An explainable earth informatics framework for automated urban water body mapping and seasonal monitoring using multi-temporal sentinel-2 imagery

Accurate extraction and monitoring of urban water bodies are essential for sustainable water resource management and environmental planning. This study presents a robust Earth observation framework that integrates the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), adaptive thresholding techniques, and deep learning models for automated water body mapping and seasonal analysis using Sentinel-2 imagery. Six thresholding methods, namely Otsu, Li, Triangle, Minimum, Mean, and Yen, were evaluated, and the extracted water bodies were processed in QGIS. Tile-based segmentation was performed using Fully Convolutional Neural Networks (FCNN), U-Net, U-Net-ResNet50, and U-Net-ResNet101. Experimental results demonstrate that Otsu and Li thresholding provide the most reliable water delineation, while NDWI consistently outperforms MNDWI in segmentation accuracy. Although FCNN and U-Net achieved excellent performance for lakes in Bengaluru, their generalization across geographically diverse Indian lakes was limited. The proposed U-Net-ResNet101 model exhibited superior robustness, achieving 99.0% training and test accuracy with near-perfect precision, recall, and F1-score across multiple regions. Grad-CAM visualizations and comprehensive ablation studies validated the effectiveness of the proposed architecture. Seasonal predictions closely matched historical water dynamics, demonstrating the framework’s potential for scalable urban water monitoring, environmental informatics, and decision support in rapidly urbanizing regions.