An improved change detection method for high-resolution soil moisture mapping in permafrost regions

Soil moisture plays a crucial role in understanding the hydrological cycle and the ecological environment. This research presents an improved change detection method that leverages time series data from Sentinel-1 radar and Sentinel-2 optical sensors (2019–2021) to estimate surface soil moisture. The response of backscatter to soil moisture in bare soil was expressed in a logarithmic form, and the influence function of the normalized difference vegetation index (NDVI) on the backscatter difference was established for various vegetation-covered surfaces. Therefore, the impact of vegetation on backscatter is effectively mitigated, and the resulting change in backscatter relative to bare soil conditions can be obtained. An empirical function is subsequently formulated to ascertain the reference values of soil moisture in each pixel. The retrieval of soil moisture is demonstrated in the Wudaoliang permafrost region of the Qinghai-Tibet Plateau and validated against ground measurements. The retrieval results of the improved change detection method exhibit correlation coefficients ranging from 0.672 to 0.941, with root mean squared errors (RMSE) ranging from 0.031 [Formula: see text] to 0.073 [Formula: see text]. Compared to the Soil Moisture Active Passive (SMAP) 9-km product, our new method demonstrates higher correlation (0.898 vs. 0.867) and lower RMSE (0.037 [Formula: see text] vs. 0.044[Formula: see text]). The soil moisture retrieved from Sentinel shows a strong correlation with the SMAP 9-km soil moisture in the time series, thereby providing a better representation of the region’s soil moisture heterogeneity. Our method demonstrates the feasibility of combining Sentinel-1 and 2 for high-resolution (100 m) soil moisture mapping in permafrost regions.