Integrating Remote Sensing and GIS for Groundwater Potential Zone Delineation: A Multi-Criteria Decision Weighted Overlay Analysis Approach

Abstract

Groundwater is a vital resource for meeting domestic and agricultural water demands in the tropical region of Thiruvattar Firka, Kanyakumari District, Tamil Nadu, India, which covers an area of approximately 67.64 km². This study aimed to delineate groundwater potential zones (GWPZs) using an integrated Remote Sensing (RS) and Geographic Information System (GIS) approach. The methodology involved the preparation of thematic layers, including lithology, lineament density, slope, soil type, land use/land cover, geomorphology, drainage density, and rainfall, derived from satellite imagery and ancillary data. These layers were assigned weights based on their relative hydrogeological importance and integrated using a Multicriterial Weighted Overlay Index (MWOI) model. Inverse Distance Weighting (IDW) interpolation was applied to generate spatial groundwater potential maps. The resulting groundwater potential zonation classified the study area into three zones: high, moderate, and low. The analysis revealed considerable spatial variation, with moderate groundwater potential zones occupying 74.63% of the area, followed by high potential zones covering 13.43%, and low potential zones accounting for 11.94%. High-potential zones are mainly associated with weathered and fractured formations, gentle slopes, low drainage density, and favourable land-use conditions, whereas low-potential zones correspond to areas characterised by steep slopes, high runoff, and relatively impermeable lithological units. The GWPZ map was validated using field observations, which demonstrated a strong agreement between the predicted zones and observed groundwater conditions, thereby confirming the robustness of the adopted methodology. The study demonstrates that RS–GIS, integrated with the MWOI model, is an effective tool for assessing groundwater potential. Future research may focus on incorporating time-series groundwater level data, machine learning techniques, and climate variability analyses to improve prediction accuracy and support sustainable groundwater management and artificial recharge planning.