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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

Soil moisture is a critical factor in cosmopolitan meteorological and hydrological processes. Microwave remote sensing brightness temperature is sensitive to soil moisture through the effects of moisture on the dielectric constant and hence emissivity of the soil [1]. It was found that the brightness temperature at L-band is useful for retrieving near-surface soil moisture [2-3]. The Soil Moisture and Ocean Salinity (SMOS) satellite, which carries an L-band passive microwave radiometer in the 1400-1427 MHz protected band, was successfully launched in November 2, 2009 and it has become a useful tool monitoring soil moisture [4-5]. However, it is very important to assess the performance of soil moisture product before using it and the validation is still a challenging task to validate these soil moisture retrievals due to the coarse spatial resolution of passive microwave remote sensing [6]. During the past few years, the SMOS soil moisture products have been evaluated over several areas of the world, e.g., Europe [7-8], North America [9] and Oceania [10]. But the evaluations were limited in the Tibet area in China [8,11]. More assessments need to be performed in other areas of China.