Integrating Remote Sensing Data in Crop Insurance: A Solution to Data Scarcity in India

Abstract Consistent crop information is vital for the survival of the crop insurance sector, which relies on historical crop data, weather records, meteorological information, and farmers’ details. In India, fragmented, low-quality, and costly data have led to adverse claims ratios, forcing insurance companies to exit the market. This paper proposes an integrated framework that assimilates crop details, quality satellite data, and an actuarial model for crop yield estimation. We use kernel density estimation for risk assessment and emphasize the critical role of bandwidth calculation. Our research indicates that traditional heuristics for bandwidth selection can be misleading. A visualization of the fitted distribution with a frequency histogram can often provide tell-tale signs of an erroneous conclusion from the heuristics. We emphasize the role of the modeller’s judgment in determining the optimal bandwidth that is free from overfitting or over-smoothing. The framework bridges the gap between data and the insurer. The proposed model is of regulatory importance as it solves the issue of missing data and improves risk assessment, which will improve crop insurance market penetration and farmers’ participation and thereby promote stability in the crop insurance sector.