Retrieving Corn Gravimetric Water Content Based on the Water Cloud Model Using Sentinel-1 and Radarsat-2 Imageries

Vegetation gravimetric water content (GWC) is a key variable in land-atmosphere interactions and plays an important role in agriculture, climate, and hydrology. Aiming at vegetation GWC estimating, this work proposed a dual-channel (co- and cross-polarization) method based on the water cloud model (WCM-based DCM) with an explicit inversion expression. The WCM-based DCM was used to derive corn GWC during the HiWATER2012 and Baoding2015 experiments using RADARSAT-2 and Sentinel-2 SAR imageries. Validation against ground-measured corn GWC shows that the correlation coefficient (R) between the model-estimated and ground-measured corn GWC is 0.514, with a root mean square error (RMSE) of 0.0901 kg/kg and a mean absolute error (MAE) of 0.075 kg/kg. The results indicate that the proposed WCM-based DCM can estimate corn GWC with acceptable accuracy and is a practical way to retrieve GWC over regional areas.