Integrated Remote Sensing and GIS for Groundwater Potential Assessment

Integrated remote sensing and geographic information systems (GIS) have emerged as indispensable tools for delineating groundwater potential zones at regional to continental scales. By combining multispectral, radar and digital elevation data with geologic, hydrologic and climatic maps, researchers generate thematic layers—such as slope, lineament density, soil texture and land-use—that influence recharge and storage. These layers are then weighted and synthesised through multi-criteria decision analysis or machine-learning ensembles to produce spatially continuous maps of groundwater potential. Such approaches facilitate rapid, cost-effective targeting of exploration wells, support sustainable aquifer management and guide land-use planning in both arid and humid environments. The fusion of statistical methods, expert-driven models and advanced classifiers enhances predictive accuracy, while ongoing validation against well-yield and geophysical measurements ensures practical applicability. By integrating data from satellites and field surveys, this framework offers a globally transferable methodology for assessing the distribution and dynamics of subsurface water resources.

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Integrated Remote Sensing and GIS for Groundwater Potential Assessment publication trend

The graph below shows the total number of articles in integrated remote sensing and gis for groundwater potential assessment across all publications each year (not limited to Nature Index journals).

Technical terms

Remote sensing: Acquisition of information about Earth’s surface via satellite or airborne sensors, used to derive environmental variables relevant to groundwater recharge.

Geographic Information System (GIS): Computer-based framework for capturing, storing, analysing and visualising spatial data across multiple thematic layers.

Multi-Criteria Decision Analysis (MCDA): A systematic approach that evaluates and ranks alternatives—in this case, groundwater potential factors—by assigning weights according to their relative importance.

Analytic Hierarchy Process (AHP): A structured MCDA technique that uses pairwise comparisons to derive numerical weights for factors influencing groundwater occurrence.

Ensemble modelling: Combination of multiple predictive algorithms—such as maximum entropy and frequency ratio—to enhance the accuracy and robustness of groundwater recharge mapping.

Groundwater Potential Zone (GPZ): A spatially defined area classified according to its likelihood of yielding economically viable groundwater based on integrated data and modelling.

References

1. Modeling of geophysical derived parameters for groundwater potential zonation using GIS-based multi-criteria conceptual model. Applied Water Science (2024).
2. Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models. Scientific Reports (2021).
3. Mapping Groundwater Potential Zones Using a Knowledge-Driven Approach and GIS Analysis. Water (2021).
4. Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions. Sustainability (2022).