Integrated Remote Sensing and GIS for Decision-oriented Climate-resilient Management of Groundwater-irrigated Cropping Systems: A Review

Abstract

Groundwater irrigation remains a critical component of agricultural production, particularly in regions experiencing climatic variability. However, increasing pressures from temperature extremes, erratic precipitation, and unsustainable extraction threaten aquifer sustainability and long-term food security. This review synthesizes recent advances in the integration of Remote Sensing (RS) and Geographic Information Systems (GIS) for climate-resilient groundwater management in irrigated cropping systems. It examines how satellite-derived indicators, including evapotranspiration, vegetation indices, soil moisture, and groundwater storage anomalies, enable improved spatial and temporal assessment of water resources. The review further evaluates GIS-based frameworks for integrating multi-source datasets to support groundwater potential mapping, irrigation planning, and adaptive decision-making. Emerging approaches such as machine learning, hydrological modeling, and multi-criteria decision analysis are also discussed for their role in predictive irrigation management and recharge zone identification. The synthesis highlights a shift from conventional monitoring toward integrated, decision-oriented systems that enhance water-use efficiency and resilience under climate uncertainty. Despite these advancements, challenges persist, including data limitations, model uncertainty, and institutional constraints. This review adopts a structured literature synthesis approach to identify key advancements, categorize applications, and highlight research gaps. Overall, integrating RS–GIS technologies into decision-support frameworks is essential for sustainable groundwater governance and resilient agricultural systems.