Welcome to Smart Agriculture

Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 139-152.doi: 10.12133/j.smartag.2020.2.3.202006-SA002

• Information Processing and Decision Making • Previous Articles    

Application of Satellite Remote Sensing Yield Estimation Technology in Regional Revenue Protection Crop Insurance: A Case of Soybean

CHEN Ailian1,2(), LI Jiayu3, ZHANG Shengjun3, ZHU Yuxia1,2(), ZHAO Sijian1,2, SUN Wei1,2, ZHANG Qiao1,2   

  1. 1.Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2.Key Laboratory of Agricultural Information Service Technology, Ministry of Agriculture and Rural Agriculture, Beijing 100081, China
    3.China Pacific Property Insurance Company Limited Shandong Branch, Jinan 250001, China
  • Received:2020-06-05 Revised:2020-09-18 Online:2020-09-30
  • corresponding author: ZHU Yuxia, E-mail: 
  • About author:CHEN Ailian, E-mail: chenailian@caas.cn
  • Supported by:
    Chinese Academy of Agricultural Sciences Institute of Agricultural Information Science and Technology Innovation Project (CAAS-ASTIP-2016-AII); Youth Exploration Research Project (2019JKY022)


In recent years, revenue protection crop insurance is an innovative insurance that has been prioritized in China. But it still lacks the support of the third-party yield data around crop harvest time. Aiming to provide objective yield data for revenue protection crop insurance, satellite remote sensing production estimation technology was employed to discuss its application mode and applicability. Taking the soybean revenue protection insurance in Jiaxiang county, Shandong province as an example, we first extracted soybean planting plots, calculated vegetation index and crop physiological parameters based on Sentinel-2 satellite images in 2018 . Combining to TRMM precipitation data from TRMM precipitation-monitoring radar satellite and MODIS land surface temperature data from Terra/Aqua satellite and site yield data, we established a multi-parameter linear regression model, and estimated soybean yield per unit area. The crop extraction results showed that the soybean planting area in the study area was 1.24 km2, which was in good agreement with the 1.27 km2 reported by the local agricultural bureau; and with using the actual measurement plots, the remote sensing identification accuracy of the planting distribution plots reached 90%. The yield estimation results showed that the NDVI of the soybean pod stage on August 23 and the leaf area index of the soybean seedling stage on September 7 explained the soybean yield per hectare the best, and the average estimated yield of the whole area was 244,500 kg/m2, which reflects the severely affected agricultural conditions, comparing to 299,800 kg/km2 in previous years.The regression coefficient between the estimated yield data and the measured data reached 0.92, which meet the application needs.With this results, the estimated yield of different towns can be summarized, and the regional yield was present, and was used as the real yield in 2018, multiplying with the average soybean price around October 11 to December 10 from the local price bureau, the real revenue was obtained. Compared the real revenue to the expected revenue in the contract of insurance, the claims work was decided. The results indicated that the Sentinel-2 satellite data could be used to identify the soybean planting distribution in the study area accurately, and to complete the yield estimation as soon as one week after the soybean harvest, which could guide the insurance company's claims work. The whole methodology is capable of aiding the claims work in revenue protection crop insurance.

Key words: agricultural insurance, regional insurance income, satellite remote sensing, yield estimation, Sentinel-2

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