Studies from Chinese Academy of Agricultural Sciences in the Area of Crop Insurance Published (Application of Satellite Remote Sensing Yield Estimation Technology in Regional Revenue Protection Crop Insurance: A Case of Soybean): Agriculture - Crop Insurance - Insurance News | InsuranceNewsNet

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November 10, 2021 Newswires
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Studies from Chinese Academy of Agricultural Sciences in the Area of Crop Insurance Published (Application of Satellite Remote Sensing Yield Estimation Technology in Regional Revenue Protection Crop Insurance: A Case of Soybean): Agriculture – Crop Insurance

Insurance Daily News

2021 NOV 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New study results on crop insurance have been published. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “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.”

Our news correspondents obtained a quote from the research from Chinese Academy of Agricultural Sciences: “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.”

According to the news reporters, the research concluded: “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.”

For more information on this research see: Application of Satellite Remote Sensing Yield Estimation Technology in Regional Revenue Protection Crop Insurance: A Case of Soybean. zhihuinongye, 2020,2(3):139-152. The publisher for zhihuinongye is Editorial Office of Smart Agriculture.

A free version of this journal article is available at https://doi.org/10.12133/j.smartag.2020.2.3.202006-SA002.

Our news editors report that more information may be obtained by contacting CHEN Ailian, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, People’s Republic of China. Additional authors for this research include LI Jiayu, ZHANG Shengjun, ZHU Yuxia, ZHAO Sijian, SUN Wei, ZHANG Qiao.

(Our reports deliver fact-based news of research and discoveries from around the world.)

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