A spatiotemporal fusion algorithm based on Fourier transform is developed to generate daily surface soil moisture with 20 m spatial resolution

Accurate soil moisture data with detailed spatial and temporal resolutions are essential for hydrological modeling, precision agriculture, and climate research. Nonetheless, the intrinsic trade-off between spatial and temporal resolution in remote sensing limits the accessibility of soil moisture products at granular scales. This study presents a spatiotemporal fusion algorithm utilizing Fourier transform (STFFT), integrated with Random Forest (RF), the Water Cloud Model (WCM), and the radiative transfer model (PROSAIL) to create a comprehensive framework for downscaling surface soil moisture (SSM). Employing Sentinel-1 and Sentinel-2 datasets, we downscaled Soil Moisture Active and Passive (SMAP) soil moisture products to generate daily Soil Surface Moisture (SSM) maps at a 20-meter spatial resolution for the study area. The findings indicate that STFFT is more adept at accommodating SSM data marked by significant heterogeneity and scale discrepancies compared to traditional spatiotemporal fusion algorithms. Furthermore, STFFT exhibits computational efficiency and is independent of reference image selection. The amalgamation of RF with WCM and PROSAIL adeptly elucidates the intricate correlations between remote sensing variables and soil moisture; the suggested framework attains precise soil moisture mapping, evidenced by an average correlation coefficient (R) of 0.892 and a root mean square error (RMSE) of 0.034 m³/m³ across diverse land cover types. Compared to benchmark methods that produce an average R of 0.753 and an RMSE of 0.043 m³/m³, STFFT demonstrates markedly enhanced accuracy and robustness, particularly in heterogeneous terrains. This study introduces an improved methodology for producing fine-scale soil moisture products characterized by enhanced spatiotemporal continuity and reliability.