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Soil Moisture (SM) plays a key role in the energy exchange between the atmosphere and the land surface. Most of the SM products retrieved from satellite remote sensing data are not suitable for drought monitoring and irrigation management in smart agriculture applications due to their coarse spatial resolution. We propose an SM downscaling method named Water Cloud Change Detection (WCCD) that effectively combines the Water-Cloud Model (WCM) and the Change Detection Method (CDM) to downscale the China Land Data Assimilation System soil moisture (CLDAS_SM, 6000-m resolution) product. The WCM is used to retrieve the soil backscattering at a fine spatial resolution by deducting the canopy backscattering from the surface total backscattering, and the linear regression relationship between soil backscattering and CLDAS_SM is established for each pixel at the coarse scale under the assumption that the surface roughness does not change for dozens of days. The performance of the algorithm is tested in an agricultural crop region in Hebei province of China with Sentinel-1 and Sentinel-2 images. The validation results show that the downscaled SM at different spatial resolutions are in good agreement with the in-situ measurements with the correlation coefficient (R) higher than 0.71 and the Root Mean Squared Error (RMSE) lower than 0.042 cm 3 ×cm −3 .

Downscaling CLDAS Soil Moisture Product by Integrating Sentinel-1 and Sentinel-2 Data over Agricultural Area