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@article{184462,
        author = {Nagaraja K and Ramanjineya K},
        title = {ADVANCEMENTS IN REMOTE SENSING AND GIS FOR GROUNDWATER AND WATER RESOURCE MONITORING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {6},
        number = {6},
        pages = {237-249},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184462},
        abstract = {Groundwater resources are increasingly threatened by population growth, urbanization, agricultural demand, and climate variability, making effective monitoring and management a global priority. Traditional hydrogeological methods, though reliable, are constrained by limited spatial coverage and high costs. Over the past two decades, advancements in remote sensing (RS) and geographic information systems (GIS) have transformed groundwater and water resource monitoring by providing scalable, cost-effective, and multi-temporal datasets. This review consolidates key developments up to 2021, highlighting the role of satellite missions, radar and LiDAR technologies, GIS-based modeling, and machine learning approaches.
One of the most significant contributions has been the Gravity Recovery and Climate Experiment (GRACE) mission, which enabled large-scale groundwater storage anomaly detection and depletion mapping in stressed regions such as northern India and California. Complementing GRACE, the Soil Moisture Active Passive (SMAP) mission improved understanding of soil moisture dynamics and recharge estimation, while optical indices such as NDWI were used for vegetation water content and irrigation monitoring. Interferometric Synthetic Aperture Radar (InSAR) has become indispensable for detecting aquifer-system deformation and land subsidence linked to excessive pumping, with applications across Mexico, California, and Asia. At finer spatial scales, Light Detection and Ranging (LiDAR) datasets have supported recharge zone delineation and hydrogeomorphic studies, particularly in complex terrains. GIS has served as the integrative platform for these datasets, enabling groundwater potential mapping through multi-criteria decision analysis (MCDA). Techniques such as frequency ratio, certainty factor, and weights-of-evidence have been widely applied, while more recent works integrated machine learning and data mining algorithms to enhance predictive accuracy. Hybrid approaches linking RS datasets with hydrological models further improved aquifer monitoring in data-scarce regions.
Comparative analysis shows a progression from large-scale monitoring in the early 2000s (GRACE, GIS-based mapping), to detailed local studies in the 2010s (InSAR, LiDAR), and finally to advanced hybrid approaches combining RS, GIS, and machine learning after 2016. Despite these advances, challenges persist, including GRACE’s coarse resolution, InSAR’s atmospheric sensitivity, and the lack of robust validation for machine learning models. Moreover, many scientific outputs have yet to be fully integrated into water governance and management frameworks. Future efforts must focus on multi-sensor data fusion, downscaling techniques, UAV-based surveys, and stronger linkages between scientific tools and policy to ensure sustainable water resource management.},
        keywords = {Groundwater resources, Remote sensing (RS), Geographic Information Systems (GIS), GRACE, SMAP, NDWI, InSAR, LiDAR, Groundwater monitoring, multi-criteria decision analysis (MCDA), Machine learning, Hydrological modeling, Aquifer deformation, Land subsidence, Recharge estimation, Multi-sensor data fusion, UAV-based monitoring, Water governance.},
        month = {September},
        }