Abstract Monitoring surface water dynamics is critical for understanding hydrological responses to climatic variability and for guiding sustainable water resource management. This study investigates the spatiotemporal changes in surface water bodies across the Jammu District of India from 2000 to 2020 using multi-temporal Landsat 5 TM and Landsat 8 OLI imagery. Four spectral indices Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEI), and Water Ratio Index (WRI) were applied to extract water bodies, with classification refined through both fixed and Otsu’s automated thresholding techniques. The study integrates 50 ground-truth validation points for each benchmark year to assess classification accuracy using confusion matrices. Results reveal a significant contraction in surface water extent, particularly after 2011, strongly correlated with declining rainfall and rising temperatures (r = 0.91 and r = −0.85, respectively). Among the indices, AWEI and MNDWI showed the highest detection accuracy and stability across heterogeneous landscapes. The methodology's use of multi-index consensus mapping and threshold adaptation enhances robustness, while limitations related to spatial resolution underscore the need for integrating higher-resolution satellite and radar data in future work. This research highlights the urgent need for climate-resilient water governance, especially in the semi-arid Kandi belt, and provides critical insights aligned with Sustainable Development Goals (SDGs) 6 (Clean Water and Sanitation), 13 (Climate Action), and 15 (Life on Land). The findings serve as a baseline for future water resource planning, surface water rejuvenation efforts, and evidence-based policy interventions in climate-sensitive regions.
Bhatt et al. (Tue,) studied this question.