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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (1): 57-70.doi: 10.12133/j.smartag.SA202201011

• Topic--Crop Growth and Its Environmental Monitoring • Previous Articles     Next Articles

Application Scenarios and Research Progress of Remote Sensing Technology in Plant Income Insurance

CHEN Ailian1,2(), ZHAO Sijian1,2(), ZHU Yuxia1,2, SUN Wei1,2, ZHANG jing1,2, 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
  • Received:2021-10-20 Online:2022-03-30
  • corresponding author: ZHAO Sijian, E-mail:
  • About author:CHEN Ailian, E-mail:chenailian@caas.cn
  • Supported by:
    Basic Research Fund of Chinese Academy of Agricultural Sciences(JBYW-AII-2022-19);Chinese Academy of Agricultural Sciences Innovation Project(CAAS-ASTIP-2019-AII);National Social Science Foundation Project (17CJY033)


Plant income insurance has become an important part of agricultural insurance in China. It has been recommended to pilot since 2016 by Chinese government in several counties, and is now (2022) required to be implemented in all major grain producing counties in the 13 major grain producing provinces. The measurement of yield for plant income insurance in such huge volume urgently needs the support of remote sensing technology. Therefore, the development history and application status of remote sensing technology in the whole agricultural insurance industry was reviewed to help understanding the whole context circumstances of plant income insurance firstly. Then, the application scenarios of remote sensing technology were analyzed, and the key remote sensing technologies involved were introduced. The technologies involved include crop field plot extraction, crop classification, crop disaster estimation, and crop yield estimation. Research progress of these technologies were reviewed and summarized,and the satellite data sources that most commonly used in plant income insurance were summarized as well. It was found that to obtain a better support for a development of plant income insurance as well as all crop insurance from remote sensing communities, issues existed not only in the involved remote sensing technologies, but also in the remote sensing industry as well as the insurance industry. The most two important technical problems in the current application scenario of planting income insurance are that: the plot extraction and crop classification are not automated enough; the yield estimation mechanism is not strong, and the accuracy is not high. At the industry level, the first issue is the limitation of the remote sensing technology itself in that the remote sensing is not almighty, suffering from limited data source, either from satellite or from other platform, laborious data preprocessing, and pricey data fees for most of the data, and the second is the compatibility between the current business of the insurance industry and the combination of remote sensing. In this regard, this paper proposed in total five specific suggestions, which are: 1st, to establish a data distribution platform to solve the problems of difficult data acquisition and processing and standardization of initial data; 2nd, to improve the sample database to promote the automation of plot extraction and crop classification; 3rd, to achieve faster, more accurate and more scientific yields through multidisciplinary research; 4th, to standardize remote sensing technology application in agricultural insurance, and 5th, to write remote sensing applications in crop insurance contract. With these improvements, the application mode of plant income insurance and probably the whole agriculture insurance would run in a way with easily available data, more automated and intelligent technology, standards to follow, and contract endorsements.

Key words: remote sensing, agricultural insurance, plant income insurance, precise claim settlement, yield estimation, cultivated land extraction, disaster estimation, remote sensing data sources

CLC Number: