| Summary: | 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 presented.) to 0.073 (Formula presented.). 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 presented.) vs. 0.044 (Formula presented.)). 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. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This study was jointly supported by the National Key Research and Development Program of China [No. 2022YFB3903302] and the National Natural Science Foundation of ...
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