Remote Sensing and Machine Learning Approaches for Hydrological Drought Detection: A PRISMA Review

Hydrological drought poses a significant threat to water security and ecosystems globally. While remote sensing offers vast spatial data, advanced analytical methods are required to translate this data into actionable insights. This review addresses this need by systematically synthesizing the state-of-the-art in using convolutional neural networks (CNNs) and satellite-derived vegetation indices for hydrological drought detection. Following PRISMA guidelines, a systematic search of studies published between 1 January 2018 and August 2025 was conducted, resulting in 137 studies for inclusion. A narrative synthesis approach was adopted. Among the 137 studies included, 58% focused on hybrid CNN-LSTM models, with a marked increase in publications observed after 2020. The analysis reveals that hybrid spatiotemporal models are the most effective, demonstrating superior forecasting skill and in some cases achieving 10–20% higher accuracy than standalone CNNs. The most robust models employ multi-modal data fusion, integrating vegetation indices (VIs) with complementary data like Land Surface Temperature (LST). Future research should focus on enhancing model transferability and incorporating explainable AI (XAI) to strengthen the operational utility of drought early warning systems.