Transformative crop insurance solution with big earth data: Implementation for potato in India

A compsite index of crop health called ‘CHF’, synthesized from a set of crop health indices derived from multitemporal satellite datasets and weather data, drives the new crop insurance model reported in this paper. Satellite-based crop mapping, satellite and weather data analysis for generating crop health indicators, smartphone-based field data collection and analysis, composite index generation and insurance loss assessment are the major elements of this crop insurance model. [Display omitted] •An innovative crop insurance scheme using big earth data from satellites, weather stations and smartphones is presented.•A compsite index of crop performance is developed using multiple indicators observed throughout the crop season.•The existing ‘Area-yield index’ scheme is replaced by an ‘Area-crop performance index’ scheme.•The scheme was implemented successfully for potato crops in India.•The solution proposes a paradigm shift in the crop insurance sector with technology-driven index-insurance schemes. Crop insurance has become an indispensable risk management tool in the agricultural sector because globally crops are being exposed to multiple hazards. The lack of reliable crop yield data has impacted the sustenance of area-yield crop insurance schemes. Index-based insurance, which links pay-outs to crop performance proxies rather than measured losses, is being explored to improve the effectiveness of crop insurance contracts. This paper presents an innovative crop insurance scheme that has replaced the existing ‘area-yield’ approach using bias-prone crop yield estimates with the ‘area-crop performance approach’ using objectively measured satellite indices. Satellite-based crop mapping, satellite and weather-based crop health indicators, field data collection and analysis, composite index generation, and insurance loss assessment are major tasks in the project. Data of Sentinel-1 and 2 satellites, weather datasets and mobile app-based field data from transplantation to harvesting of the crop constituted a huge repository of the database in this project. Metrics derived from established satellite indices, such as NDVI, LSWI and Backscatter, along with weather indices, were synthesized into a composite index of crop performance called Crop Health Factor (CHF). The input data matrix of the CHF model included eight input indicators. After data normalization, weights for these indicators were generated using the entropy technique, a proven method of information me