A comparison between the use of machine learning techniques and the water cloud model for the retrieval of soil moisture from Sentinel-1A and Sentinel-2A products

ABSTRACTIn this letter, we aim to reveal the most accurate method for soil moisture (SM) retrieving. Based on the synergistic use of Sentinel-1 (S-1) and Sentinel-2 (S-2), the most popular Machine Learning (ML) techniques in addition to the Water Cloud Model (WCM) are evaluated. Experiments were carried out at two different sites Dar Dhaoui and Chammakh, located in an arid region in the south of Tunisia between 2015 and 2017. The input data includes the in-situ measurements, dual-polarized S-1A backscattering coefficients (σ VV∘ and σ VH∘), and Normalized Difference Vegetation Index (NDVI) derived from S-2A images. The experiment results revealed that Random Forest Regression (RFR), Convolutional Neural Network (CNN), and WCM described accuracy almost identical to Pearson correlation (r) 0.85 where CNN is the best over them. Teacher-Student (TS) with r 0.96 and Root-Mean-Square Error (RMSE) 0.80% remains the most accurate method for SM retrieving. Disclosure statementNo potential conflict of interest was reported by the authors.