Climate Risk Intelligence Index (CRII): An AI-Augmented Satellite-Based Climate Vulnerability Assessment Framework for India

Climate change has emerged as a major global challenge affecting environmental sustainability, hydrological stability, urban resilience, and ecosystem health. Conventional climate vulnerability assessment systems primarily rely on static rule-based frameworks and isolated environmental indicators, limiting contextual interpretability and adaptive reasoning across geographically diverse regions. This paper proposes the Climate Risk Intelligence Index (CRII), an AI augmented satellite-based climate vulnerability assessment framework for large-scale climate intelligence generation across India. The proposed system integrates multi-source Earth observation datasets including MODIS Land Surface Temperature (LST), MODIS NDVI vegetation indices, GHSL urban built-up datasets, ERA5 rainfall reanalysis data, and satellite derived hydrological flood exposure indicators from the JRC Global Surface Water dataset to compute a composite climate vulnerability index. India was spatially partitioned into approximately 3,840 geospatial grid cells for localized environmental risk analysis using grid-wise zonal statistical extraction. In addition to geospatial climate-risk computation, the framework incorporates a Large Language Model (LLM)-based reasoning layer powered by the Groq API and the llama-3.1-8b-instant model for contextual environmental interpretation, dominant-risk identification, and interactive climate intelligence generation. Experimental analysis demonstrated consistent climate-risk characterization across flood-prone coastal regions, metropolitan urban clusters, ecologically sensitive landscapes, and high-temperature arid zones. The AI reasoning module achieved low-latency inference responses of approximately 1–3 seconds per query while improving interpretability and contextual explanation quality compared to earlier rule-based approaches. The proposed CRII framework demonstrates the potential of integrating remote sensing, geospatial analytics, and explainable AI-driven reasoning for scalable climate vulnerability assessment and environmental decision support systems.