基于无人机高光谱影像的冬小麦叶片氮浓度遥感估测

doi:10.13866/j.azr.2024.06.15

干旱区研究 ›› 2024, Vol. 41 ›› Issue (6): 1069-1078.doi: 10.13866/j.azr.2024.06.15

基于无人机高光谱影像的冬小麦叶片氮浓度遥感估测

孙法福¹^,²^,³(), 赖宁¹^,², 耿庆龙¹^,², 李永福¹^,², 吕彩霞¹^,², 信会男¹^,², 李娜¹^,², 陈署晃¹^,²()

1.新疆农业科学院土壤肥料与农业节水研究所，新疆乌鲁木齐 830091
2.新疆农业科学院农业遥感中心，新疆乌鲁木齐 830091
3.新疆农业大学资源与环境学院，新疆乌鲁木齐 830052

收稿日期:2023-12-01 修回日期:2024-04-20 出版日期:2024-06-15 发布日期:2024-07-03
通讯作者: 陈署晃. E-mail: chensh66@163.com
作者简介:孙法福(1997-)，男，硕士研究生，主要从事农业遥感相关研究. E-mail: 18253289263@163.com
基金资助:
农业科技创新稳定支持专项(xjnkywdzc-2023002);农业科技创新稳定支持专项(xjnkywdzc-2023007-3);新疆小麦产业技术体系(XJARS-01);新疆维吾尔自治区重大专项(2022A02011-2)

Estimation of nitrogen contentration in winter wheat leaves based on hyperspectral images of UAV

SUN Fafu¹^,²^,³(), LAI Ning¹^,², GENG Qinglong¹^,², LI Yongfu¹^,², LV Caixia¹^,², XIN Huinan¹^,², LI Na¹^,², CHEN Shuhuang¹^,²()

1. Institute of Soil, Fertilizer and Water Saving Agriculture Xinjiang Academy of Agricultural Sciences, Urumqi 830091, Xinjiang, China
2. Agricultural Remote Sensing Center, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, Xinjiang, China
3. College of Resources and Environmental Sciences, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China

Received:2023-12-01 Revised:2024-04-20 Online:2024-06-15 Published:2024-07-03

摘要/Abstract

摘要：

叶片氮浓度（LNC）是反应作物光合作用、营养状况和长势的重要指标，为精准高效地估测不同生育期冬小麦叶片氮浓度，以新冬22为研究对象，利用无人机搭载Pika L高光谱相机获取4个关键生育期冬小麦冠层反射率数据。基于波段优化算法和相关性分析筛选LNC敏感光谱指数，结合逐步回归、多元线性回归和偏最小二乘回归建立关键生育期冬小麦叶片氮浓度估测模型，并与单变量估测模型进行比较。结果表明：基于波段优化算法筛选的组合光谱指数与LNC的相关性优于传统植被指数，且达到极显著性相关；在单变量LNC估测模型中，组合光谱指数构建的模型精度优于传统植被指数，其中，扬花期差值光谱指数（DSI₍_R_940、_R₉₆₈₎）建立的估测模型最好，R²为0.789；多变量估测模型精度均优于单变量估测模型，其中，基于偏最小二乘回归构建的LNC估算模型最好，孕穗期和扬花期拟合效果较优，模型决定系数均为0.923，均方根误差为0.082、0.084。本研究结果可以作为冬小麦LNC估测和长势监测的科学依据。

关键词: 冬小麦, 叶片氮浓度, 无人机, 高光谱, 偏最小二乘回归, 组合光谱指数

Abstract:

Established leaf nitrogen concentration (LNC) is the response of crop photosynthesis, an important index of nutrition and growth. To accurately and efficiently estimate different growth period of winter wheat LNC, with the new winter 22 as the research object, using the (UAVs) Pika L hyperspectral cameras for four key growth period of winter wheat canopy reflectance data. The LNC-sensitive spectral index was screened based on the band optimization algorithm and correlation analysis. Stepwise regression, multiple linear regression, and partial least squares regression were combined to establish the estimation model of winter wheat LNC in each key growth stage, which was compared with the single variable estimation model. The results showed that (1) the correlation between the combined spectral index screened using the band optimization algorithm and LNC was stronger than that obtained using the traditional vegetation index and was extremely significant; (2) the combined spectral index in the single variable LNC estimation model allowed to obtain a more accurate model compared with the traditional vegetation index, including Yang flowering DSI₍_R_940,_R₉₆₈₎ estimate model is set up, best R² of 0.789. The multi-variable estimation models were more accurate than the single variable estimation models and, among them, the LNC estimation model based on partial least squares regression was the best, and the fitting effect of the booting and flowering stages was better. This model had a coefficient of determination of 0.923 and root-mean-square errors of 0.082 and 0.084. The results of this study provide a theoretical basis and technical support to estimate the LNC of winter wheat and monitor its growth.

Key words: winter wheat, leaf nitrogen content (LNC), unmanned aerial vehicle (UAV), hyperspectral, partial leastsquares regression, combination of spectral index

孙法福, 赖宁, 耿庆龙, 李永福, 吕彩霞, 信会男, 李娜, 陈署晃. 基于无人机高光谱影像的冬小麦叶片氮浓度遥感估测[J]. 干旱区研究, 2024, 41(6): 1069-1078.

SUN Fafu, LAI Ning, GENG Qinglong, LI Yongfu, LV Caixia, XIN Huinan, LI Na, CHEN Shuhuang. Estimation of nitrogen contentration in winter wheat leaves based on hyperspectral images of UAV[J]. Arid Zone Research, 2024, 41(6): 1069-1078.

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参数	数值
光谱范围	400~1000 nm
采样间隔	2.1 nm
光谱通道	281 bands
空间通道数	900个
光谱分辨率	2.1 nm
尺寸	11.5 cm×10.4 cm×6.6 cm

光谱指数类型	光谱特征指数	定义	文献
任意两波段光谱指数	DSI（差值光谱指数）	Ri－Rj	本研究
	RSI（比值光谱指数）	Ri/Rj	本研究
	NDSI（归一化光谱指数）	(Ri－Rj)/(Ri+Rj)	本研究
植被指数	SR（简单比值指数）	R750/R706	[21]
	NDRE（红边归一化光谱指数）	(R790－R720)/(R790+R720)	[11]
	MTCI（陆地叶绿素指数）	(R750－R710)/(R710－R680)	[22]
	NDVI（归一化植被指数）	(R780－R670)/(R780+R670)	[23]
	GNDVI（绿色归一化植被指数）	(R750－R550)/(R750+R550)	[22]
	RNDRE（红边归一化植被指数）	（R750－R705)/(R750+R706)	[15]
	VOG（红边指数）	R742/R722	[23]
	CIred-edge（红光叶绿素光谱指数）	(R780/R710)－1	[12]
	CIgreen（绿光叶绿素光谱指数）	(R780/R550)－1	[15]
	RVI（比值植被指数）	R790/R670	[8]

生育时期	光谱指数	相关系数
拔节期	DSI₍_R_644、_R₆₈₈₎	-0.864^**
	RSI₍_R_596、_R₆₉₂₎	0.836^**
	NDSI₍_R_596、_R₆₉₂₎	-0.837^**
孕穗期	DSI₍_R_826、_R₇₉₀₎	0.858^**
	RSI₍_R_632、_R₆₂₀₎	0.863^**
	NDSI₍_R_620、_R₆₃₂₎	0.863^**
扬花期	DSI₍_R_940、_R₉₆₈₎	0.877^**
	RSI₍_R_494、_R₅₁₄₎	0.870^**
	NDSI₍_R_514、_R₄₉₄₎	0.869^**
灌浆期	DSI₍_R_914、_R₉₁₂₎	0.811^**
	RSI₍_R_444、_R₅₂₀₎	0.808^**
	NDSI₍_R_444、_R₅₂₀₎	-0.810 ^**

光谱指数	线性函数				二次函数				指数函数				幂函数
光谱指数	A	B	C	D	A	B	C	D	A	B	C	D	A	B	C	D
SR	0.530^*	0.538^*	0.549^*	0.476^*	0.531^*	0.636^**	0.567^*	0.578^*	0.523^*	0.548^*	0.581^*	0.546^*	0.519^*	0.508^*	0.598^**	0.616^**
NDRE	0.504^*	0.560^*	0.585^*	0.533^*	0.509^*	0.650^**	0.585^*	0.575^*	0.504^*	0.572^*	0.613^**	0.622^*	0.483^*	0.537^*	0.604^**	0.660^**
MTCI	0.541^*	0.553^*	0.583^*	0.507^*	0.543^*	0.583^*	0.592^*	0.595^*	0.538^*	0.570^*	0.605^**	0.579^*	0.520^*	0.538^*	0.619^**	0.656^**
NDVI	0.476^*	0.360	0.476^*	0.356	0.507^*	0.571^*	0.492^*	0.429^*	0.478^*	0.362	0.525^*	0.401^*	0.457^*	0.353	0.521^*	0.423^*
GNDVI	0.490^*	0.439^*	0.521^*	0.240	0.511^*	0.633^**	0.541^*	0.273	0.491^*	0.446^*	0.567^*	0.269	0.473^*	0.429^*	0.555^*	0.278
RNDRE	0.510^*	0.468^*	0.556^*	0.549^*	0.529^*	0.633^**	0.562^*	0.572^*	0.511^*	0.479^*	0.597^**	0.643^*	0.484^*	0.453^*	0.586^*	0.665^**
VOG	0.515^*	0.556^*	0.577^*	0.505^*	0.517^*	0.656^**	0.580^*	0.578^*	0.513^*	0.568^*	0.608^*	0.586^*	0.510^*	0.548^*	0.612^**	0.617^**
CIred-edge	0.524^*	0.569^*	0.564^*	0.470^*	0.524^*	0.631^**	0.581^*	0.580^*	0.517^*	0.578^*	0.589^*	0.539^*	0.501^*	0.524^*	0.606^**	0.639^**
CIgreen	0.506^*	0.561^*	0.530^*	0.429^*	0.506^*	0.652^**	0.564^*	0.560^*	0.499^*	0.563^*	0.562^*	0.491^*	0.491^*	0.503^*	0.584^*	0.587^*
RVI	0.511^*	0.507^*	0.454^*	0.407^*	0.511^*	0.644^**	0.520^*	0.535^*	0.494^*	0.504^*	0.501^*	0.477^*	0.502^*	0.429^*	0.531^*	0.575^*
DSI	0.747^**	0.737^**	0.769^**	0.657^**	0.752^**	0.739^**	0.770^**	0.705^**	0.777^**	0.701^**	0.757^**	0.652^**	0.777^**	0.689^**	0.789^**	0.736^**
RSI	0.699^**	0.743^**	0.757^**	0.652^**	0.700^**	0.781^**	0.759^**	0.671^**	0.704^**	0.746^**	0.756^**	0.697^**	0.706^**	0.747^**	0.757^**	0.708^**
NDSI	0.700^**	0.744^**	0.756^**	0.657^**	0.701^**	0.781^**	0.759^**	0.672^**	0.706^**	0.747^**	0.757^**	0.705^**	0.668^**	0.702^**	0.750^**	0.686^**

生育期	模型类型	模型表达式	R²
拔节期	逐步回归	y=4.941+328.631×DSI	0.785^**	0.152
	多元线性回归	y=5.237+1.643×SR-7.167×NDRE-0.371×MTCI-0.69×VOG+2.107×CIred-edge-1.298×CIgreen+416×DSI-12.857×NDSI	0.810^**	0.148
	偏最小二乘回归	y=-439.214+2.537×SR-0.644×CIgreen-847.763×NDSI-4.84×NDRE-0.166×MTCI+534.02×DSI+441.385×RSI-1.491×CIred-edge+2.067×VOG	0.827^**	0.125
孕穗期	逐步回归	y=121.58×DSI+64.851×NDSI+0.676	0.886^**	0.146
	多元线性回归	y=-25.266-3.528×SR-63.571×NDRE-0.808×MTCI+35.126×VOG+2.556×CIred-edge-0.299×CIgreen+120.277×DSI+69.302×NDSI	0.915^**	0.088
	偏最小二乘回归	y=1989.584-3.786×SR-67.171×NDRE-1.078×MTCI+36.272×VOG+3.021×CIred-edge-0.287×CIgreen+135.439×DSI-2015.69×RSI-3856.162×NDSI	0.923^**	0.082
扬花期	逐步回归	y=-8.102+26.947×DSI+13.905×RSI-4.374×NDRE	0.899^**	0.106
	多元线性回归	y=-10.266+0.444×RS-5.814×NDRE-0.276×MTCI-0.45×CIred-edge+0.228×CIgreen-0.848×VOG+17.924×RSI+29.493×DSI	0.916^**	0.086
	偏最小二乘回归	y=-20.042+0.476×SR-5.603×NDRE-0.309×MTCI-0.47×CIred-edge+0.226×CIgreen-0.814×VOG+15.656×NDSI+27.821×RSI+29.548×DSI	0.923^**	0.084
灌浆期	逐步回归	y=3.344+247.593×DSI-7.417×NDSI	0.753^**	0.212
	多元线性回归	y=-19.407+1.844×SR-35.582×NDRE+3.161×MTCI+22.083×VOG-5.733×CIred-edge-0.529×CIgreen+406.249×DSI-2.746×RSI-3.227×NDSI	0.815^**	0.126
	偏最小二乘回归	y=1.952+1.646×SR-2.536×NDRE+2.848×MTCI+3.772×VOG-4.888×CIred-edge-0.74×CIgreen+0.327×DSI+0.565×RSI+0.591×NDSI	0.862^**	0.125

基于无人机高光谱影像的冬小麦叶片氮浓度遥感估测

Estimation of nitrogen contentration in winter wheat leaves based on hyperspectral images of UAV

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