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Precise information on soil moisture (SM) and crop water dynamics is essential for hydrological and agricultural applications. The SM measurements from SAR data are influenced by varying degree of soil-water binding and thus affect the reliable assessment of vegetation dynamics. Hence, in this present study, the six SAR-based SM descriptors capable of representing bound and free water in the soil and the corresponding in-situ SM measurements were used to train the four state-of-the-art machine learning regression (MLR) approaches, namely, random forest regression (RFR), gradient boosting regression Tree (GBRT), support vector regression (SVR), and gaussian processes regression (GPR) to retrieve enhanced surface soil moisture ( SSMenh) estimates. The  SSMenhresults were used in the Water Cloud Model (WCM) to retrieve the PWC (plant water content). Lastly, the crop health schema (CHS) was proposed considering  SSMenhand PWC, to examine the health dynamics of the cotton and sorghum crops. The results demonstrated that the GBRT model (R 2  = 0.91, RMSE = 0.004 m3m − 3, MAE = 0.021) outperformed the other ML models in retrieving SM. The PWC (R 2  = 0.82, RMSE = 0.014, MAE = 0.019) from WCM and CHS schema (F1 score = 0.86, Kappa = 0.79) have shown good statistical agreement with the field observations. The present study demonstrated the scope of  SSMenhproduct in investigating vegetation health response to water stress from single-date dual-pol SAR imagery.

Appraising the crop health response to water stress from enhanced crop and soil water estimates using SAR data and machine learning approaches