Satellite microwave remote sensing is acknowledged as the primary method for obtaining global-scale surface soil moisture (SSM) data. Typically, spaceborne microwave sensors aboard polar-orbiting satellites yield SSM estimates with a native spatial resolution of several tens of kilometers and a temporal resolution of about once or twice daily. This indicates considerable potential for enhancing both of their spatial and temporal resolutions. Although spatial downscaling of spaceborne microwave SSM has garnered significant attention recently, the enhancement of their temporal resolution has received less focus. This study pioneers a methodology for generating hourly-scale satellite SSM estimates. The developed approach integrates a novel blending module that combines geostationary satellite observations with a spatially downscaled SSM dataset derived from traditional fusion among microwave and optical observations on polar-orbiting platforms. This blending module leverages land surface temperature (LST) data from geostationary satellites, which effectively quantify SSM variations on an hourly interval upon the thermal inertia theory. Consequently, a comprehensive spatio-temporal integrated framework for SSM downscaling is established to produce hourly-scale SSM at a resolution of 6 km, enhancing upon the daily and 36-km resolutions of existing microwave SSM datasets. Validation of the downscaled hourly SSM estimates was conducted through an established ground soil moisture observatory network in North China, revealing an unbiased root mean square error (ubRMSE) of no higher than 0.04 cm³/cm³. This result confirms preservation of the fundamental accuracy of original microwave SSM retrievals and demonstrates the effectiveness of the developed framework in improving both spatial and temporal representativeness of SSM data.

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.

The impact of the initial spatial resolution of soil moisture maps on the quality of downscaled maps by merging with a higher resolution dataset was addressed. Soil moisture maps acquired with airborne sensors in four different campaigns in different climate regions with resolutions of 500 m to 1 km were aggregated to 4-5 km, 8-10 km, 18-20 km and 36-40 km before applying a downscaling algorithm to compute 1 km maps. These maps were compared to the original maps at 1 km resolution. Using different quality metrics, it is shown that the downscaled maps are 30%-75% more accurate when the initial resolution is in the range of 5-10 km with respect to initial resolutions of 36-40 km.

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

The 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.

Evapotranspiration (ET) plays a crucial role in the global energy and water cycles. Clarifying the time-series characteristics of ET is essential for accurately estimating ET and comprehending the ET change. This study utilized the latest ETMonitor product to analyze ET time-series characteristics in China from 2001 to 2021. The standard deviation and coefficient of variation were used to measure the absolute and relative variability of ET. A monthly ET time series was decomposed using an additive decomposition method to analyze its components. The results showed that China's ET exhibited an increasing trend, especially in the middle regions of the Yellow River to northeastern China, at a rate exceeding 5 mm/year. Northwestern China showed larger variation coefficients due to lower ET. Seasonal and irregular components were found to dominate the monthly ET time series, with significant seasonal components observed in eastern China and irregular components dominating the western region. Moreover, the seasonal characteristic of ET was more evident in north and northeast China compared to southern and northwestern regions. Additionally, relative variation in ET during winter was more significant than in summer, primarily in northern river basins.

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

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

Optical sensors are subject to cloud obscuration and sunlight dependence, resulting in large proportions of missing snow cover information. Microwave sensors are a good alternative to snow cover monitoring in all weather conditions. Thus far, few studies in the literature have directly derived the fractional snow cover (FSC) from passive microwave data, and none have considered the relationship between FSC and brightness temperature (TB). This study first explores the FSC–TB relationship with a radiation transfer model, exhibiting that no generic function can properly describe the nonlinear and complex FSC–TB relationship. Therefore, a new algorithm based on machine learning method was designed to improve FSC retrieval from TB data, considering other auxiliary information, including soil property, land surface, and geography information. Benchmarked against the Moderate Resolution Imaging Spectroradiometer (MODIS) reference FSC, our FSC retrieval model performed well with an average correlation coefficient of 0.70, the mean absolute error ranging from 0.15 to 0.17, and the root-mean-square error ranging from 0.19 to 0.21. The generated FSC maps reasonably characterized the seasonal dynamics and spatial distribution patterns of snow cover; time series analysis with three AmeriFlux stations observation indicated effective capture of snowpack evolution process by the generated FSC. In addition, the verification of snow mapping capability using snow depth measurements from 13 521 stations indicates that it was relatively stable with overall accuracy greater than 0.88. For precise monitoring of snow cover extent in all weather conditions, particularly for subpixel snow cover areas, the development of the FSC estimation scheme with TB data should be extensively encouraged and implemented.

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

Surface snowmelt is a vital environmental parameter that affects energy exchanges between polar ice sheets and the atmosphere. Due to the difficulties of continuous in-situ measurements, passive microwave remote sensing technology has become a major method for obtaining ice sheet surface snowmelt states over large areas. Feng Yun-3 (FY-3) series satellites, the second generation of Chinese polar-orbiting meteorological satellite missions, have great potential for providing long-term polar ice sheet surface snowmelt state products. In this study, we establish a monthly inter-calibration model to synergize brightness temperatures from the Microwave Radiation Imager (MWRI) aboard different FY-3 satellites. Based on the calibrated continuous brightness temperature record, an improved snowmelt algorithm is proposed by using an adaptive thresholding method, which does not rely on in-situ observation data. After inter-calibration, the consistency of the melt extent obtained by different sensors is considerably better than before, with the bias decreasing from 85 pixels to 3 pixels in the Greenland Ice Sheet (GrIS) and from 16 pixels to 6 pixels in the Antarctic Ice Sheet (AIS). Evaluation of the snowmelt result is conducted with the automatic weather station (AWS) air temperature, and a promising accuracy is found with an overall accuracy above 92% in the AIS and approximately 86% in the GrIS. This study provides new possibilities for a long-term continuous snowmelt product by connecting FY-3B, FY-3C, FY-3D, and its successors FY-3F and FY-3G. The inter-calibration coefficients and FY-3 crossing times are available at https://doi.org/10.6084/m9.figshare.20657712.v1.

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

An unexplored aspect of L-band microwave emission is the impact of soil moisture and soil temperature (SMST) profile dynamics on diurnal brightness temperature ( TB) signatures of frozen soil. This study investigates this effect by comparing the TBsimulations of layered ( TB,l) and uniform ( TB,u) soils using a newly developed integrated land emission model. The multilayer Wilheit model and the single-layer Fresnel model are adopted to compute the smooth soil reflectivity for the layered and uniform soils, respectively. A four-phase dielectric mixing model is used to calculate the soil permittivity ( εs). A data set of concurrent ELBARA-III TBand SMST profile measurements performed in a seasonally frozen Tibetan meadow ecosystem is used for the analysis. The simulated TB,lconsidering SMST profile information captures well the ELBARA-III measurements with low biases (≤6 K) and high correlations ( R2≥ 0.88). TB,uproduced based on the Fresnel model using the soil moisture of 2.5 cm is more consistent with the TB,l. The sensitivity test of averaging SMST profile below 2.5 cm leads to maximum differences of 2 K in TB,lsimulations, indicating that the TBvariations are primary dominated by the SMST dynamics at the surface layer. A sensitivity test of the Wilheit model to different εsparameterizations shows that the dielectric model of Zhang et al. is comparable to the four-phase dielectric model in simulating TB,l, while the Mironov et al. 's model demonstrates larger biases for frozen soil with, on average, 2.2% clay content, 49.7% sand content, and a bulk density of 1 g·cm-3.

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

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

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

Vegetation monitoring is important for the study of the global carbon cycle and ecosystem. Aquarius/SAC-D mission that launched in 2011 is the first satellite with both active and passive L-band microwave instrumentation. The Aquarius instrument consists of three dual polarized L-band (1.413 GHz) radiometers each with its own feedhorn and a fully polarimetric L-band (1.26 GHz) scatterometer that makes use of the radiometer feedhorns. In this study, the impact of soil moisture and vegetation on L-band backscatter and emission is studied using Aquarius scatterometer/radiometer measurements. Considering that the passive microwave data are more sensitive to changes in surface water and active microwave data are more sensitive to vegetation cover and roughness, we attempt to use radar vegetation index (RVI) for monitoring global vegetation. The relationship between RVI and VWC is explored at regional and global and continental scales based on Aquarius observations. The VWC data are derived using satellite estimates from MODIS/NDVI. It is found a good statistical relationship between RVI and VWC, especially for agricultural areas. Although statistical relationship and seasonal variability is captured, the dynamic range of the RVI is insufficient for a meaningful signal-to-noise. Further vegetation optical depth (VOD) is estimated using the τ−ω model by reconstructing the microwave vegetation indices (MVIs) and Microwave Polarization Difference Index (MPDI) derived from Aquarius radiometer data. The results indicate that the VOD can generally reveal vegetation seasonal changes and can provide unique information for vegetation monitoring. The uniqueness of this approach is that the RVI and VOD are retrieved directly from the backscatter and brightness temperature without any field correction or auxiliary vegetation data. This method can provide a new opportunity for long-term global vegetation monitoring for Aquarius or SMAP mission.

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

Drought is one of the major natural disasters which not only causes great damage to ecosystems and the environment, but also seriously affects social and economic activities and residents' lives. In this study, the MODIS- Albedo, LST and NDVI data was used to downscale the FY-3B soil moisture product at 25km to 1km by multivariate statistical regression method. Then the drought of Shandong Province was monitored using the downscaling results in 2016. The results indicate that drought monitoring using multisource remote sensing data is very useful for drought monitoring.

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

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

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

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

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

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

According to the microwave radiation characteristics, this paper introduced a new frozen soil dielectric model to calculate the dielectric constant of frozen and thawed soil based on the Helsinki University of Technology (HUT) microwave snow emission model. The Advanced Integrated Emission Model (AIEM) was used to calculate surface emissivity. The multi-frequency microwave radiation model and soil freeze/thaw discriminant algorithm were improved. The classification accuracies of original and improved soil freeze/thaw discriminant algorithms were validated using AMSR2 Level 3 daily gridded 0.25° brightness temperature products and the measured values obtained by ground-based microwave radiometer. The results showed that compared to the original discriminant algorithm, the frozen soil classification accuracy of the improved discriminant algorithm was effectively improved and the overall classification accuracy reached 82%. It turned out to be a comparatively reliable mode of discrimination.

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

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

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

The synergy of active and passive microwave measurements have attracted increasing attention in recently years. In this study, we investigate the relationship and covariation of the SMAP radar backscatter and radiometer reflectivity as a function of surface roughness and vegetation. Two radar-derived indices, namely the radar vegetation index (RVI) and radar roughness index (RRI) are adopted to account for the contributions from vegetation and surface roughness respectively. The results show RVI distinguishes vegetation density well in sparse to densely vegetated regions, while significantly overestimates the biomass over some dry desert regions due to possible soil volume scattering effects. RRI well captures the negative covariation of active and passive measurements in bare and sparsely vegetated surfaces, while becomes ineffective in densely vegetated areas due to the reduced contribution from soil surfaces.

Large amounts of RFI(Radio Frequency Interference) are observed in the data obtained over East Asia, North America and Europe by L-band missions of SMOS, Aquarius and SMAP etc. Multiple approaches have been proposed to detect and eliminate the RFI signals in the past few decades. This paper focuses on a new potential RFI detection and mitigation method that is based on the Local Outlier Factor (LOF) algorithm. Results on Aquarius L1A data show that a better performance can be obtained by the LOF algorithm compared with the official “glitch detector” method.

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

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements.

Surface soil moisture (SSM) is a key state variable in controlling land surface energy balance and hydrological process. Based on the mechanism behind land surface temperature (LST)-vegetation index (VI) triangle space, an empirical relationship model has been proposed for SSM estimation with LST, NDVI, and surface albedo, and it has been applied in downscaling the coarse resolution microwave soil moisture product. In this paper, three soil moisture observation networks (REMEDHUS, MAQU, and MURRUMBIDGEE) were selected to evaluate the performance of this model at different climate and land cover conditions with in situ soil moisture measurements and Landsat satellite observations. According to the estimation results from different days for each network, it was found that the model was able to capture SSM variation with a satisfied accuracy [overall root-mean-squared error (RMSE) ranging from 0.025 to 0.055 m3/m3], and the R2can reach 0.9 on some individual days. However, the performance has high daily variability with some poor ones. The reason is partly attributed to the high sensitivity of the coefficients of the model to the variation degrees of the input LST, normalized difference vegetation index (NDVI), and SSM. Meanwhile, the spatial scale differences between the point measurement and satellite footprint observation are another important issue. To improve the model performance, a new relationship model was proposed by introducing the modified normalized difference water index, and the estimation results had a pronounced improvement (overall RMSE ranging from 0.021 to 0.049 m3/m3) compared with the previous model. The application effect of the proposed model showed that the model coefficient calibration accuracy greatly determined the uncertainty level of the estimation results.

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

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

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

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

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

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

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

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

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

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology. Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m3/m3, Bias = -0.007 m3/m3, and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

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

Soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS) satellite were evaluated using in situ observations. The sites are located in different regions of the U.S. and provide replicate sampling of surface soil moisture at the SMOS footprint scale. Data from a sparse network were also considered. Soil moisture products from the Advanced Microwave Scanning Radiometer were also used for validation. Results based upon a preliminary version of the retrieval algorithm indicate promising performance. It is anticipated that the accuracy and reliability of the retrievals will improve as validation information is evaluated.

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.

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.

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.

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.

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.

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.

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.