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This study presents a GIS-based spatial analysis approach to assess and enhance solar energy planning in India, focusing on four diverse states—Rajasthan, Andhra Pradesh, Odisha, and Uttar Pradesh. These regions were selected due to their varied climatic, geographic, and socio-economic characteristics, which together provide a comprehensive framework for evaluating solar energy potential. The study employs advanced remote sensing techniques integrated with Google Earth Engine (GEE), a cloud-based geospatial analysis platform, to examine decadal changes in land use, environmental conditions, and surface characteristics from 2014 to 2024. GEE was instrumental in processing, modifying, and validating large volumes of satellite imagery, enabling efficient and accurate spatial assessments over broad geographic scales. The analysis identifies key spatial patterns such as increasing urbanization, declining vegetation in some regions, and rising surface temperatures, all of which influence land suitability for solar energy deployment. Arid and semi-arid zones with low ecological sensitivity and minimal water presence were found to be most favourable for ground-mounted solar installations, while certain stable water bodies in Andhra Pradesh and Odisha present potential for floating solar projects. Based on the spatial trends and observed transitions, the research recommends the integration of continuously updated geospatial data in solar infrastructure planning, the adoption of location- specific technologies such as agrivoltaics and floating solar systems, and the involvement of local communities through improved access to financing, training, and participatory planning. These strategies not only enhance the technical efficiency of solar projects but also align with broader sustainability and equity goals. Overall, the study demonstrates how geospatial intelligence and cloud computing tools like GEE can significantly improve renewable energy planning by enabling more data-driven, climate-resilient, and socially inclusive decision- making across diverse Indian landscapes.
