基于机载多光谱的冬小麦返青期土壤墒情反演

doi:10.3969/j.issn.1004-1524.2022.06.20

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (6): 1297-1305.DOI: 10.3969/j.issn.1004-1524.2022.06.20

基于机载多光谱的冬小麦返青期土壤墒情反演

王钧¹^,²(), 陆洲², 罗明², 徐飞飞², 张序¹^,^*()

1.苏州科技大学环境科学与工程学院,江苏苏州 215009
2.中国科学院地理科学与资源研究所,北京 100101

收稿日期:2021-07-15 出版日期:2022-06-25 发布日期:2022-06-30
通讯作者: 张序
作者简介:*张序,E-mail: xu1960zhang@sina.com
王钧（1997—）,男,江苏连云港人,硕士研究生,主要从事无人机多光谱遥感研究。E-mail: wj1240120487@163.com
基金资助:
国家重点研发计划(2016YFD0300201);苏州市科技计划(SNG2018100)

Inversion of soil moisture content of winter wheat at turning green period based on multispectral remote sensing by unmanned aerial vehicle

WANG Jun¹^,²(), LU Zhou², LUO Ming², XU Feifei², ZHANG Xu¹^,^*()

1. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China
2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2021-07-15 Online:2022-06-25 Published:2022-06-30
Contact: ZHANG Xu

摘要/Abstract

摘要：

准确快速得获取冬小麦地块的土壤墒情,可为高效利用水资源、实现精准灌溉提供参考。为此,特在江苏省张家港市获取返青期冬小麦种植区的无人机多光谱遥感数据,并同步测定2个深度（10 cm和20 cm）的土壤墒情,通过遥感图像提取光谱反射率,计算归一化植被指数（NDVI）、增强型植被指数（EVI）和垂直干旱指数（PDI）,进行共线性分析后,分别运用逐步回归法、岭回归法和偏最小二乘法,构建针对不同深度土壤墒情的反演模型,并基于最佳反演模型绘制试验区不同深度土壤的墒情反演图。结果表明,用逐步回归法构建的模型在10、20 cm深度土壤墒情反演中的决定系数分别达到了0.885、0.782,建模精度最优,且针对10 cm深度土壤墒情的反演效果优于20 cm。研究结果可为冬小麦返青期土壤墒情监测方法的选择提供参考。

关键词: 土壤墒情, 无人机, 多光谱遥感, 反演

Abstract:

Accurate and quick determination of soil moisture of winter wheat plot could provide references for efficient water utilization and precision irrigation. In this study, the multispectral remote sensing data of winter wheat at turning green period were obtained by unmanned aerial vehicle in Zhangjiagang City, Jiangsu Province. The soil moisture at two depths (10 cm and 20 cm) was measured simultaneously. Spectral reflectance was extracted from remote sensing images, and normalized difference vegetation index, enhanced vegetation index, perpendicular drought index were calculated. After collinearity analysis, stepwise regression, ridge regression and partial least squares regression methods were used to construct soil moisture content inversion models at different soil depths, then the optimal inversion model was adopted to draw soil moisture inversion maps. The results showed that the determination coefficient of models constructed by the stepwise regression method for 10, 20 cm soil depths were 0.885 and 0.782, respectively, of which the inversion precision was the highest. The inversion performance for 10 cm soil depth of all the constructed models were better than that of the 20 cm soil depth. The present study could provide references for the selection of soil moisture monitoring of winter wheat at turning green period.

Key words: soil moisture, unmanned aerial vehicle, multispectral remote sensing, inversion

中图分类号:

S152.7

王钧, 陆洲, 罗明, 徐飞飞, 张序. 基于机载多光谱的冬小麦返青期土壤墒情反演[J]. 浙江农业学报, 2022, 34(6): 1297-1305.

WANG Jun, LU Zhou, LUO Ming, XU Feifei, ZHANG Xu. Inversion of soil moisture content of winter wheat at turning green period based on multispectral remote sensing by unmanned aerial vehicle[J]. Acta Agriculturae Zhejiangensis, 2022, 34(6): 1297-1305.

图/表 8

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深度 Depth/cm	样本集 Dataset	样本数量 Sample size	最小值 Minimum	最大值 Maximum	平均值 Mean	标准差 Standard deviation	变异系数 Coefficient of variation
10	总集Whole set	39	0.347	0.456	0.393	0.024	0.060
	建模集Modeling set	30	0.347	0.456	0.394	0.026	0.065
	检验集Verification set	9	0.363	0.412	0.392	0.016	0.041
20	总集Whole set	39	0.368	0.507	0.476	0.048	0.101
	建模集Modeling set	30	0.403	0.507	0.489	0.044	0.091
	检验集Verification set	9	0.368	0.504	0.440	0.038	0.087

深度 Depth/cm	样本集 Dataset	样本数量 Sample size	最小值 Minimum	最大值 Maximum	平均值 Mean	标准差 Standard deviation	变异系数 Coefficient of variation
10	总集Whole set	39	0.347	0.456	0.393	0.024	0.060
	建模集Modeling set	30	0.347	0.456	0.394	0.026	0.065
	检验集Verification set	9	0.363	0.412	0.392	0.016	0.041
20	总集Whole set	39	0.368	0.507	0.476	0.048	0.101
	建模集Modeling set	30	0.403	0.507	0.489	0.044	0.091
	检验集Verification set	9	0.368	0.504	0.440	0.038	0.087

指标Index	VIF	指标Index	VIF
蓝Blue	35.935	近红外Near-infrared	46.548
绿Green	27.135	NDVI	101.345
红Red	24.573	EVI	99.657
红边Red edge	48.147	PDI	34.640

指标Index	VIF	指标Index	VIF
蓝Blue	35.935	近红外Near-infrared	46.548
绿Green	27.135	NDVI	101.345
红Red	24.573	EVI	99.657
红边Red edge	48.147	PDI	34.640

建模方法 Modeling method	深度 Depth/cm	建模结果 Modeling result	R²	RMSE
逐步回归法 Stepwise regression	10	Y=-0.396B₁-0.232B₂-0.068N+0.508	0.885	0.009
逐步回归法 Stepwise regression	20	Y=-2.695B₂+0.960B₅+1.019E-1.072P+1.298	0.782	0.016
岭回归法 Ridge regression	10	Y=0.021B₁-0.084B₂-0.301B₃-0.252B₄-0.086B₅-0.063N+0.064E-0.255P+0.428	0.762	0.013
岭回归法 Ridge regression	20	Y=-0.125B₁-0.244B₂+0.068B₃-0.330B₄-0.009B₅+0.075N-0.101E-0.304P +0.871	0.668	0.023
偏最小二乘法 Partial least squares regression	10	Y=0.076B₁-0.293B₂-0.255B₃-0.179B₄+0.204B₅-0.755N+0.983E-0.151P +0.643	0.838	0.009
偏最小二乘法 Partial least squares regression	20	Y=-1.085B₁-0.101B₂-0.400B₃-0.514B₄+0.803B₅-0.557N-0.034E-0.339P +0.938	0.737	0.020

基于机载多光谱的冬小麦返青期土壤墒情反演

Inversion of soil moisture content of winter wheat at turning green period based on multispectral remote sensing by unmanned aerial vehicle

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建模方法 Modeling method	深度 Depth/cm	R²	RMSE	RPD
逐步回归法 Stepwise regression	10	0.899	0.005	3.091
逐步回归法 Stepwise regression	20	0.865	0.016	2.220
岭回归法 Ridge regression	10	0.843	0.008	2.020
岭回归法 Ridge regression	20	0.814	0.020	1.095
偏最小二乘法 Partial least squares regression	10	0.856	0.008	2.142
偏最小二乘法 Partial least squares regression	20	0.831	0.017	2.136

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