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 cm ³ /cm ³ , respectively. ALOS-2 SMC retrievals provide higher accuracy (RMSE = 0.076 cm ³ /cm ³ ) 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 cm ³ /cm ³ ) 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.

Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R²) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.

Accurate long-term snow-covered-area mapping is essential for climate change studies and water resource management. The NOAA AVHRR/2 provides a unique data source for long-term, large-spatial-scale monitoring of snow-covered areas at a daily scale. However, the value of AVHRR/2 in mapping snow-covered areas is limited, due to its lack of a shortwave infrared band for snow/cloud discrimination. We simulated the reflectance in the 3.75 µm mid-infrared band with a radiative transfer model and then developed three fractional-snow-cover retrieval algorithms for AVHRR/2 imagery at 1 km and 5 km resolutions. These algorithms are based on the multiple endmember spectral mixture analysis algorithm (MESMA), snow index (SI) algorithm, and non-snow/snow two endmember model (TEM) algorithm. Evaluation and comparison of these algorithms were performed using 313 scenarios that referenced snow-cover maps from Landsat-5/TM imagery at 30 m resolution. For all the evaluation data, the MESMA algorithm outperformed the other two algorithms, with an overall accuracy of 0.84 (0.85) and an RMSE of 0.23 (0.21) at the 1 km (5 km) scale. Regarding the effect of land cover type, we found that the three AVHRR/2 fractional-snow-cover retrieval algorithms have good accuracy in bare land, grassland, and Himalayan areas; however, the accuracy decreases in forest areas due to the shading of snow by the canopy. Regarding the topographic effect, the accuracy evaluation indices showed a decreasing and then increasing trend as the elevation increased. The accuracy was worst in the 4000–5000 m range, which was due to the severe snow fragmentation in the High Mountain Asia region; the early AVHRR/2 sensors could not effectively monitor the snow cover in this region. In this study, by increasing the number of bands of AVHRR/2 1 km data for fractional-snow-cover retrieval, a good foundation for subsequent long time series kilometre- resolution snow-cover monitoring has been laid.

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. FengYun-3 (FY-3) series satellites have accumulated nearly ten 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 FengYun-3B (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 surface freeze/thaw (F/T) information is valuable for hydrological, frost creep and gelifluction/solifluction, and climate prediction studies. Currently, large-scale, high-resolution F/T detection is restricted by low spatial resolution of passive microwave remote sensing sensors or low temporal resolution of synthetic aperture radar (SAR) data. In this study, we propose a new method for detecting daily land surface F/T state at 1 km spatial resolution by combining the Sentinel-1 radar and the Advanced Microwave Scanning Radiometer 2 (AMSR2) with leaf area index (LAI) data. A non-linear relationship is established between the 1 km F/T index from Sentinel-1 with 1 km F/T index from AMSR2 (FTI) and 1 km LAI data. The 1 km FTI is a disaggregation of the 25 km FTI obtained from AMSR2. This non-linear relationship is then applied to daily 1 km FTI and LAI data to predict the 1 km daily F/T index, based on which the F/T status is detected with grid-cell-based F/T thresholds. The overall accuracy of this daily 1 km F/T is more than 88.1% when evaluated with the in situ 5 cm soil temperature over China and Canada. This study is valuable for detecting daily, high-resolution F/T status and is helpful for studies related to disaster and climate prediction.