisture can present more details than before with low errors.

This study investigated a data fusion method based on pixel-based robust stepwise regression technique to retrieve a long-term snow depth dataset from passive microwave observations. The NOAA's Snow Data Assimilation System (SNODAS) snow depth(SD) product covered the United States was selected as standard reference to train the brightness temperature data from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature Earth System Data Record. In order to achieve robustness against the presence of outliers and avoid multicollinearity problem in regression, the robust stepwise regression technique was selected as the training approach. The retrieved snow depth were evaluated against in situ observations and SNODAS SD. The results show that the retrieved SD have a good agreement with both in situ observations and SNODAS SD, and are more consistent with SNODAS SD than in situ data.

Land surface freeze/thaw (FT) state is important for identifying the variable of carbon-nitrogen, water and energy cycling and soil erosion. The Soil Moisture Active Passive (SMAP) mission produces global and northern hemispheric daily landscape FT dataset [1] at a spatial resolution of 36 km from L-band radiometer. Parameterized discriminant function algorithm (PDFA) [2] detect landscape FT state from Advanced Microwave Scanning Radiometer 2 (AMSR2) observations. In this study, we use the in-situ soil temperature to validate the SMAP global FT dataset (36 km) and PDFA-based AMSR2 FT dataset (0.25°). Performance evaluation are obtained from two regions at northern hemispheric located in the China. The evaluation results show that overall accuracies usually greater than 85% for SMAP global FT dataset at Genhe (GH) region, but lower than 70% at Saihanba (SHB) region. AMSR2 FT dataset have accuracies that always higher than 85% at both regions.

China’s new generation of geostationary weather satellite, FengYun-4A (FY-4A) carrying the Advanced Geosynchronous Radiation Imager (AGRI) was launched on December 1 2016 and its data became publicly available on March 12 2018. The availability of high temporal observation at visible, near infrared, short-wave infrared and long-wave infrared bands over stable snow covered areas in China inspired this study to investigate the feasibility of FY-4A AGRI to estimate fractional snow cover. In this preliminary study, we present the multiple end-member spectral mixture analysis on FY-4A AGRI’s 5-band reflectances for snow covered area estimation. The end-members were extracted automatically from multispectral images and typical end-members were selected using the vector length. To account for the reflectance variation due to sun-target-sensor geometry, the end-member extraction and selection and subsequent estimation were implemented by the 5°×5° spatial window. The fractional snow cover estimates were evaluated by using reference data from corresponding Landsat-8 Operational Land Imager (OLI)’s 30-m resolution observations. The outcome indicated that FY-4A AGRI’s fractional snow cover agrees well with Landsat-8 OLI’s 30 m estimates. Comparisons showed that root mean squared error of FY-4A AGRI fractional snow cover ranges from 0.10 to 0.16 with R2exceeding 0.7.

Snow depth (SD) and snow water equivalent (SWE) are significant parameters in climate and hydrologic models. Successful estimation of SD (SWE) can improve the accuracy of snowmelt-runoff predictions and the management of water supplies. Currently, passive microwave (PMW) remote sensing is the most efficient way to monitor SD on global and regional scales; however, there are many challenges for accurate SD estimation. In this study, a new spatial dynamic method is developed by introducing random forest (RF) model, AMSR-2 TB and other auxiliary data. The main objective of this work is to produce long term SD dataset with the dynamic method using the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMI/S) which span from 1987 to present. Through evaluation and analysis, the RF method performs better than traditional linear-fitting model. However, it tends to overestimate SD in shallow snow cover areas. Now, a preliminary spatial dynamic method (pixel-based model) is developed. For further work, the evaluation will be conducted to assess the feasibility of SD reconstruction. Moreover, to address overestimation over shallow snow areas, the snow depletion curve (SDC) incorporating SD and fractional snow cover (FSC) is expected to improve SD retrievals.

High resolution and long-term soil moisture products play an important role in estimating forest carbon storage and carbon emissions in Genhe, China. In order to obtain the high spatial and temporal resolution of soil moisture datasets in China, this paper proposed a downscaling method for the revised QP model with Dual-Channel Algorithm (QDCA) soil moisture product based on microwave polarization difference index (MPDI) from AMSR2 and Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Moderate resolution Imaging Spectroradiometer (MODIS) to derive high resolution of soil moisture data (1km). The downscaling method is validated with the in situ soil moisture data over Genhe in China, and the results showed that the R, bias, RMSE between downscaling revised QDCA soil moisture and in situ measurements is 0.176~0.4156, 009~0.050m3m-3and 0.056~0.087m3m-3, respectively. With the different land surface, the accuracy of downscaling soil moisture in grass land cover is higher than the forest land cover.

Soil freezing and thawing processes play important roles in water and energy exchanges, weather and climatology. Passive microwave remote sensing tends to be one of the most effective ways of monitoring global surface state of freezing and thawing. However, Due to the complexity and variability of surface environmental factors, the thresholds in many algorithms are not universally suitable and the selection of thresholds mainly depends on the existing ground data. In addition, there is still a lack of comprehensive consideration of the complexity of the real surface in the modeling process. In order to solve these problems, firstly, a comprehensive database which contains complex surface conditions was built based on the data simulated from Cold Area Microwave Radiation Model and observed from satellite and ground sites. In this database, the effect of soil organic matter on microwave radiation was considered, the effective range of forest stock was redefined based on the biomass data, and the long-range satellite observations of brightness temperatures and nationwide ground-based meteorological site data were integrated. Then, the discrimination indexes (Tb36.5v and Qe, Tb36.5v and SDI) of “DFT algorithm” and “standard deviation algorithm” were respectively selected from the comprehensive database and used to establish new discrimination formulas based on the Fisher discrimination method. Through validated with the ground data, the F/T discrimination results based on the new formulas showed better performance than those based on the original algorithms, which demonstrated a better applicability for complicated surface conditions.

Ongoing information on snow and its extent is critical for understanding global water and energy cycles. Passive microwave data have been widely used in snow cover mapping for its long-time observation capabilities under all-weather conditions. But assessment of different passive microwave (PM) snow cover area (SCA) mapping algorithms have been rarely reported, especially in China. In this study, the performance of seven well documented successfully applied PM SCA mapping algorithms were tested using in situ snow depth measurements over China. The results shown in this study would contribute to the ongoing effort to improve the performance and applicability of PM SCA algorithms.

Fractional snow cover from Low-Earth-Orbit (LEO) satellites often encounters data gaps mainly caused by cloud obscuration in one or two observations in a single day. Imagers onboard GEOstationary (GEO) satellites have hourly or more frequent observations, making it possible to reduce cloud obscuration significantly. To map daily cloud-free fractional snow cover, we present a way using blended measurements from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and FengYun-2 Visible and Infrared Spin Scan Radiometer (FY-2 VISSR). The fractional snow cover from MODIS is estimated using the Multiple End-member Spectral Mixture Analysis (MESMA), while that from FY-2 VISSR is generated using a simple linear interpolation between snow and snow-free end-members. Quickly-moving clouds can be alleviated mainly by the utilization of FY-2 VISSR multi-temporal data. Then spatio-temporally continuous daily fractional snow cover results from the removal of remaining clouds through the interpolation pixel by pixel in time series of snow fraction.

The high-resolution freeze/thaw (F/T) monitoring plays an important role in studying carbon-nitrogen cycle, soil erosion and climate change in Genhe, China. In this paper, high-resolution downscaled land surface temperature (LST) retrieved from AMSR2 [1] were obtained from previous study [1]. And then were used to downscale the passive microwave (PMW) brightness temperature (TB) from 0.25° to 0.01° through downscaling method of PMW TB [2]. Finally, the downscaled TB data and F/T discriminant function algorithms [3], [4] were adopted to discriminate the surface freeze/thaw status. A comparison between high-resolution F/T state and soil temperature measured at 0~5 cm over Genhe area turned out that the F/T discriminant function algorithm [3] has a total classification accuracy higher than and 70%, and the improved F/T discriminant function algorithm [4] has a total classification accuracy higher than and 60%. From the perspective of orbit, both algorithms had freezing distinguished accuracy high than 90% at ascending and descending orbits. At last, we discussed and analyzed the possible problems of F/T discriminant function algorithms and downscaling method of TB.

The main objective of this work is to improve the snow depth (SD) estimation for the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMI/S) in China. To avoid systematic bias of different sensors to brightness temperature, it's better to retrieve SD with similar sensors. Meanwhile, long-term dataset is essential, which has significant impact on climate, weather and water resources. Sensors (SSM/I, SSMI/S) that span from 1987 to present are optimal for long time series of SD product generation. However, the accuracy of current SD (SWE) product still can't meet demand of climate and hydrological models. In this study, each grid-cell SD was estimated as the sum of SDs from each land cover algorithm weighted by percentages of land cover types. Through evaluation of this algorithm using measurements from 2005-2006, the root mean square errors (RMSE) are about 3.2, 2.9 and 5.3 cm for farmland, grassland and forest respectively. However, for mixed pixels, the RMSE is 6.6 cm. Finally, reasons about poor performance for mixed pixels were discussed. For further work, incorporating 10 GHz and fractional snow cover (FSC) is expected to improve SD retrievals in China.

For the multi-frequency sensors such as AMSR-E and AMSR-2, the verification results of QP model with dual-channel algorithm (QDCA) soil moisture product are not good in Genhe area. In order to obtain the long time series of soil moisture datasets and improve the accuracy of QDCA in China, this paper improved the vegetation correction method on current QDCA soil moisture algorithm. This paper incorporated the Vegetation water content (VWC) formulation from SMAP (Soil Moisture Active Passive) algorithm to estimate vegetation opacity thickness (VOD), b values and single scattering albedo redefined by different situations. The results showed that R2between revised QDCA retrievals and in situ measurements increased by approximately 18.4% and 10.1% and RMSE decreased by about 5% and 8.5% for the ascending and descending orbits, respectively.

Three MODIS-based fractional snow cover data are evaluated over the Tibetan Plateau from May, 2013 to April, 2015 with Landsat8/OLI data, including MOD10A1 in MODIS snow products collection 6[1, 2], along with MODSCAG[3] and MODAGE[4] fractional snow cover data which were retrieved based on linear spectral mixture analysis algorithm. The significant difference between MODSCAG and MODAGE is the endmember selection approach. This study compared these three products against the `true value' derived from Landsat-8/OLI. Forests, grass and soil region were chosen to carry out the evaluation, as well as the Himalaya Mountain due to its acute topographic heterogeneity. We applied both binary and fractional metrics to evaluate all three of them with the spatial resolution increasing from 500m to 1km, 2km and 5km. We also quantitatively depict the patchiness of snow cover under diverse spatial resolutions in order to analyze the effect of snow patchiness to the accuracy of algorithms under different spatial scales.

The Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow cover products may have significant errors due to cloud contamination, varying viewing geometry and complex surface properties. To improve snow and cloud discrimination with a particular interest in large sensor viewing angles, we utilize a reinterpretation test accounting for temporal surface variability to discard false positives and recover false negatives. This method is applied to MODIS fractional snow cover products including MOD10A1 and MODSCAG, then evaluated with reference snow cover generated from Landsat-8 Operational Land Imager (OLI) data. Rather than simply implementing evaluation at the normative 500 m spatial resolution, the expansion of pixel size is considered. Preliminary results indicate that this method significantly improves the precision and F-score of these two snow cover products, especially MODSCAG.

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).

QP model with dual-channel algorithm could accurately represent the effect of surface roughness on emission at different polarization under big view angle. The purpose of this paper is to estimate long temporal series soil moisture product based on the QP algorithm, and compared it with JAXA, LPRM in Genhe basin. The results indicate that QP retrieval values are closest to the ground data but it have many missing values and high RMSE (around 0.15m3m-3); JAXA product has good correlation coefficients (around 0.4) but underestimates the ground data. LPRM product overestimates the ground data and it is found to be very noisy and unstable. Finally, In order to improve the model, this paper analysed the tendency of QP retrievals with satellite brightness temperature, and examined the influence of auxiliary data for retrievals.

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.

This paper presented a pixel-based statistical regression method based on a high-resolution snow depth product to improve the accuracy of passive microwave snow depth retrievals. The statistical regression relation was established based on a linear relationship between the snow depth and the brightness temperature (TB) difference. The coefficients of these regression equations were derived using the snow depth product of Snow Data Assimilation System (SNODAS) as a reference. The regression relation was established over the winter period of 2013 and 2014. Passive microwave SD maps can be produced using the regression relation. Then retrieved SD was evaluated by the SNODAS SD product from November to December in 2010. The root mean square error (RMSE) and correlations (R) were computed between the SD retrievals and the SNODAS SD product. The R (mostly greater than 0.55) and RMSE (mostly lower than 16cm) maps showed a good agreement between the retrieved SD and the SNODAS SD product.

The coarse spatial resolution of microwave radiometer measurements hinders its application on land and sea surface parameters estimation. Measurements therefore require better spatial resolution to improve parameters estimation with enhanced resolution and accuracy. In this paper, a linear unmixing method is presented to downscale brightness temperatures (TB) for accurate land surface parameters retrieval. Contributions to brightness temperatures originating from different land surfaces can be identified with high-resolution land-cover images, land surface temperature products, and an antenna gain function. This produces an underdetermined equation set, which can be solved by a constrained linear least-square method with an assumption that the emissivity of each land-cover type over a small localized region is uniform. Finally, downscaled (unmixed) brightness temperatures of each land-cover type are derived from mixed pixels. Simulation results of three numerical experiments validated that the unmixing algorithm is capable of separating the signals of land-cover types from mixed pixels. The unmixing method is then applied to FY3B-MWRI measurements. The resulting downscaled brightness temperature presented enhanced details while keeping the original overall distribution of brightness temperatures. In conclusion, the linear unmixing method is capable of downscaling brightness temperatures.

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.

Soil moisture plays a crucial role in the terrestrial water cycle. It can be estimated by manual measurements for a small watershed, while this is very time-consuming when applied in the large scale. The remote sensing technology provides a new approach to monitor the soil moisture in a large scale. However, it should be evaluated before being used. In the study, the L-band Microwave Emission of the Biosphere model (L-MEB) model was used to retrieve the soil moisture based on the airborne brightness temperature in the Heihe River Basin, then the retrieved soil moisture was aggregated to 25km based on the area weighting factor method. The aggregated soil moisture was used to validate the two AMSR2 data: the JAXA soil moisture products and the LPRM soil moisture products. The results shows the JAXA SM products has an underestimation compared with the pixel-averaged SM, and the LPRM SM products is higher than the pixel-averaged SM.

Passive microwave remote sensing has demonstrated the potential for capturing the high temporal variability of the near-surface soil moisture, however the use of these data is limited by the poor spatial resolution. We compared two different downscaling methods using soil evaporative efficiency derived from Moderate-resolution Imaging Spectroradiometer (MODIS) to disaggregate AMSR-2 soil moisture product. Both methods used information from MODIS to obtain the distributed soil moisture map at regional scales and the results showed reasonable agreement with ground-based soil moisture observations. For Merlin downscaling results, the correlation coefficient and RMSE with ground measured data are 0.74 and 3.21%, and for UCLA downscaling results, the correlation coefficient and RMSE with ground measured data are 0.76 and 3.81%, respectively.

Determination of the component soil moisture content within one pixel using passive microwave remote sensing data is important for predicting soil moisture contents in ecohydrological research within the Heihe River Basin. The Heihe Watershed Allied Telemetry Experimental Research was conducted in 2012 to address this issue. An airborne polarimetric L-band microwave radiometer (PLMR) instrument was used to measure surface emissions over the middle stream of the Heihe River Basin. Extensive ground-based soil moisture content and temperature data were obtained during the PLMR flights. In this letter, an algorithm for estimating the component soil moisture content was developed using biangular PLMR observations. Based on a theoretical analysis, the linear relationship between the soil emissivities at two different incidence angles was obtained. Therefore, the component soil moisture could be derived based on the tau-omega model. In addition, the component soil moisture contents determined over the bare surface were lower than those over the vegetated surface. The root-mean-square errors between the calculated soil moisture contents and the measured soil moisture contents over the bare and vegetated surfaces were 0.050 and 0.051 cm3/cm3, respectively. Overall, the results indicate that the component soil moisture contents can be estimated using biangular observations from airborne radiometer data.

The dielectric property of vegetation has a considerable effect on the characteristics of the microwave radiation of vegetation. In frozen environments, when the temperature is colder than normal, changes such as increased soluble sugar and decreased moisture content (MC) can occur in the vegetation. The dielectric property of vegetation, which is almost entirely controlled by its free and bound water content, will also change. To characterize the dielectric behavior of vegetation in frozen regions, a sensitive experiment was conducted on holly leaves with a high-performance coaxial probe over a frequency range from 0.5 to 40 GHz and a temperature range from 0°C to -20°C. Based on the measurements and the physical properties of the constituent substances of vegetation, a semiempirical dielectric model for holly leaves in low temperature environments was developed. In this model, a decrease in MC, which causes a reduction in the complex permittivity, was described as an increase in the ice content. The complex permittivity of bound water was measured using a saturated sucrose solution at -6.5°C. The research will provide a reference for the dielectric property study of the vegetation in frozen environments.

Soil moisture is an important parameter in many fields. Since the dielectric constant of soil is directly related with its moisture content, many soil dielectric constant models have been established and used in the application of soil moisture inversion. As an effective composition of soil, organic matter could increase the adsorption of soil particles and affect the dielectric constant. However, due to its little content, it was seldom considered in soil moisture inversion and brightness simulation. In this study, a semi-empirical organic dielectric model was used in the forward simulation of brightness temperature in Genhe River basin. The results show that it has a higher accuracy about 1.6k~2.4k than using TMD model at C-band and X-band.

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 usefulness of microwave remote sensing to retrieve near-surface soil moisture has already been demonstrated in many studies. However, obtaining high quality estimates of soil moisture is influenced by many effects from soil, vegetation and atmosphere; one of the key parameters is surface roughness. This research focusses on a semi-empirical method to evaluate the roughness effects from space borne observations. Global maps of roughness effects are evaluated at C-band from AMSR-E measurements.

The Interactive Multisensor Snow and Ice Mapping System (IMS) combines multiple data sources to map global snow cover. IMS can discriminate snow cover beneath clouds using time sequential imagery from geostationary satellites and passive microwave observations. During the snow disaster of 2008 in southern China, the IMS snow cover data identified more snow than those retrieved from passive microwave remote sensing data. In this study, the IMS snow cover mapping accuracy was assessed against the Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. Land cover and terrain effects on the snow cover accuracy were considered. Good agreements (> 90%) was observed over flat cropland surfaces. For forested, mountainous areas, a pronounced disagreement was observed between the two datasets. The accuracy of the IMS in these regions was less than 50%. Due to the sparse distributed snow cover in these regions, the effects of snow cover fragmentation degrees were studied.

The microwave signal at L-band is very sensitive to the soil moisture due to its penetrability. To analyze an algorithm to retrieve soil moisture at L-band, a simulation of microwave brightness temperature is conducted by using the τ-ω model in this study, and two methods of resampling are compared. One of the brightness temperature simulation is based on the point observation on ground, and the other is from the ground observation data of 1km resolution resampled from point. It turns out the latter method has a smaller error. An airborne L-band data from a Polarimetric L-band Microwave Radiometer (PLMR) acquired during the Hiwater experiment held in the Heihe River Basin in 2012 are used to validate the brightness temperature simulation. And the root-mean-square errors between L-MEB simulated and PLMR are 9K to 12K for V-polarization, and 6K to 8K at H-polarization respectively at different angles.

Passive microwave remote sensing observations have been widely used for long-term global monitoring of the Earth. Passive microwave data can be utilized to obtain important parameters (e.g. precipitation, snow cover, sea ice and soil moisture) of the Earth system with relatively high temporal resolution and regardless of lighting and cloud conditions. However, due to the limited lifetime of individual satellite sensors it is necessary to examine and cross-calibrate the brightness temperature data of different instruments when establishing long-term time series of observations. In this paper, brightness temperature data from SSMIS, AMSR-E and MWRI were compared over the Greenland ice sheet. In addition, the brightness temperature data from these instruments were also compared against tower-based brightness temperature observations over a test site in the boreal forest zone. A simple Snow Water Equivalent (SWE) retrieval algorithm was applied to the three satellite data sources to investigate the effect of observational biases to a typical satellite product on snow cover.

A useful indicator to evaluate the soil freeze-thaw intensity is the amount of phase transition water content (PTWC) in soil pores. In this research, a power function relation between soil phase transition water content (PTWC) and the variation rate of land surface temperature (VTS) was found by analyzing of ground measured soil moisture and temperature data obtained in the Tibet plateau during the winter of 2012. Then a downscaling approach combining MODIS VTsand AMSR2 products was employed to retrieve high resolution PTWC. The downscaled result was tested using in situ observations from the CTP-SMTMN network and found that it quite followed the trend of ground data with a RMSE of 0.0034 (m3/m3) and MAE of 0.0025 (m3/m3). The comparisons indicate that PTWC-VTSmodel has combined the advantage of microwave remote sensing and optical remote sensing; it has a high precision and can generate PTWC in small scale.

Knowledge about the surface soil water content is essential because it controls the surface water dynamics and land-atmosphere interaction. In high mountain areas in particular, soil surface water content controls infiltration and flood events. Although satellite-derived surface soil moisture data from passive microwave sensors are readily available for most regions globally, mountainous areas are often excluded from these data (or at least flagged as biased) due to the strong topographic influence on the retrieved signal. Even though a substantial volume of literature is available dealing with topographic effects on spaceborne brightness temperature, no systematic analysis has been reported. Therefore, we present a comprehensive analysis of topographic effects on brightness temperature at C-band using a two-step approach. First, a well-controlled field experiment is carried out using a mobile truck-mounted C-band radiometer to analyze the impact of geometric and adjacent effects on the radiometer signal. Additionally, a comprehensive radiative transfer model is developed accounting for both effects and tested on the ground-based data. Second, recorded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data over the Tibetan Plateau were used to analyze the error due to the impact of topography using the developed model. The results of the field experiment clearly show that the geometric effect of a single hill has a much larger impact on brightness temperature compared to the adjacent effect of multiple hills, whereby, due to the geometric effect, the bias is up to +20 K for horizontal and -13 K for vertical polarization. For the adjacent effect, the bias is less than 3 K for both polarizations. Additionally, the developed radio transfer model was able to reproduce both effects with high accuracy. For the AMSR-E data, the model shows that the brightness temperature recorded is biased in the same way as the ground-based measurements and that uncertainties induced by the wide existence of atypical mountain regions in the Tibetan Plateau will have a great impact on the retrieving error (maximum 30%). The largest impact on the retrieval error, on the other hand, is calculated for the soil moisture with a maximum relative error of 44%. The negligible impact can be attributed to false parameterization of the soil texture, soil surface temperature, and sky temperature. Finally, the overall absolute error in the estimated water content is quantified on average with 4%, whereby single pixels indicate a maximum absolute error of up to 16%. In conclusion, we show that recorded spaceborne brightness temperatures are highly biased by topographic effects in mountainous regions using a comprehensive radiative transfer model. Additionally, we suggest using this model to invert the effective surface emissivity of mountain areas for standard processing of higher level data products such as surface soil water content.

Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion, which is a serious threat to agricultural land and various buildings, especially for water projects. In this paper, a new method based on the passive microwave remote sensing technique was proposed to classify and assess the freeze-thaw erosion. The core of this new method is at two important indices: the freeze-thaw cycling days per year, and the phase transition water content per day. The first index can be used to determine the freeze-thaw erosion region and the second index can be used to evaluate the degree of freeze-thaw erosion. The application of this new method in China shows good result. It indicates that the freeze-thaw erosion regions in China are mainly distributed in Tibet, Mongolia and the province of Qinghai, Xinjiang, Gansu, Sichuan and Heilongjiang. Furthermore, the comparably serious freeze-thaw erosion region is located in Tibet Plateau.

Overpopulation, the rapid development of industrialization and the acceleration of urban expansion, all of these factors have changed the characteristics of underlying surface and atmospheric environment, especially in the urban area. The urban effect on regional climate changes in BTT regions was analyzed by the Regional Atmospheric Model System (RAMS). We simulate and analyze three days changes during the time from July 1stto 4th, 2003 in the BTT areas. The results show that simulation and observation is almost the same. Furthermore, the rapid urbanization makes temperature and sensible heat flux increase significantly, but latent heat flux decrease in BTT regions.

Dielectric constant is an important parameter in microwave remote sensing. The microwave scattering/radiation signal of vegetation is closely related to its dielectric constant. Many related models have been established by now. However, most of them can only be used in room temperature. Therefore, it brings errors in the research of vegetation in frozen environment. In this study, a new dielectric model, which can be used at frequencies ranged from 3GHz to 40GHz under temperatures between -20°C and -4°C, has been established. It was developed based on Debye-Cole dual-dispersion model. The validation shows it has an acceptable precision.

In this study, the dielectric constants of 12 types of soil with different organic matter content were measured using the coaxial probe method by network analyzer (0.5-40 GHz) at room temperature (approx. 23°C). The observed dielectric constant increases only slowly with soil volumetric water content up to a transition point. Beyond the transition point, it increases rapidly with volumetric water content. It was found that the value of the transition point was higher and the observed dielectric constant was lower at the same soil volumetric water content and frequency for soil with higher organic matter content. A simple semi-empirical model was proposed to describe the dielectric behavior of soil with organic matter. This model was developed based on the refractive mixing dielectric model (RMDM).

Surface soil moisture is an essential variable in hydrological process. A physically based statistical methodology for surface soil moisture retrieval in the SNOTEL-770 station was examined in this study. This approach uses MVIs-B parameter to minimize the vegetation effects. And by adding the weighted emissivity at two polarizations, the surface roughness effects are eliminated. Considering the noisy behavior of MVI-B might limit its applications, in this study, we attempted to use the Fourier analysis to refine the MVI. The methodology was tested against the SNOTEL-770 station with experimental data sets collected from Climate Change Initiative (CCI) Soil Moisture project and was shown to be an effective method of soil moisture retrieval for areas with sparse vegetation coverage.

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.

Penetrability is one of the greatest advantages of microwave remote sensing over other remote sensing techniques. Estimating the sensing depth of passive microwave remote sensing is meaningful for simulation of satellite signals and validation of land surface parameters. In this paper, microwave radiation response depth (MRRD) was proposed to describe the thickness of the soil layer, within which variations in soil moisture can significantly affect the emitted radiation. Then, a statistical model for estimating MRRD was developed by regression analysis. This model can estimate the MRRD using four parameters, which are soil moisture, soil temperature, frequency, and soil specific surface area. For validation, a controlled field experiment was performed using a truck-mounted multi-frequency microwave radiometer (TMMR) at Baoding, Hebei province, China. The accuracy of the statistical model, in terms of root mean square error (rmse), was approximately 0.54 cm for the available experimental data at the frequency of 6.925, 10.65, and 18.7 GHz. Finally, the statistical model was applied into soil moisture validation. Taking the Global Land Data Assimilation Systems (GLDAS) product as the measurements, we validated the soil moisture retrieved from advanced microwave scanning radiometer-earth observing system (AMSR-E) data. The measured soil moisture obtained from an empirical depth and MRRD were both used to comparison. The rmse between soil moisture retrieved and measured is 0.060 and 0.047 cm3/cm3for empirical depth and MRRD based method, respectively. These results have shown that it is more reasonable to considered MRRD than an empirical depth in soil moisture validation.

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.

Phase transition water content (PTWC) is an important indicator of the intensity of soil freezing-thawing and is the crucial parameter that influences land surface energy balancing. In this paper, a PTWC retrieval algorithm was used to estimate the PTWC of pixel-scale. Through the field experiment at North China Plain, the actual PTWC is obtained to evaluate the accuracy of inversion. The validation result showed that the algorithm based on AMSR-E can provide reliable values of PTWC in pixel-scale. The RMSE of all sampling points is 0.008v/v.

Global measurements of soil moisture, the key variables in the water cycle, are provided by spaceborne radiometer based on the long wavelength detection. As one potentially critical factor, topography will induce soil moisture retrieval error over mountain areas from space. To explore the mechanism of relief effects on L-band, the imitated landscapes are generated underlying Gaussian surfaces, and an improved microwave radiative transfer model to simulate relief effects is proposed. Based on the model, the significance of soil moisture and land surface temperature to relief effects in these terrain scenes are analyzed respectively, and the impact of topography on brightness temperature and soil moisture retrieval is predicted. It is shown that the maximum fractional error of soil moisture retrieval arisen by topography compared to soil moisture in the flat terrain at L band is 77.6%.

Snow mapping is of great importance in meteorological, hydrology and global change researches. In the heavy snow event in 2008 in the south of China, the optic remote sensing fails to map snow cover due to the existence of thick clouds, and the traditional passive microwave snow detection algorithm could only be used for dry snow. Therefore, to map snowcover in the south of China where the snowpacks are mostly wet, shallow snow, AMSR-E brightness temperatures from Jan 1stto Feb 20th, 2008 is used to analyze the brightness temperature characteristics in the region of 23-43°N, 102-122°E. Eight land surface types are extracted based on the site-observed snow depth and ground temperature, IMS (Interactive Multi-sensor Snow and Ice Mapping System) and MODIS snowcover. Then, a snow detection decision tree algorithm is established. Comparison of the algorithm-detected snowcover with the IMS product in 2008 and 2011 shows that, the use of 89 GHz channel can improve the snow-detection ability in the southern part of the study region. The performance of the algorithm in the sparse-vegetated region is better than that in the forest-covered region. The total accuracy of the algorithm is about 94%.

In this study, a parameterized first-order radiative transfer (RT) model for short vegetation layer is employed to retrieve the single scattering albedo of winter wheat by combining passive microwave AMSR-E data with optical MODIS data. The microwave vegetation indices (MVIs) with two adjacent frequencies of AMSR-E at H/V polarization derived from the parameterized model are used to cancel out the ground surface emission signals. Then a simulating database based on field measured parameters is established to figure out the relationship of the optical thickness, single scattering albedo at C and X band, respectively. Finally the characteristics of retrieved single scattering albedo are analyzed and the daily NDVI was utilized for evaluating the retrieved results.

Surface soil moisture is a key parameter in material exchange and energy cycle at the land surface and atmosphere interface. In this study, a soil moisture retrieval algorithm was developed based on a parameterized first-order radiative transfer (RT) model. This method was validated in ground surface covered by vegetation in Tibet. The results showed that the RMSE of the retrieved soil moisture reached about 4% by using the parameterized first-order RT model. The retrieval results were also compared to zero-order RT model and noted that both zero-order and first-order RT model varied similarily but the first-order retrieval algorithm seemed to be more accurate than zero-order model. Future work will investigate possible improments to the algorithm extend testing of the algorithm to other regions.

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.

Penetration depth of passive microwave remote sensing indicates the area below the land surface where singles come from. A reliable penetration depth is helpful not only in understanding the mechanism of microwave remote sensing but also in validating the retrieved results of land parameters. In this paper, we defined that the penetration depth in passive microwave remote sensing is the soil depth above which contributes more than 99% power of the total signal. Based on a three-layer non-coherent model (air-soil-aluminum), sensitive analysis was conducted to find out parameters which have more effect on penetration depth. Then three parameters including soil moisture, frequency and soil temperature were selected to build a simple statistic model to calculate the penetration depth of passive microwave remote sensing. To validate the statistic model, two controlled experiments were designed and carried out using a Truck-mounted Multi-frequency Microwave Radiometer (TMMR). It was shown that the penetration depth calculated from field experiment data coincided well with the statistic model.

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.

Spectral Gradient (SG) is one of indictors that had been used to classify prairie soil as either frozen or thawed. Researchers suggested that negative spectral gradients are caused by volume scattering darkening within the frozen soil. A radiative transfer version of first order that considered volume scattering was used to simulate the brightness temperature of frozen soil. On the basis of sensitivity analysis, the prediction of this model was compared with ground experimental measurements. The results show that the volume scattering effect should be considered when modeling and measured BT of frozen soil especially when the temperature of ground is low.

One of the most important characteristics of crop is row-structure, whose periodic property can result in different scattering features comparing with other types of vegetation. In this paper, we'll take wheat for example to analyze these special features based on field experiments. Theoretical foundation was first introduced and a field experiment was carried out for comparison and analysis. In the experiment, wheat land was scanned at different angles in both parallel-to-row and perpendicular-to-row directions based on a Truck-mounted Multi-frequency Microwave Radiometer (TMMR). With its support, we have done relevant sensitivity analysis of some characteristic parameters to explore the regular pattern of emissivity at these two azimuths. It has shown that row-structure of wheat has obvious effects on passive microwave emissivity at different observation azimuths, especially parallel-to-row and perpendicular-to-row. Soil moisture can influence these characteristics distinctly when the wheat was shot and sparse.

A ground based experiment measuring relief effects on microwave radiation at C band (6.925GHz) was carried on. According to the results of the field observation, relief effects were dived into primary and secondary effects. Both effects based we developed a relief effect simulated model that provides a reliable method to describe microwave radiation properties over mountain areas.

Simulation of brightness temperature and related snow parameters is essential to understand the microwave emission property and its evolution with change of the snow soil system status. In this paper, a typical thin snow pack on North China Plain is measured on Nov 13-16th, 2009 at Luancheng test site HUT (Helsinki University of Technology) wet snow emission model is used to predict the brightness temperatures at 10.65, 18.7 and 36.5 GHz. A physically-based snow process model, SNTHERM (SNow THERmal Model), is applied to simulate the snow melting process. The measured snow density and grain size is compared with SNTHERM prediction and HUT inputs. Results show that the application of snow emission model and process model can explain the variation trend of wet snow emission properties well.

The Tibetan Plateau is the highest plateau. And snow in covered at Tibetan Plateau can exert have important influence on the study of climate change and hydrological cycle. In this paper, we found that the brightness temperature of horizontal polarization at Nam Cu Lake is very low, which is about 170K at 18.7 GHz, by the analysis of the time series of the brightness temperature observed by AMSR-E. Even if the lake got frozen, the brightness temperature of horizontal polarization of ice is about 220K at 18.7 GHz, which is much lower than that at land. Then we use HUT (Helsinki University of Technology) snow emission model for a homogeneous snowpack - ice - water system to simulate the brightness temperature at AMSR-E's frequencies which are used in the current algorithms of estimation SWE at the satellite scale. Also from the seasonal variation of the time series of the brightness temperature, we could see that the brightness temperature of lakes increased sharply when being frozen and decreased rapidly when being melting. The HUT model can match well with the observed brightness temperature at 18.7GHz at horizontal channel.

The spectrums of different land types are very important in the application of remote sensing, which can be used in surface classification, change detection, and so on. The microwave emissivity of these land types are the foundation of land parameters retrieval using passive microwave remote sensing. As one of the most important land types, city's contribution in a passive microwave pixel cannot be ignored. In this paper, some "pure" city microwave pixel was selected and RFI effect was evaluated using several indicators. Eliminating days of RFI contaminated, city microwave emissivity in lower frequencies was extracted from AMSR-E brightness temperature data in 2008. Then the characteristic of city emissivity was analyzed. Based on precipitation data, we are trying to find the factors to affect city emissivity. The results have shown that the RFI effect was little except some channels. City emissivity was different for different frequencies and polarizations. It increased with the frequency becoming higher. The emissivity fluctuated along with seasons. According to comparison with meteorological data, there was an obvious correlation between the city emissivity and rainfall in higher precipitation.

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.

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.

To investigate relief effects on microwave radiation, it is essential to experiment, based on track-mounted microwave radiometer. There are four relief factors affecting microwave radiation features in this study we have researched, which are hill slopes, hill elevation, hill aspects, and hill shadows. To compare with relief effect simulation, we built relief landscape in the field experiment to validate microwave radiation of hill-scale topography bias resulted from some of relief factors. In the final analysis, through the relief experiment observed results we consider hill-scale topography dose have influence on microwave radiation, and it can not be ignored in the retrieval of surface parameters.

As an indicator of vegetation, vegetation index, such as Microwave Polarization Difference Temperatures (MPDT), Microwave Polarization Difference Index (MPDI) and Microwave Vegetation Indices (MVIs), is widely used in vegetation water content (VWC) and soil moisture retrieval. In this essay, Ferrazzoli's radiative transfer model was utilized for simulation, and a truck-mounted Microwave Radiometer (BNU-TMMR) for experiments. Subsequently, there comes a comparison and evaluation of the three vegetation indices' sensitivity to soil moisture and VWC which were proposed by both utilizing model simulation and field measurements. MPDT and MPDI increase with soil moisture rising up, and are more sensitive to lower soil moisture, while the trend of MVIs_B is smooth. In term of VWC influence, MVIs_B has higher sensitivity to lower VWC vegetation, and they are all negatively correlated to VWC.

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.

The current algorithms of retrieval of snow water equivalence (SWE) using passive microwave remote sensing is based on the linear brightness temperature difference. In the mountain areas, the topography affects the microwave signal received by the microwave radiometer by ways of changing the elevation and the radiation between the terrains. Accordingly it can exert some certain influences on the result of SWE using the current algorithms of the retrieval of SWE. In this paper, Guo's terrain correction algorithm (2009) of passive microwave remote sensing is applied to correct the brightness temperature globally at AMSR-E's frequencies which are used in the current algorithms of the retrieval of SWE. We evaluated the terrain impact on brightness temperature at 18.7 GHz and 36.5 GHz at each polarization and polarization difference, respectively. From our analysis, we could see that: at the global scale, Guo's terrain correction algorithm is sensible to the changes of the topography on brightness temperature; the influence of the terrain on the passive microwave brightness temperature is similar with at 18.7 GHz and 36.5GHz.The vertical polarization channels are apt to be greater than zero while the horizontal ones to be less than zero. From the analysis of Guo's terrain correction on Tb at 18.7 GHz and 36.5 GHz, brightness temperature differences of the same polarizations are better combination compared to that of the different polarizations. Most of the difference of SWE estimated on Jan. 2, 2009 after terrain correction could be 14 mm, and the maximum difference is up to 40 mm.

Soil moisture is an important variable in the process of water and energy exchanges at the land surface. Passive microwave remote sensing techniques have great potential for its frequent coverage, low data rates, and simpler data processing, but with poor spatial resolution, which resulted in sub-pixel heterogeneity. To study the effect of spatial heterogeneity of soil parameters on the retrieval of soil moisture, HI and EI were defined as the description of parameter's sub-pixel heterogeneity and the error of soil moisture retrieval, respectively. This paper firstly simulated different sub-pixel heterogeneities of each parameter, which were used as the input of the radiative transfer (RT) model, and then compared the soil moisture results retrieved by taking heterogeneity into account with that neglected the heterogeneity, seeking the relationship between those parameters' heterogeneity and the error of soil moisture retrieval. Finally, the conclusion was validated by two exact field experiments based on a Truck-mounted Multifrequency Microwave Radiometer (TMMR). It can be concluded that the RT model has enough accuracy to this study. The simulation analysis and field experiment reveals that there's a good relationship between the heterogeneity index and error index. The EI increased with HI grow up. However, the absolute error resulted by the spatial heterogeneity of soil moisture in bare soil is negligible.

Spectral gradient is one of the indicators of soil freeze and thaw. A negative spectral gradient was a sign of frozen soil. A truck-mounted microwave radiometer observed the frozen ground in two experiments. However, the measured spectral gradient was positive when the surface is frozen. From experiment measured and model simulated result, the relationship between the amount of unfrozen water of frozen soil and SG was found. It indicates that the amount of unfrozen water is a very important factor that determines the microwave radiation of frozen soil.

The snow coverage and physical parameters play a special role in the global water and energy budget study. The passive microwave brightness temperatures of snowpack depend not only on the snow depth or snow water equivalent, but also the snow fraction and possible vegetation canopy. In this paper, we established a mixed model for simulating the dry snow radiation, based on the advancements of recent years. Through simulation analysis, it is found that the underestimation of snow fraction will cause the snow depth or snow water equivalent to be overestimated. And the error increases with the increase of snow depth.

Ground surface freeze-thaw cycles caused by changes in solar radiation have a great impact on soil-air water heat exchanges due to the phase transition of pore water. This influence should not be ignored in the land surface process and global environment change studies because of its large extent and the rapid changes in daily and seasonal frozen ground. The key index for evaluating the influence intensity is the content of water-ice phase transition in soil pores at the ground surface. In this paper, a data set was generated by observing field experiments and physical model simulations based on the configuration of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). The results showed that microwave radiation from freezing/thawing soil has an obvious correlation to the phase transition process of soil water. A large change in soil surface emissivity was shown after the freezing of soil. The magnitude of the difference in emissivity change is strongly related to the amount of water-ice phase transition. It can be shown that the higher the phase transition water content (PTWC), the greater the emissivity difference, and the higher the frequency, the smaller the emissivity difference. Based on an analysis of a large amount of random simulation data, an interesting characteristic was found, in that the emissivity difference in vertical polarization at each frequency is nearly proportional to the phase transition water content. Thus, a ratio index called Quasi-emissivity (Qe) was developed to eliminate temperature effects during retrieval. Using these clear rules, a physical statistical algorithm was put forth to estimate the phase transition water content. Finally, the inferred results by ground-based radiometer observation were compared with the ground truth. A satisfying agreement was achieved with a root mean square error of 0.0265 (v/v). This indicated that the microwave radiometer has a great potential in the measurement of PTWC.

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.

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.

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.

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.

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.

Interference effect happens in layered medium. The brightness temperature oscillation has been observed during the freezing process of over-saturated soil, which could be explained by interference effect and a three layer coherent model. The modeled BT is qualitatively in consistent with the measurement. It is shown that the interference must be considered when measuring frozen soil with ground based microwave radiometer especially when using the frequency is low.

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.

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).

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.

The parameterized emission model of dry snow developed by Jiang et al. could be used to simulate the microwave emission signal for one layer snow. On the basis of sensitivity analysis, this simple snow parameterized model is firstly compared with the HUT model using the PSR observation with the corresponding snow pits data over North Park area in Feb., 2003. At lower frequencies, both of the two models underestimated the measurements, while the parameterized model was closer to the PSR at 36.5 GHz, since the parameterized model considered the multiscattering in the snow layer. Finally, with this parameterized model, we performed the brightness temperature simulation of the Polarimetric Scanning Radiometer data similar as AMSR-E, with the outputs from the Snow Model. The difference between the simulated TBs and the measurements of PSR was as large as 20K, even more at l0.7 GHz. Through analysis, the discrimination was possibly either linked with the emission model or due to snow surface simulations. This comparison case made us to better understand how accurate the simulations could be in reality.

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.

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.

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.

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

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.

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.

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.

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.

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.

Kernel-driven BRDF (bi-directional reflectance distribution) model was the core of the AMBRALS, an algorithm for MODIS land surface BRDF and albedo products. In the onboard version of AMBRALS, the LiSparseR Geometrical-Optical (GO) kernel was used. But a new derived kernel - LiTransit kernel is also good at transition from LiSparse kernel to LiDense kernel when zenith angle is large, and accords more to the basic principle of GO model than LiSparseR kernel. Results of validation show: RossThick-LiTransit kernels combination has more stability when extrapolated to large sun zenith angles with LiSparseR kernel. Therefore, we will use the LiTransit kernel instead of LiSparseR kernel in the new version of AMBRALS. The speed requirement of tremendous data processing can't be meet easily, such as MODIS data. Although we can calculate the integration of the kernel beforehand, store up and acquire through look-up table method during retrieving albedo, it's inconvenient for an integrated data processing system. Thus we need to get a simple form of the integration of the kernels. Because the integrations of the kernels are approximately independent on directions than BRDF, it's sufficient to use a polynome dependent on the solar zenith angle to regress the integration of kernel. In this paper, we study the polynome regression of LiTransit kernel to instead LiSparseR, but not affect the systematic of the algorithm at the same time.

Predictions of climate change typically use a GCM linked to a land surface model. Land surface models, e.g. Biosphere-Atmosphere Transfer Scheme (BATS), estimate the albedo of trees over snow roughly with the parameters of roughness length, z/sub 0/, and snow depth, d. Based on their work, we further consider the difference in directional-to-hemisphere albedo for different solar zenith angle (SZA), and leaf area index (LAI) dependence. In order to keep the basic feature of the BATS model and to add these two new features, we simplified the geometric optical and radiative transfer (GORT) hybrid model of Li, et al. [1995] to reach this purpose. This model can be rather simple to retrieve the albedo of remote sensing pixel. It can be a strong tool to understand the climate system.

Local statistic-based fusion algorithms are discussed, which can be applied to fuse high-resolution VNIR images and a single low-resolution TIR image. These algorithms are based the experiments that structural information of observed objects In visible and near-infared spectral range (VNIR) is essentially correlated with the environmental information (especially moisture information) in thermal infrared spectral range (TIR). The local is performed at three scales. This algorithm can be useful method to merge the TM image and VNIR images.

We propose a thermal bidirectional gap model to describe the thermal directional emission from row crop canopies. An important concept of overlap index is used in this model to express the correlation between the gaps in the Sun and view directions. Detailed directional thermal emissions, row structure, LAI, component temperatures were measured in the experiment taken in Shunyi China, 2001. These data are used to validate our model. As an illustration, we compared our bidirectional gap model with the model that doesn't consider gaps (Kimes model) and the model only consider gaps in view direction. It is found that our model gives out the closest results to the field measurements.