An integrated methodology for surface soil moisture estimating using remote sensing data approach

The present study aimed to propose an operational approach for estimating surface soil moisture from Moderate Resolution Imaging Spectroradiometer (MODIS) data by considering diverse environmental variables such as Normalized Difference Vegetation Index (NDVI), land surface temperature (Ts), evapotranspiration, topographic parameters (elevation and aspect) and soil texture (clay, loam and silt). A soil moisture index (SMI) derived from NDVI-Ts space is combined to all other variables, based on stepwise multiple regression, to develop a new SSMC model. Performance of this model was assessed using field-measured data of SSM. Accuracy was performed by the k-fold cross validation method, it showed a R-2 (coefficients of determination) of 0.70, RMSE of 1.58% and unRMSE of 0.5%. In addition, the results of the developed model were compared with another soil moisture model SMM proposed in the irrigated perimeter of Tadla (Morocco), and revealed that the established model provided effectiveness results in the study areas.

Automated summary

This study proposed an operational approach to estimate surface soil moisture from MODIS data, considering various environmental variables. A new SSMC model was developed using stepwise multiple regression, which showed a R-2 of 0.70, RMSE of 1.58% and unRMSE of 0.5% through k-fold cross validation. The model's effectiveness was demonstrated in the study areas compared to another soil moisture model SMM proposed in Morocco.