Multi-Index Assessment and Machine Learning Integration for Drought Monitoring Using Google Earth Engine

This study advances multisensor remote sensing data fusion integrating optical (Sentinel-2, MODIS), thermal (LST), and hydrological (SMAP) sensors with climate datasets to evaluate soil moisture dynamics at five depths (0–50 cm) across nine agricultural zones (October 2021–September 2023), leveraging AI and machine learning to address data quality challenges in heterogeneous sensor inputs. Our novel AI-driven approach improved data fusion accuracy by 27% compared to conventional methods, enabling more reliable drought detection even in areas with persistent cloud cover and sensor inconsistencies. Using Google Earth Engine for spatiotemporal fusion and random forest classification for feature optimization, we demonstrate the superiority of fused multisensor indices, with vegetation condition index and normalized vegetation-soil water index achieving the highest accuracy (r = 0.56–0.59) at 10–40 cm depths, while single-sensor optical indices underperformed at 50 cm (r < 0.30). Temporal analysis revealed cumulative drought signals in standardized precipitation evapotranspiration index (3–6-month lag) and rapid surface responses in temperature condition index, emphasizing the need for adaptive sensor fusion. Land cover changes included an 18.5% reduction in surface water and a 3714.6 km² expansion of bare ground in 2023, correlating with standardized precipitation index-based drought severity (29.8% –47.3% affected area). The framework’s AI-driven error correction and multisensor synergy provide a scalable model for drought applications, such as ecosystem resilience monitoring (integrating thermal and optical analysis) and hydrological modelling (fusing soil moisture, precipitation, and vegetation datasets). By resolving sensor inconsistencies and enhancing reliability in complex environments, this work underscores the broader relevance of multisensor fusion for drought vulnerability assessments, where land-sea interactions demand integrated sensor networks and machine learning to mitigate ecological and climatic risks.