In cold regions, the coexistence and interconversion of liquid water and ice in frozen soils have important implications for energy partitioning and surface runoff at the Earth's surface. Passive microwave remote sensing is crucial for global monitoring of soil moisture (SM). However, current research on SM focuses mainly on unfrozen soil conditions. Limited studies have been conducted on variations in soil liquid water content throughout the freezing season. This study investigated the potential use of brightness temperature (TB) observations from the Soil Moisture Active Passive (SMAP) satellite for retrieving all-season liquid SM. The single-channel algorithm (SCA) and the Zhang-Zhao dielectric model, which was specifically developed for freezing and thawing soils, achieved successful retrieval of liquid SM in both frozen and thawed soils, even when snow cover was present. The results indicate improved spatial coverage (during winter) and consistent spatial patterns in SM compared to the SMAP products. Validation at 17 SM networks suggests that the retrieved all-season liquid SM effectively captures the dynamic characteristics of each region with an average bias of 0.011 m ³ /m ³ , an average unbiased root mean square error (ubRMSE) of 0.056 m ³ /m ³ , and an average correlation coefficient of 0.76. The additional retrieval of unfrozen water content during the freezing season would enhance the monitoring and understanding of the hydrological cycle and energy balance in cold regions.

Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a $\tau -\omega$ vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters ($\tau$, $\omega$) and soil roughness ($S_D$) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m³ for all stations, with a mean bias of 9.4 kg/m³. The RMSE can be further reduced if an artificial tuning of three predetermined parameters ($\tau$, $\omega$, and $S_D$) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics.

The topography can be very important for passive microwave remote sensing of soil moisture due to its complex influence on the emitted brightness temperature observed by a satellite microwave radiometer. In this study, a methodology of using the first brightness Stokes parameter (i.e., the sum of vertical and horizontal polarization brightness temperature) observed by the Soil Moisture and Ocean Salinity (SMOS) was proposed to improve the soil moisture retrieval under complex topographic conditions. The applicability of the proposed method is validated using in-situ soil moisture measurements collected at four networks (Pali, Naqu, Maqu and Wudaoliang) on the Qinghai-Tibetan Plateau. The results over Pali, which is a typical mountainous area, showed that soil moisture retrievals using the first brightness Stokes parameter are in better agreement with the in-situ measurements (the correlation coefficient R >0.75 and unbiased root mean square error < 0.04 m ³ /m ³ ) compared with that using the single-polarization brightness temperature. At the other three networks with relatively flatter terrains, soil moisture retrievals using the first brightness Stokes parameter are found to be comparable to the single-polarization retrievals. On the contrary, the maximum bias of the retrieved soil moisture caused by topographic effects exceeds 0.1 m ³ /m ³ when using vertical or horizontal polarization alone, which is far beyond the expected accuracy (0.04 m ³ /m ³ ) of SMOS satellite. In the regions on the Qinghai-Tibetan Plateau where the vegetation effect can be ignored, soil moisture retrieved using horizontal polarization brightness temperature is generally underestimated, overestimated when using vertical polarization brightness temperature. It is reasonable due to the polarization rotation effect (depolarization) caused by the topographic effects. It is concluded that the proposed method for soil moisture retrieval using the first brightness Stokes parameter has a great potential in reducing the influence of topographic effects.

L-band passive microwave remote sensing is currently the optimum tool for the large-scale soil moisture mapping. However, due to the coarse spatial resolution of passive microwave remote sensing, the existence and dynamic changes of water bodies in the field of view would affect the performance of soil moisture retrieval. The Qinghai-Tibet Plateau (QTP) is a densely populated area of lakes in China, and the correction of water contamination effect is much more challenging. In this letter, we proposed a simulation-based approach which utilizes a radiative transfer model to simulate the brightness temperature (TB) of water bodies to correct the Soil Moisture Active Passive (SMAP) data over the QTP, taking into account of wind speed, surface temperature and salinity of lakes. The simulated data and water area fraction obtained from a dynamic map of water bodies were applied to remove the effect of water contamination. Results showed that the relatively low SMAP TBs surrounding both small and large lakes could be eliminated and do not have correlation with the water area fraction after using the proposed approach. Validation results in the Naqu network demonstrated that the simulation-based approach can effectively remove the water contamination from SMAP data with improved soil moisture retrievals.

Soil moisture is an essential variable for understanding water and heat exchanges between land and the atmosphere. Presently, L -band remote sensing technology has been widely employed for routine measurement of soil moisture from space. However, the spatial resolution of L -band soil moisture products obtained from microwave radiometers is too low (dozens of kilometres) to meet the needs of practical applications, such as hydrology modeling, weather forecasts, agricultural applications and water resource management. Therefore, this article proposes a new concept to downscale the soil moisture active passive (SMAP) L -band brightness temperature by using advanced microwave scanning radiometer-2 (AMSR2) X -band TB data, including the time-series regression (TSR) algorithm and two-dimensional discrete wavelet transform algorithm. An intercomparison study was conducted over a semiarid area located in the Shandian river basin with the other two algorithms of the Backus–Gilbert (BG) optimal interpolation and natural neighbor interpolation without using the X -band TB data. The results revealed that the BG algorithm outperformed the NNI, 2D-DWT, and TSR algorithms compared with the original 36-km SMAP TB and airborne 1-km TB data. However, the soil moisture retrievals within one 9-km pixel with 8 soil moisture stations showed that the downscaled L -band TB with X -band data are reliable with lower unbiased root-mean-squared errors compared with resolution-enhanced TB without AMSR2 data.

The monitoring of global soil moisture is crucial for understanding the hydrological cycle and managing terrestrial water resources. The Chinese-French Oceanography Satellite (CFOSAT), equipped with the first sector-beam rotary scanning microwave scatterometer (CSCAT), provides a novel opportunity for global soil moisture mapping. However, the capability of CFOSAT's Ku-band for soil moisture retrieval remains underexplored and lacks systematic evaluation. In this study, an Adaptive Backscatter Change Tracking (ABCT) algorithm is designed to retrieve absolute soil moisture from CFOSAT's CSCAT measurements. The ABCT algorithm assumes a stable roughness, where changes in backscattering are primarily attributed to soil moisture variation based on a logarithmic relationship. It incorporates a vegetation influence coefficient, which quantifies how vegetation impacts the backscatter signal This coefficient adaptively scales with changes in the Normalized Difference Vegetation Index (NDVI) to adjust the backscattering appropriately to include the effect of vegetation growth or decay. This allows the algorithm to isolate changes in the backscatter signal that are due to soil moisture while minimizing the false readings from vegetation growth or wilting. The CFOSAT ABCT algorithm's performance was evaluated against extensive in-situ soil moisture data, demonstrating a robust correlation, with the Vertical-Vertical Polarization Ascending Orbit (VV Asc) result showing the highest accuracy, indicated by Pearson's correlation coefficient (R) of 0.68 and unbiased root mean squared error (ubRMSE) of 0.057 m3/m3. Comparative analysis with the Advanced Scatterometer (ASCAT) data revealed that, while the ABCT algorithm's correlation was slightly lower than that of the official EUMETSAT H SAF product, it notably improved the bias and ubRMSE metrics. This study underscores that the CFOSAT ABCT soil moisture retrieval algorithm and product are a valuable addition to global soil moisture mapping, complementing existing satellite missions or sensors such as SMAP, SMOS, ASCAT, AMSR2 and FY-3/MWRI.