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).

Land surface albedo analysis and prediction are of great significance for global energy budget research and global change forecasting. Research has been performed on time series albedo analysis but seldom attempt was performed on land surface albedo prediction. This article develops an effective method for land surface albedo prediction from Moderate-Resolution Imaging Spectroradiometer (MODIS) time series albedo data (MCD43A3). It consists of time series data decomposing and time series data forecasting. The ensemble empirical mode decomposition (EEMD) method decomposes the MODIS historical time series albedo data into several intrinsic mode functions (IMFs) and one residual series, then the nonlinear autoregressive neural network (NARnet) method is used to forecast each IMF component and residue. The predictions of all IMFs and residue are summed to obtain a final forecast for the albedo series. The proposed method was performed on monthly and daily albedo prediction both in snow-free and snowy areas. The results showed that the forecast albedo consists of the MODIS albedo data well, with R² greater than 0.89 and RMSE less than 0.052 for snow-free areas. For snowy areas, the forecasting also performed well during snow cover periods, with R² greater than 0.76 and RMSE less than 0.076. For irregular change periods of snow falling and melting, it is hard to get very high prediction accuracy due to the irregular land surface change. For this problem, more land surface information should be introduced, or adjusting the model over time is necessary.

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.

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 m ³ /m ³ when 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.

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo” approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.