An Evaluation of Soil Moisture Retrieval Using Machine Learning Methods: Application in Arid Regions of Tunisia

Soil Moisture (SM) is an important parameter for many environmental applications. In recent years, different Machine Learning (ML) methods have been developed in order to estimate the SM. The aim of this paper is to evaluate three ML methods based methodology to estimate the SM, namely Artificial Neural Networks (ANN), Random Forest Regression (RFR) and extreme Gradient Boosting (XGBoost). The used input data for the three ML methods include the in situ measurements SM, the Backscatter coefficient in two modes polarizations (σ°yy and σ°YH) from Sentinel-1A and Normalized Difference Vegetation Index (NDVI) from Sentinel-2A data. The comparative results show that the SM obtained using the ANN, RF and XG Boost algorithms are highly correlated to the in-situ SM, with high Pearson's correlation coefficient R (R
RF
= 0.86, R
ANN
= 0.75, R XGBoost= 0.77) respectively and low RMSE (RMSE
RF
= 1.09%, RMSE
ANN
= 1.49%, RMSEx
GBoost
= 1.39%) respectively.