Paddy crop insurance using satellite-based composite index of crop performance

The well-known area-yield crop insurance contract, guaranteeing a certain percent of normal yield over an insured area, is losing its effectiveness due to poor quality yield data. This paper introduces a "satellite-derived crop health index" as an alternative to yield data in such an insurance model. The new approach was implemented in the 2020 crop season, covering 3.5 million ha of paddy crop over 3200 Insurance Units in the West Bengal state of India. Data of Sentinel satellites, gridded weather data, and Mobile-app based field data were analyzed to generate paddy crop map and crop health indicators, namely NDVI, LSWI, Backscatter and FAPAR for the current (2020) and past years (2016-2019). Using the metrics derived from these indices and entropy technique, a composite index of crop performance called Crop Health Factor (CHF), ranging from 0-1, was generated. Deviations of CHF and yield between the years showed good correlation. The CHF data has successfully replaced the yield data for indemnity and pay-out assessments in 2020, as notified by the Government in advance. Thus, this project makes an entry point for developing remote sensing based transformative crop insurance solutions to enhance risk transfer in agriculture which perhaps the most plausible way forward.