Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (7): 1578-1587.doi: 10.12082/dqxxkx.2020.190207

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Pure Premium Rate-making of Winter Wheat Insurance based on MODIS GPP

WANG Weijia1(), WANG Wen1,*(), YANG Xi1,3, ZHAO Yanyun2   

  1. 1. Center for Spatial Information, School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China;
    2. School of Statistics, Renmin University of China, Beijing 100872, China;
    3. Agricultural Insurance Market Development Department, China Pacific Property Insurance Company Limited, Shanghai 210000, China
  • Received:2019-05-06 Revised:2020-01-20 Online:2020-07-25 Published:2020-09-25
  • Contact: WANG Wen;
  • Supported by:
    Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China(17XNLG09)


Crop insurance is an important measure to reduce farmers' economic loss caused by disasters and to guarantee farmers' basic income of agricultural production. Traditionally, the rate-making of crop insurance is usually based on statistical data collected at administrative level without considering the differences in risk between farms within each administrative unit. Hence, how to obtain the pure premium rate of crop insurance for land parcels within each administrative unit is critical in precision crop insurance. In this study, we calculated the pure premium rate of winter wheat at the farm level in Zhoukou city, Henan, China, based on remote sensing data and insurance actuarial model. We first extracted the GPP data in the growing season from 2005 to 2015 using Moderate Resolution Imaging Spectroradiometer(MODIS) GPP product(MOD17A2) and the administrative boundary of Zhoukou city. We then generated the ratios of winter wheat area in the pre-winter periods from 2004 to 2014 in Zhoukou city at 1 km spatial resolution using Landsat TM/ETM/OLI data. The guaranteed GPP was estimated based on the Bühlmann-Straub reliability model and the real GPP estimates. Second, we calculated the GPP loss rate using the guaranteed GPP and real GPP estimates. Finally, we used the empirical rate method to set the pure insurance premium rate for winter wheat in each land parcel. Our results showed a map of winter wheat pure premium insurance rate at fine scale for Zhoukou city. Compared to the insurance rate calculated using administrative-level statistical data, the insurance rates calculated using remote sensing data were more precision. The fine-scale pure insurance rate map provides an important reference for farm-level winter wheat insurance pricing, making the pricing of crop insurance more reasonable. Our study highlights the integration of remote sensing in determination of crop insurance rates and improvement on the spatial fineness of crop insurance, which could further promote the commercial development of precision crop insurance. Government can hence formulate appropriate crop insurance policies for different farmers, while insurance companies can charge premiums reasonably.

Key words: crop, insurance, remote sensing, pure premium rate, winter wheat, Zhoukou, GPP, spatial fineness