A Review of Remote Sensing and GIS for Integrated Hydrogeological Characterization and Groundwater Dynamics
DOI:
https://doi.org/10.33003/fjs-2026-1017-5815Keywords:
Remote Sensing-GIS Integration, Multi-Sensor Integration, Aquifer Mapping, Groundwater Recharge, Groundwater Storage Change, Land SubsidenceAbstract
Remote sensing, GIS, field observations, geophysics, and process models are increasingly integrated in groundwater studies, but the hydrogeological significance of multi-sensor integration remains inconsistently defined. This systematic and critical review evaluates four domains: aquifer mapping and hydrogeological characterization (C1), groundwater recharge (C2), groundwater storage change (C3), and groundwater-related land subsidence (C4). A predefined framework separates integration level (I1-I4), sensor diversity (S1-S3+), and hydrogeological coupling (H0-H2), and assesses whether added data streams improve construct validity, uncertainty characterization, independent verification, scale representation, or process understanding. Four Scopus search modules yielded 12,125 records and 10,689 unique identifiers; consolidation produced a master dataset of 10,640 records. Title/abstract screening excluded 3,816 records, leaving 6,824 for eligibility assessment. Final reconciliation retained 4,014 report/study units and excluded 2,810. Detailed Q1-Q12 appraisal and critical inferential synthesis were restricted to 359 primary-source-verified studies; the remaining 3,655 retained records were not treated as fully verified or critically evaluated. The strongest designs moved beyond subjective overlay by integrating independent wells, tracers, hydrogeophysics, geodetic verification, cross-method estimation, uncertainty-aware fusion, and mechanistic coupling between groundwater and hydraulic pressure. Multi-sensor hydrogeology should therefore be judged by inferential gain rather than sensor count. Additional data are scientifically valuable when they provide non-redundant constraints, improve an explicit comparator, extend a key observation dimension, reduce uncertainty, strengthen causal attribution, or bridge a defined gap between process and inference
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