Abstract:

Based on the MOD13A2 time series remote sensing data and daily meteorological data from 99 meteorological stations in Shaanxi province, the winter wheat planting area in Shaanxi was extracted using the EVI differencing method. The remote sensing index most correlated with winter wheat yield was selected and combined with meteorological indicators for agricultural meteorological disasters such as late spring frosts, drought, continuous rainy days, and hot dry winds during the winter wheat growth period to construct a comprehensive remote sensing index model to cover the agricultural meteorological disaster risk throughout the entire winter wheat growth period. A comprehensive remote sensing index insurance for winter wheat was designed based on the optimal yield prediction model. Using distribution fitting and Monte Carlo simulation methods, the claim threshold and actuarial pure premium rate for 10770 grid cells of winter wheat comprehensive remote sensing index insurance were calculated, and claim threshold maps and actuarial pure premium rate maps were generated. The results showed that: (1) using the EVI differencing method to extract the winter wheat planting area, different time periods and thresholds were used in different regions to achieve higher accuracy, with a correlation coefficient of 0.997 between the extracted area at the county level and the actual planting area in 2020, with an average absolute error of 524.9ha. (2) The EVI on the 65th and 81st days of 2000−2020 and their maximum values were highly correlated with the single yield of Shaanxi winter wheat, with a highest county-level average correlation coefficient of 0.692. (3) After integrating the meteorological indicators of drought, continuous rainy days, and late spring frosts, the optimal comprehensive remote sensing index model predicted a correlation coefficient of 0.837 between simulated and actual yield, with an R² of 0.602 for the optimal comprehensive remote sensing index model. (4) The risk of winter wheat planting in some areas of Guanzhong and southern Shaanxi was relatively low, with an average yield loss rate of less than 2%, while the risk of winter wheat planting at the junction of the Wei river and the Yellow river was higher, with an average yield loss rate of over 4%, and in other areas, the risk of winter wheat planting ranged between these two extremes. There were significant spatial differences in winter wheat yield and planting risk below the county level. Improving the spatial accuracy of calculating claim thresholds and actuarial pure premium rates can ensure that high-yield and low-yield areas have equal compensation opportunities, avoid moral hazards caused by paying more than actual losses, match rates with actual planting risks, increase rate fairness, enhance the willingness of low-risk areas to be insured, and reduce adverse selection.

Key words: Remote sensing index insurance, Winter wheat, Rate setting, EVI