遥感技术在种植收入保险中的应用场景及研究进展

doi:10.12133/j.smartag.SA202201011

摘要/Abstract

摘要：

种植收入保险已成为中国农业保险的一种重要形式，2022年将在13个粮食主产省的所有主产县开展。本文首先回顾了遥感技术在农业保险中的总体应用历程，其次，通过阐述现有种植收入保险的业务模式，展现了目前遥感技术在该模式下的应用场景，并对各种应用场景下的关键技术的应用研究进展进行了评述，包括耕地地块提取、作物分类提取、作物灾情评估和作物产量估算。最后，列举了目前种植收入保险常用的遥感数据源。通过综述，发现种植收入保险应用场景下最重要的两个技术层面的问题，一是地块提取和作物分类不够自动化，二是产量估算机理性不强、准确度不高。由此又延伸出两个行业层面的问题，一是遥感行业自身局限性问题，二是保险行业的现行业务与遥感技术结合的兼容性问题。对此，本文提出建立数据分发平台解决数据获取和与处理难和初始数据标准化的问题、完善耕地地块和作物类型样本库以促进地块提取和作物分类自动化、多学科交叉研究实现更快更准更科学地产量估算、农业保险遥感技术应用标准化，以及遥感技术应用合同化等五个具体建议。展望未来，种植收入保险乃至所有农业保险中遥感技术的应用模式应该是一个有数据可用、技术上更自动化智能化、有标准可依、有合同背书的新型模式。

关键词: 遥感技术, 农业保险, 种植收入保险, 精确理赔, 产量估算, 耕地提取, 灾情评估, 遥感数据源

Abstract:

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

中图分类号:

陈爱莲, 赵思健, 朱玉霞, 孙伟, 张晶, 张峭. 遥感技术在种植收入保险中的应用场景及研究进展[J]. 智慧农业(中英文), 2022, 4(1): 57-70.

CHEN Ailian, ZHAO Sijian, ZHU Yuxia, SUN Wei, ZHANG jing, ZHANG Qiao. Application Scenarios and Research Progress of Remote Sensing Technology in Plant Income Insurance[J]. Smart Agriculture, 2022, 4(1): 57-70.

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