Abstract
Agricultural production is essential for achieving food security (SDG 2) and ensuring sustainable water management (SDG 6). This study investigates the spatial and temporal impacts of the Kaleshwaram Project in Telangana, which aims to irrigate drought-prone areas and enhance resilience to climate change (SDG 13). Using satellite data and geospatial techniques, the research tracks changes in crop intensity from 2018–19 to 2022–23 without relying on time-intensive ground surveys. The methodology involved the use of Sentinel-2 satellite data, analyzed using unsupervised classification (K-means clustering) and Pearson correlation within the Google Earth Engine (GEE) platform. The findings reveal a significant increase in double-cropping and more efficient land utilization during the Rabi season, contributing to sustainable agricultural practices (SDG 12). The study demonstrates high precision in crop intensity classification, achieving a kappa coefficient of 0.91 and an overall accuracy of 0.97. These insights provide valuable guidance for policymakers and stakeholders (SDG 17) in promoting sustainable agricultural development and effective natural resource management.