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

Abstract

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

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.

Figures/Tables 11

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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

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

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