Evaluation of SMAP and CYGNSS Soil Moistures in Drought Prediction Using Multiple Linear Regression and GLDAS Product

Drought is a devastating natural hazard and exerts profound effects on both the environment and society. Predicting drought occurrences is significant in aiding decision-making and implementing effective mitigation strategies. In regions characterized by limited data availability, such as Southern Africa, the use of satellite remote sensing data promises an excellent opportunity for achieving this predictive goal. In this article, we assess the effectiveness of Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) soil moisture data in predicting drought conditions using multiple linear regression-predicted data and Global Land Data Assimilation System (GLDAS) soil moisture data.