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 cstr: 32277.14.AZR.20240615

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
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 Published:2024-06-15 Online: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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参考文献 30

[1]	Zhao D L, Raja R K, Vijaya G K, et al. Nitrogen deficiency effects on plant growth, leaf photosynthesis and hyperspectral reflectance properties of sorghum[J]. European Journal of Agronomy, 2005, 22(4): 391-403.
[2]	郭建华, 赵春江, 王秀, 等. 作物氮素营养诊断方法的研究现状及进展[J]. 中国土壤与肥料, 2008(4): 10-14.
	[Guo Jianhua, Zhao Chunjiang, Wang Xiu, et al. Research advancement and status on crop nitrogen nutrition diagnosis[J]. Soil and Fertilizer Sciences in China, 2008(4): 10-14. ]
[3]	郭发旭, 冯全, 杨森, 等. 基于无人机高光谱的马铃薯冠层叶片全氮含量反演[J]. 浙江农业学报, 2023, 35(8): 1904-1914. doi: 10.3969/j.issn.1004-1524.20221475
	[Guo Faxu, Feng Quan, Yang Sen, et al. Inversion of leaf nitrogen content in potato canopy based on unmanned aerial vehicle hyperspectral images[J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1904-1914. ] doi: 10.3969/j.issn.1004-1524.20221475
[4]	易翔, 吕新, 张立福, 等. 基于RF和SPA的无人机高光谱估算棉花叶片全氮含量[J]. 作物杂志, 2023(2): 245-252.
	[Yi Xiang, Lv Xin, Zhang Lifu, et al. Unmanned aerial vehicle hyperspectral estimation of nitrogen content in cotton leaves based on RF and SPA[J]. Crops, 2023(2): 245-252. ]
[5]	邹德秋, 王家强, 张冬冬, 等. 基于光谱指数的胡杨叶片氮含量估算[J]. 森林与环境学报, 2022, 42(6): 623-630.
	[Zou Deqiu, Wang Jiaqiang, Zhang Dongdong, et al. Estimation of nitrogen content in Populus euphratica leaves based on spectral index[J]. Journal of Forest and Environment, 2022, 42(6): 623-630. ]
[6]	Fitzgerald G, Rodriguez D, O’leary G, et al. Measuring and predicting canopy nitrogen nutrition in wheat using aspectral index-the canopy chlorophyll content index (CCCI)[J]. Field Crops Research, 2010, 116(3): 318-324.
[7]	冯伟, 朱艳, 姚霞, 等. 基于高光谱遥感的小麦叶干重和叶面积指数监测[J]. 植物生态学报, 2009, 33(1): 34-44. doi: 10.3773/j.issn.1005-264x.2009.01.004
	[Feng Wei, Zhu Yan, Yao Xia, et al. Monitoring leaf dry weight and leaf area index in wheat with hyperspectral remote sensing[J]. Chinese Journal of Plant Ecology, 2009, 33(1): 34-44. ] doi: 10.3773/j.issn.1005-264x.2009.01.004
[8]	魏鹏飞, 徐新刚, 李中元, 等. 基于无人机多光谱影像的夏玉米叶片氮含量遥感估测[J]. 农业工程学报, 2019, 35(8): 126-133.
	[Wei Pengfei, Xu Xingang, Li Zhongyuan, et al. Remote sensing estimation of nitrogen content in summer maize leaves based on multispectral images of UAV[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(8): 126-133. ]
[9]	康恺, 张伟, 贺燕, 等. 大豆冠层叶片氮含量检测研究—基于无人机多光谱图像[J]. 农机化研究, 2024, 46(2): 151-156.
	[Kang Kai, Zhang Wei, He Yan, et al. Study on nitrogen content detection of soybean canopy-based on multispectral image of UAV[J]. Journal of Agricultural Mechanization Research, 2024, 46(2): 151-156. ]
[10]	杨欣, 袁自然, 叶寅, 等. 基于无人机高光谱遥感的冬小麦全氮含量反演[J]. 光谱学与光谱分析, 2022, 42(10): 3269-3274.
	[Yang Xin, Yuan Ziran, Ye Yin, et al. Winter wheat total nitrogen content estimation based on UAV hyperspectral remote sensing[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3269-3274. ]
[11]	宋晓, 许端阳, 黄绍敏, 等. 基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型[J]. 应用生态学报, 2020, 31(5): 1636-1644. doi: 10.13287/j.1001-9332.202005.022
	[Song Xiao, Xu Duanyang, Huang Shaomin, et al. Nitrogen content inversion of wheat canopy leaf based on ground spectral reflectance data[J]. Chinese Journal of Applied Ecology, 2020, 31(5): 1636-1644. ] doi: 10.13287/j.1001-9332.202005.022
[12]	李丹, 李斐, 胡云才, 等. 基于光谱指数波段优化算法的小麦玉米冠层含氮量估测[J]. 光谱学与光谱分析, 2016, 36(4): 1150-1157.
	[Li Dan, Li Fei, Hu Yuncai, et al. Study on the estimation of nitrogen content in wheat and maize canopy based on band optimization of spectral parameters[J]. Spectroscopy and Spectral Analysis, 2016, 36(4): 1150-1157. ] pmid: 30052017
[13]	秦占飞, 常庆瑞, 谢宝妮, 等. 基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测[J]. 农业工程学报, 2016, 32(23): 77-85.
	[Qin Zhanfei, Chang Qingrui, Xie Baoni, et al. Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(23): 77-85. ]
[14]	常潇月, 常庆瑞, 王晓凡, 等. 基于无人机高光谱影像玉米叶绿素含量估算[J]. 干旱地区农业研究, 2019, 37(1): 66-73.
	[Chang Xiaoyue, Chang Qingrui, Wang Xiaofan, et al. Estimation of maize leaf chlorophyll contents based on UAV hyperspectral drone image[J]. Agricultural Research in the Arid Areas, 2019, 37(1): 66-73. ]
[15]	尹航, 李斐, 杨海波, 等. 基于无人机高光谱影像的马铃薯叶绿素含量估测[J]. 植物营养与肥料学报, 2021, 27(12): 2184-2195.
	[Yin Hang, Li Fei, Yang Haibo, et al. Estimation of canopy chlorophyll in potato based on UAV hyperspectral images[J]. Journal of Plant Nutrition and Fertilizers, 2021, 27(12): 2184-2195. ]
[16]	Liu H Y, Zhu H C, Wang P. Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hy-perspectral data[J]. International Journal of Remote Sensing, 2017, 38(8-10): 2117-2134.
[17]	付波霖, 邓良超, 张丽, 等. 联合星载高光谱影像和堆栈集成学习回归算法的红树林冠层叶绿素含量遥感反演[J]. 遥感学报, 2022, 26(6): 1182-1205.
	[Fu Bolin, Deng Liangchao, Zhang Li, et al. Estimation of man-grove canopy chlorophyll content using hyperspectral im-age and stacking ensemble regression algorithm[J]. National Remote Sensing Bulletin, 2022, 26(6): 1182-1205. ]
[18]	Zhang Y, Xia C Z, Zhang X Y, et al. Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images[J]. Ecological Indicators, 2021, 129.
[19]	李玉霞, 杨武年, 童玲, 等. 基于光谱指数法的植被含水量遥感定量监测及分析[J]. 光学学报, 2009, 29(5): 1404-1405.
	[Li Yuxia, Yang Wunian, Tong Ling, et al. Remote sensing quantitative monitoring and analysis of fuel moisture content based on spectral index[J]. Acta Optica Sinica, 2009, 29(5): 1404-1405. ]
[20]	左璐, 王焕炯, 刘荣高, 等. 基于不同光谱指数的植被物候期遥感监测差异[J]. 应用生态学报, 2018, 29(2): 599-603. doi: 10.13287/j.1001-9332.201802.019
	[Zuo Lu, Wang Huanjiong, Liu Ronggao, et al. Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices[J]. Chinese Journal of Applied Ecology, 2018, 29(2): 599-603. ] doi: 10.13287/j.1001-9332.201802.019
[21]	Gitelson A, Merzlyak M N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation[J]. Journal of Plant Physiology, 1994, 143(3): 286-292.
[22]	赖佳政, 李贝贝, 程翔, 等. 基于无人机高光谱遥感的烤烟叶片叶绿素含量估测[J]. 智慧农业(中英文), 2023, 5(2): 68-81.
	[Lai Jiazheng, Li Beibei, Cheng Xiang, et al. Monitoring of leaves chlorophyll content in flue-cured tobacco based on hyperspectral remote sensing of unmanned aerial vehicle and machine learning[J]. Smart Agriculture, 2023, 5(2): 68-81. ] doi: 10.12133/j.smartag.SA202303007
[23]	王玉娜, 李粉玲, 王伟东, 等. 基于无人机高光谱的冬小麦氮素营养监测[J]. 农业工程学报, 2020, 36(22): 31-39.
	[Wang Yuna, Li Fenling, Wang Weidong, et al. Monitoring of winter wheat nitrogen nutrition based on UAV hyperspectral images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(22): 31-39. ]
[24]	高兴, 李斐, 杨海波, 等. 基于红边位置的马铃薯植株氮浓度估测方法研究[J]. 植物营养与肥料学报, 2019, 25(2): 296-310.
	[Gao Xing, Li Fei, Yang Haibo, et al. Appropriate calculation method for the use of red edge position to estimate potato nitrogen concentration[J]. Journal of Plant Nutrition and Fertilizers, 2019, 25(2): 296-310. ]
[25]	Li C, Chen P, Ma C Y, et al. Estimation of potato chlo-rophyll content using composite hyperspectral index parameters collected by an unmanned aerial vehicle[J]. International Journal of Remote Sensing, 2020, 41(21): 8176-8197.
[26]	杨海波, 李斐, 张加康, 等. 基于高光谱指数估测马铃薯植株氮素浓度的敏感波段提取[J]. 植物营养与肥料学报, 2020, 26(3): 541-551.
	[Yang Haibo, Li Fei, Zhang Jiakang, et al. The deriving of sensitive wave band for the estimation of plant nitrogen concentration in potato based on hyperspectral indices[J]. Journal of Plant Nutrition and Fertilizers, 2020, 26(3): 541-551. ]
[27]	罗丹, 常庆瑞, 齐雁冰, 等. 基于光谱指数的冬小麦冠层叶绿素含量估算模型研究[J]. 麦类作物学报, 2016, 36(9): 1225-1233.
	[Luo Dan, Chang Qingrui, Qi Yanbing, et al. Estimation model for chlorophyll content in winter wheat canopy based on spectral indices[J]. Journal of Triticeae Crops, 2016, 36(9): 1225-1233. ]
[28]	班松涛, 田明璐, 常庆瑞, 等. 基于无人机高光谱影像的水稻叶片磷素含量估算[J]. 农业机械学报, 2021, 52(8): 163-171.
	[Ban Songtao, Tian Minglu, Chang Qingrui, et al. Estimation of rice leaf phosphorus content using UAV-based hyperspectral images[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(8): 163-171. ]
[29]	杨福芹, 李蕊, 冯海宽, 等. 不同生育期冬小麦植株氮含量遥感反演方法比较[J]. 东北农业科学, 2023, 48(3): 118-124.
	[Yang Fuqin, Li Rui, Feng Haikuan, et al. Comparison of hyperspectral remote sensing inversion methods for plant nitrogen content in different growth stages[J]. Journal of Northeast Agricultural Sciences, 2023, 48(3): 118-124. ]
[30]	张文旭, 佟炫梦, 周天航, 等. 基于高光谱成像的棉花叶片氮素含量遥感估测[J]. 沈阳农业大学学报, 2021, 52(5): 586-596.
	[Zhang Wenxu, Tong Xuanmeng, Zhou Tianhang, et al. Remote sensing estimation of cotton leaf nitrogen content based on hyperspectral imaging[J]. Journal of Shenyang Agricultural University, 2021, 52(5): 586-596. ]

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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