地球信息科学学报

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (7): 1578-1587.doi: 10.12082/dqxxkx.2020.190207

• 遥感科学与应用技术 • 上一篇    下一篇

基于MODIS GPP产品的冬小麦保险费率厘定方法研究

王维佳1(), 王汶1,*(), 杨熙1,3, 赵彦云2   

  1. 1.中国人民大学环境学院地理空间信息研究中心,北京 100872;
    2.中国人民大学统计学院,北京 100872;
    3.中国太平洋财产保险股份有限公司农险市场发展部,上海 210000
  • 收稿日期:2019-05-06 修回日期:2020-01-20 出版日期:2020-07-25 发布日期:2020-09-25
  • 通讯作者: 王汶 E-mail:weijiaw@ruc.edu.cn;wenw@ruc.edu.cn
  • 作者简介:王维佳（1992— ）,男,河北邢台人,硕士生,研究方向为遥感与地理信息系统的应用。E-mail:weijiaw@ruc.edu.cn
  • 基金资助:
    中国人民大学科学研究基金重大规划项目“互联网统计学研究”(17XNLG09)

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 E-mail:weijiaw@ruc.edu.cn;wenw@ruc.edu.cn
  • Supported by:
    Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China(17XNLG09)

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摘要：

农作物保险是国内外减少灾害造成的种植户经济损失,保障农民基本生产收入的重要手段。国内传统的农作物保险费率是基于行政单元的统计数据厘定的,忽略了行政单元内部灾害的空间风险差异,因此如何获得行政单元内部农户级农作物纯保险费率,成为精细化农作物保险的关键问题。本文针对农户级的冬小麦纯保险费率,以河南省周口市为实验区,利用2005—2015年MODIS MOD17 A2 GPP总初级生产力数据产品生成2005—2015年冬小麦生长季的GPP数据,同时利用Landsat5/7/8 TM/ETM/OLI数据计算2005—2015年公里级的冬小麦种植面积比。通过Bühlmann-Straub模型和经验费率法厘定得到2016年实验区基于格网单元的冬小麦纯保险费率。研究表明：遥感数据可以为农作物保险空间精细费率厘定提供数据保障,利用遥感数据可以得到公里级格网单元的冬小麦纯保险费率。将利用遥感数据得到的农作物纯保险费率用于农作物保险中,提高了农作物保险的空间精细水平,可以进行基于地块的空间差异化农户投保,有利于政府针对不同农户制定合理的农作物保险政策,保险公司合理的收取保费。

关键词: 农作物, 保险, 遥感, 纯保险费率, 冬小麦, 周口, GPP, 空间精细化

Abstract:

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