融合Sentinel-1/2数据和机器学习算法的冬小麦产量估算方法研究

doi:10.3969/j.issn.1004-1524.20231368

浙江农业学报 ›› 2024, Vol. 36 ›› Issue (12): 2812-2822.DOI: 10.3969/j.issn.1004-1524.20231368

融合Sentinel-1/2数据和机器学习算法的冬小麦产量估算方法研究

张永彬¹(), 李想¹, 满卫东¹^,²^,³, 刘明月¹^,²^,⁴^,^*(), 樊继好⁵, 胡皓然⁵, 宋利杰¹, 刘玮佳¹

1.华北理工大学矿业工程学院,河北唐山 063210
2.唐山市资源与环境遥感重点实验室,河北唐山 063210
3.河北省矿区生态修复产业技术研究院,河北唐山 063210
4.矿产资源绿色开发与生态修复协同创新中心,河北唐山 063210
5.高分辨率对地观测系统河北唐山数据应用中心,河北唐山 063210

收稿日期:2023-12-06 出版日期:2024-12-25 发布日期:2024-12-27
作者简介:张永彬(1969—),男,河北衡水人,博士,教授,研究方向为3S技术在资源与环境中的应用与地理国情监测。E-mail:zyb063009@yeah.net
通讯作者: *刘明月,E-mail:liumy917@ncst.edu.cn
基金资助:
河北省自然科学基金项目(D2019209322);河北省自然科学基金项目(D2022209005);河北省高等学校科学技术研究项目青年拔尖人才项目(BJ2020058);唐山市科技计划重点研发项目(22150221J);河北省引进留学人员资助项目(C20200103)

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

摘要/Abstract

摘要：

针对光学影像容易受到云雨天气影响,导致农作物产量估算精度低的问题,本研究融合冬小麦孕穗期Sentinel-2光谱信息和Sentinel-1后向散射系数,并采用极端梯度提升、随机森林和支持向量机3种机器学习回归方法建立唐山市冬小麦产量估算模型,选用最佳模型实现唐山市冬小麦产量反演。结果表明: 基于植被指数和后向散射系数的极端梯度提升模型的估产效果最好,决定系数(R²)为0.654,均方根误差(RMSE)为0.499 t·hm^-2,归一化均方根误差(nRMSE)为10.02%。24个遥感特征变量中,NDMI、NDVIre3和NDVIre2的重要性远高于后向散射系数。基于最佳估产模型反演唐山市冬小麦产量空间分布,冬小麦产量范围主要集中在7.00~8.00 t·hm^-2,所占比例达到40.75%,冬小麦产量分布总体上与地面真实情况相近。本研究提出Sentinel-1/2数据和机器学习算法相融合的冬小麦产量估算方法,有效提高了机器学习方法反演冬小麦产量的准确性,并加强了模型的解释性,该方法具有一定可行性。

关键词: 遥感, 产量, 冬小麦, Sentinel-1/2数据, 机器学习算法

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

中图分类号:

S512.11

张永彬, 李想, 满卫东, 刘明月, 樊继好, 胡皓然, 宋利杰, 刘玮佳. 融合Sentinel-1/2数据和机器学习算法的冬小麦产量估算方法研究[J]. 浙江农业学报, 2024, 36(12): 2812-2822.

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.

图/表 8

表1 植被指数与计算公式

Table 1 Vegetation indices and calculation formulas

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)

表2 冬小麦产量不同特征的组合方式

Table 2 Statistical analysis of winter wheat yield

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

图1 冬小麦产量分区统计图

Fig.1 Statistical plot of winter wheat yield partition

表3 模型综合评价

Table 3 Model comprehensive evaluation

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

图2 冬小麦实测产量数据与预测产量数据散点图 A,A组合;B,B组合;C,C组合。

Fig.2 Scatter plot of measured production data and predicted production data of winter wheat yield A, Combination A; B, Combination B; C, Combination C.

图3 XGBoost模型的变量重要性排序图 A,A组合;B,B组合;C,C组合。NDMI,归一化差值水汽指数;NDVIre3,红边归一化植被指数3;NDVIre2,红边归一化植被指数2;GNDVI,绿色归一化植被指数;DVI,差值植被指数;RDVI,重归一化植被指数;SIPI,结构加强色素植被指数;NDVIre1,红边归一化植被指数1;MTVI1,修正三角植被指数1;EVI2,增强植被指数2;CIre2,红边植被指数2;CIre3,红边植被指数3;MSR,改进简单植被指数;OSAVI,优化土壤调节植被指数;RVI,比值植被指数;NDVI,归一化植被指数;CIg,绿色叶绿素指数;CIre1,红边植被指数1;VV,共极化;VH,交叉极化;VH+VV,求和;VH-VV,差值;VH*VV,乘积;VH/VV,求商。

Fig.3 Variable importance ranking chart of XGBoost model A, Combination A; B, Combination B; C, Combination C. NDMI, Normalized difference moisture index; NDVIre3, Red-edge normalized difference vegetation index 3; NDVIre2, Red-edge normalized difference vegetation index 2; GNDVI, Green normalized difference vegetation index; DVI, Difference vegetation index; RDVI, Renormalized difference vegetation index; SIPI, Structure intensive pigment index; NDVIre1, Red-edge normalized difference vegetation index 1; MTVI1, Modified triangular vegetation index; EVI2, Enhanced vegetation index without a blue band; CIre2, Red-edge chlorophyll index 2; CIre3, Red-edge chlorophyll index 3; MSR, Modified simple ratio; OSAVI, Optimal soil adjusted vegetation index; RVI, Ratio vegetation index; NDVI, Normalized difference vegetation index; CIg, Green chlorophyll vegetation index; CIre1, Red-edge chlorophyll index 1; VV, Co-polarization; VH, Cross polarization; VH+VV, Summation; VH-VV, Difference; VH*VV, Product; VH/VV, Quotient.

图4 冬小麦产量估算空间分布图 A,研究区冬小麦产量分布;B,红色区域放大图;C,绿色区域放大图;D,蓝色区域放大图。背景影像:研究区Sentinel-2灰度影像。

Fig.4 Spatial distribution map of winter wheat yield estimation A, Winter wheat yield distribution in study area; B, Zoom in on the red area; C, Zoom in on the green area; D, Zoom in on the blue area. Background imagery:Sentinel-2 grayscale image of the study area.

表4 唐山市冬小麦产量分级统计

Table 4 Classification statistics of winter wheat yield in Tangshan

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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融合Sentinel-1/2数据和机器学习算法的冬小麦产量估算方法研究

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

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