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

doi:10.12133/j.smartag.2020.2.3.202006-SA002

Abstract

Abstract:

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

CLC Number:

CHEN Ailian, LI Jiayu, ZHANG Shengjun, ZHU Yuxia, ZHAO Sijian, SUN Wei, ZHANG Qiao. Application of Satellite Remote Sensing Yield Estimation Technology in Regional Revenue Protection Crop Insurance: A Case of Soybean[J]. Smart Agriculture, 2020, 2(3): 139-152.

Figures/Tables 14

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变量	与产量相关系数	变量	与产量相关系数
0823NDVI	0.644^**	0922Cw	0.356^**
0907LAI	0.641^**	0803Cab	0.351^**
0907Cab	0.638^**	0922NDVI	0.348^**
0907FaPar	0.633^**	0808Cw	0.333^**
0823FaPar	0.628^**	0803FCover	0.320^**
0907FCover	0.618^**	0724FCover	0.290^*
0823FCover	0.615^**	0724FaPar	0.289^*
0823Cab	0.611^**	Max07FCover	0.289^*
0907NDVI	0.606^**	Max07FaPar	0.288^*
0823LAI	0.603^**	0808Cab	0.281^*
0907Cw	0.528^**	0808FCover	0.274^*
0922FaPar	0.486^**	0724LAI	0.273^*
1002_0823	-0.482^**	0808FaPar	0.268^*
0823_0624	0.480^**	0803FaPar	0.268^*
0922FCover	0.475^**	0803NDVI	0.261^*
0922LAI	0.449^**	TRMM2018091	0.257^*
0823Cw	0.437^**	20180727_Day_LST	0.255^*
0922Cab	0.437^**	20180711_Day_LST	0.237^*
0803LAI	0.402^**	20180828_Night_LST	0.235^*
0927NDVI	0.393^**	Max07NDVI	0.232^*
0808LAI	0.376^**

因子	0823NDVI	0907NDVI	0907LAI	0907FaPar	0907FCover	0907Cab	0907Cw
0823LAI	0.816^**	0.735^**	0.812^**	0.802^**	0.780^**	0.802^**	0.561^**
0823FCover	0.920^**	0.846^**	0.833^**	0.857^**	0.847^**	0.815^**	0.603^**
0823FaPar	0.908^**	0.837^**	0.849^**	0.867^**	0.851^**	0.836^**	0.633^**
0823Cab	0.817^**	0.734^**	0.834^**	0.819^**	0.793^**	0.833^**	0.623^**
0823Cw	0.474^**	0.467^**	0.556^**	0.516^**	0.479^**	0.562^**	0.593^**
0823NDVI	1.000	0.879^**	0.779^**	0.813^**	0.813^**	0.744^**	0.597^**
0907NDVI	0.879^**	1.000	0.825^**	0.889^**	0.899^**	0.791^**	0.591^**
0907LAI	0.779^**	0.825^**	1.000	0.982^**	0.968^**	0.989^**	0.837^**
0907FaPar	0.813^**	0.889^**	0.982^**	1.000	0.995^**	0.960^**	0.797^**
0907FCover	0.813^**	0.899^**	0.968^**	0.995^**	1.000	0.940^**	0.772^**
0907Cab	0.744^**	0.791^**	0.989^**	0.960^**	0.940^**	1	0.828^**
0907Cw	0.597^**	0.591^**	0.837^**	0.797^**	0.772^**	0.828^**	1

模型序号	模型	R²	标准误	F	输入变量	标准化系数	变量显著性
1	Y=-282.356+547.583×0823NDVI （7）	0.415^**	47.554	50.379	（常量）		0.000
1	Y=-282.356+547.583×0823NDVI （7）	0.415^**	47.554	50.379	0823NDVI	0.644	0.000
2	Y=-171.365+313.127×0823NDVI+35.154×0907LAI （8）	0.464^**	45.824	30.359	（常量）		0.025
					0823NDVI	0.386	0.010
					0907LAI	0.354	0.013
3	Y=-187.199+289.032×0823NDVI+30.816×0907LAI+957.102×0823Cw-12.239×NDVI_{（1002-0823）} （9）	0.471^**	46.210	15.136	（常量）		0.051
					0823NDVI	0.340	0.049
					0907LAI	0.311	0.042
					0823Cw	0.093	0.388
					NDVI_{(1002-0823）}	-0.025	0.845
4	Y=2025.482+323.644×0823NDVI+33.527×0907LAI+22.704×K_MOD11_10+8.307×K_MOD11_14-16.884×TRMM091+0.923×K_MOD11_23 （10）	0.482^**	46.407	10.242	（常量）		0.640
					0823NDVI	0.381	0.018
					0907LAI	0.338	0.022
					K_MOD11_10	0.106	0.348
					K_MOD11_14	-0.196	0.208
					TRMM091	0.163	0.207
					K_MOD11_23	0.013	0.902
5	Y=-285.649+509.074×0823NDVI+4.999×0907LAI+1349.340×0823Cw-95.854×NDVI_{（1002-0823）}-309.911×NDVI_{（0823-0624）}+231.163×0922FAPAR （11）	0.520^**	44.684	11.912	（常量）		0.005
					0823NDVI	0.599	0.007
					0907LAI	0.050	0.789
					0823Cw	0.131	0.219
					NDVI_(1002-0823)	-0.196	0.176
					NDVI_{(0823-0624）}	-0.428	0.025
					0922FAPAR	0.320	0.036