Assessing machine learning algorithms for retrieving soil moisture from a semi-arid region using a synthetic database

Recently, there has been growing interest in achieving the Sustainable Development Goals (SDGs) due to the increasing global population and the observed changes in Earth’s natural environment. In this study, we aim to contribute to the achievement of three Sustainable Development Goals (SDGs), namely SDG2, SDG13, and SDG15, by providing an accurate retrieval of soil moisture (SM). The selection of an accurate SM retrieval method for agricultural semi-arid regions is challenging. Therefore, the main goal of this study is to evaluate different machine learning (ML) algorithms to identify the most powerful algorithm capable of estimating SM conditions in those areas based on the combined use of radar and optical data. The different ML algorithms are implemented using a synthetic database obtained by a calibrated Water Cloud Model (CWCM). Statistical evaluations are performed using five corn fields in Huamantla, Central Mexico. The results demonstrate that the Random Forest Regressor (RFR) achieves the lowest error values, thus outperforming the other models. In order to improve the synthetic dataset and forecast accuracy, we consider the integration of surface-scattering models with the WCM in the next step of this research.