Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms

doi:10.3969/j.issn.1004-1524.20231368

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (12): 2812-2822.DOI: 10.3969/j.issn.1004-1524.20231368

• Biosystems Engineering • Previous Articles Next Articles

Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms

ZHANG Yongbin¹(), LI Xiang¹, MAN Weidong¹^,²^,³, LIU Mingyue¹^,²^,⁴^,^*(), FAN Jihao⁵, HU Haoran⁵, SONG Lijie¹, LIU Weijia¹

1. College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China
2. Tangshan Key Laboratory of Resources and Environmental Remote Sensing, Tangshan 063210, Hebei, China
3. Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210, Hebei, China
4. Collaborative Innovation Center of Green Development and Ecological Restoration of Mineral Resources, Tangshan 063210, Hebei, China
5. Hebei Tangshan High Resolution Earth Observation System Data and Application Center, Tangshan 063210, Hebei, China

Received:2023-12-06 Online:2024-12-25 Published:2024-12-27

Abstract

Abstract:

Aiming at the problem that optical images are easily affected by cloud and rain weather, resulting in low accuracy of crop yield estimation, in this study, the Sentinel-1/2 spectral information and backscattering coefficient at winter wheat heading stage were combined, and three machine learning regression methods of extreme gradient boosting, random forest and support vector machine were used to establish the winter wheat yield estimation model in Tangshan, the best model was selected to realize the winter wheat yield inversion in Tangshan. The results show that:the extreme gradient boosting model based on vegetation index and backscattering coefficient had the best estimation effect, with the determination coefficient (R²) of 0.654, the root mean square error (RMSE) of 0.499 t·hm^-2, and the normalized root mean square error (nRMSE) of 10.02%. Among the 24 remote sensing feature variables, the importance of NDMI, NDVIre3 and NDVIre2 was much higher than that of the backscattering coefficient. Inverse spatial distribution of winter wheat yield in Tangshan based on optimal yield estimation model, the yield range of winter wheat was mainly concentrated in 7.00-8.00 t·hm^-2, accounting for 40.75%, the distribution of winter wheat yield was generally similar to the ground truth. This study proposed Sentinel-1/2 data and integration of machine learning algorithms of winter wheat yield estimation method, effectively improve the inversion accuracy of winter wheat yield and machine learning method to strengthen the explanatory of the model, the method has certain feasibility.

Key words: remote sensing, yield, winter wheat, Sentinel-1/2 data, machine learning algorithm

CLC Number:

S512.11

ZHANG Yongbin, LI Xiang, MAN Weidong, LIU Mingyue, FAN Jihao, HU Haoran, SONG Lijie, LIU Weijia. Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms[J]. Acta Agriculturae Zhejiangensis, 2024, 36(12): 2812-2822.

Figures/Tables 8

References 37

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植被指数Vegetation index	计算公式Calculation formula
差值植被指数Difference vegetation index (DVI)	ρ_NIR-ρ_R
增强植被指数2 Enhanced vegetation index without a blue band (EVI2)	2.5(ρ_NIR-ρ_R)/(ρ_NIR+2.4ρ_R+1)
绿色归一化植被指数Green normalized difference vegetation index (GNDVI)	(ρ_NIR-ρ_G)/(ρ_NIR+ρ_G)
比值植被指数Ratio vegetation index (RVI)	ρ_NIR/ρ_R
重归一化植被指数Renormalized difference vegetation index (RDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)^0.5
修正三角植被指数1 Modified triangular vegetation index (MTVI1)	1.2[1.2(ρ_NIR-ρ_G)-2.5(ρ_R-ρ_G)]
改进简单植被指数Modified simple ratio (MSR)	(ρ_NIR/ρ_R-1)/(ρ_NIR/ρ_R+1)^0.5
优化土壤调节植被指数Optimal soil adjusted vegetation index (OSAVI)	1.16(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R+0.16)
结构加强色素植被指数Structure intensive pigment index (SIPI)	(ρ_NIR-ρ_B)/(ρ_NIR+ρ_B)
归一化差值水汽指数Normalized difference moisture index (NDMI)	(ρ_NIR-ρ_SWIR11)/(ρ_NIR+ρ_SWIR11)
绿色叶绿素指数Green chlorophyll vegetation index (CIg)	(ρ_NIR/ρ_R)-1
红边植被指数1 Red-edge chlorophyll index 1 (CIre1)	(ρ_NIR/ρ_RE1)-1
红边植被指数2 Red-edge chlorophyll index 2 (CIre2)	(ρ_NIR/ρ_RE2)-1
红边植被指数3 Red-edge chlorophyll index 3 (CIre3)	(ρ_NIR/ρ_RE3)-1
归一化植被指数Normalized difference vegetation index (NDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)
红边归一化植被指数1 Red-edge normalized difference vegetation index 1 (NDVIre1)	(ρ_NIR-ρ_RE1)/(ρ_NIR+ρ_RE1)
红边归一化植被指数2 Red-edge normalized difference vegetation index 2 (NDVIre2)	(ρ_NIR-ρ_RE2)/(ρ_NIR+ρ_RE2)
红边归一化植被指数3 Red-edge normalized difference vegetation index 3 (NDVIre3)	(ρ_NIR-ρ_RE3)/(ρ_NIR+ρ_RE3)

植被指数Vegetation index	计算公式Calculation formula
差值植被指数Difference vegetation index (DVI)	ρ_NIR-ρ_R
增强植被指数2 Enhanced vegetation index without a blue band (EVI2)	2.5(ρ_NIR-ρ_R)/(ρ_NIR+2.4ρ_R+1)
绿色归一化植被指数Green normalized difference vegetation index (GNDVI)	(ρ_NIR-ρ_G)/(ρ_NIR+ρ_G)
比值植被指数Ratio vegetation index (RVI)	ρ_NIR/ρ_R
重归一化植被指数Renormalized difference vegetation index (RDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)^0.5
修正三角植被指数1 Modified triangular vegetation index (MTVI1)	1.2[1.2(ρ_NIR-ρ_G)-2.5(ρ_R-ρ_G)]
改进简单植被指数Modified simple ratio (MSR)	(ρ_NIR/ρ_R-1)/(ρ_NIR/ρ_R+1)^0.5
优化土壤调节植被指数Optimal soil adjusted vegetation index (OSAVI)	1.16(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R+0.16)
结构加强色素植被指数Structure intensive pigment index (SIPI)	(ρ_NIR-ρ_B)/(ρ_NIR+ρ_B)
归一化差值水汽指数Normalized difference moisture index (NDMI)	(ρ_NIR-ρ_SWIR11)/(ρ_NIR+ρ_SWIR11)
绿色叶绿素指数Green chlorophyll vegetation index (CIg)	(ρ_NIR/ρ_R)-1
红边植被指数1 Red-edge chlorophyll index 1 (CIre1)	(ρ_NIR/ρ_RE1)-1
红边植被指数2 Red-edge chlorophyll index 2 (CIre2)	(ρ_NIR/ρ_RE2)-1
红边植被指数3 Red-edge chlorophyll index 3 (CIre3)	(ρ_NIR/ρ_RE3)-1
归一化植被指数Normalized difference vegetation index (NDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)
红边归一化植被指数1 Red-edge normalized difference vegetation index 1 (NDVIre1)	(ρ_NIR-ρ_RE1)/(ρ_NIR+ρ_RE1)
红边归一化植被指数2 Red-edge normalized difference vegetation index 2 (NDVIre2)	(ρ_NIR-ρ_RE2)/(ρ_NIR+ρ_RE2)
红边归一化植被指数3 Red-edge normalized difference vegetation index 3 (NDVIre3)	(ρ_NIR-ρ_RE3)/(ρ_NIR+ρ_RE3)

组合方式Combination	特征变量Characteristic variable
A	Sentinel-1+Sentinel-2
B	Sentinel-2
C	Sentinel-1

组合方式Combination	特征变量Characteristic variable
A	Sentinel-1+Sentinel-2
B	Sentinel-2
C	Sentinel-1

回归模型 Regression model	组合方式 Combination	R²	RMSE/ (t·hm^-2)	nRMSE/%
XGBoost	A	0.654	0.499	10.02
	B	0.632	0.513	10.27
	C	0.539	0.592	11.86
RFR	A	0.473	0.534	10.71
	B	0.517	0.521	10.44
	C	0.311	0.673	13.49
SVR	A	0.340	0.866	17.36
	B	0.356	0.857	17.17
	C	0.213	1.303	26.11

Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms

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等级 Level/ (t·hm^-2)	冬小麦产量空间分布 Spatial distribution of winter wheat yield
等级 Level/ (t·hm^-2)	像元个数Number of pixels	所占比例Proportion/%
≤6	74 875	2.98
>6~7	447 252	17.78
>7~8	1 025 241	40.75
>8~9	891 461	35.43
>9	77 039	3.06

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