Remote Sensing Applications for Insurance: A Predictive Model for Pasture Yield in the Presence of Systemic Weather
North American Actuarial Journal, 24(2), 333-354.
46 Pages Posted: 27 Jun 2018 Last revised: 15 Sep 2020
Date Written: June 14, 2018
A robust predictive model for crop yield is essential for designing a commercially viable index-based insurance policy. Index insurance for crops is still at a relatively infant stage, and more research and development is needed in order to address a main limitation, which is referred to as basis risk (the mismatch between the loss indicated by the index and the actual loss suffered by the insured). Traditionally ground weather station measurements have been the most common approach used in weather indices, and this approach has often led to high levels of basis risk. Recent advances in satellite-based remote sensing provides new opportunities to use publicly available and transparent “big data,” to potentially make index-based insurance policies more relevant by reducing basis risk. This is the first paper to provide a comprehensive comparison of thirteen Pasture Production Indices (PPI’s), including those developed based on satellite-derived vegetation and biophysical parameter indices using data products from the Moderate Resolution Imaging Spectroradiometer - MODIS. A validation protocol is established, and a unique dataset covering the period from 2002 to 2016 from a network of pasture clip sites in the province of Alberta, Canada is used to demonstrate new applications for insurance based on remote sensing derived data. The results of the satellite-deriverd PPI’s are compared to those PPI’s based on ground weather station data as benchmarks, which to date are the most common design for index insurance. Overall, the satellite-based indices report higher correlations with the ground truth forage yield data compared to the weather station PPI’s. As an example, in 2015 and 2016 the best performing satellite-based PPI’s produce correlations of close to 90%. When considering the whole sample period, the highest overall average correlation is 62.0% for the biophysical parameter PPI based on FPAR 500 meter MODIS, and the lowest overall average correlation is 43.8% for the vegetation PPI based on EVI 250 meter MODIS. Comparatively, the highest correlation reported for the weather station indices is for precipitation at 12.51%. A straightforward, yet effective PCA-based prediction model is introduced in this paper to address the issues of variable selection and dimension reduction in insurance design. This research makes an important contribution to the field of actuarial science and insurance, as it highlights potential new opportunities for insurance design and predictive analytics using large and comprehensive satellite datasets that remain relatively unexplored to date in insurance practice. While agriculture is used as the example here, the research could be extended to other crops, and other areas in the Property & Casualty sector, including for fire and flood.
Keywords: Crop Yield Prediction; Forage Insurance; Remote Sensing; Index-Based Insurance; Principal Component Analysis; Predictive Analytics
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