小麦叶层氮含量估测的最佳高光谱参数研究

 【目的】作物体内氮素状况是评价长势和预测产量的重要指标。小麦植株氮素营养的快速监测和无损诊断对于精确氮素管理具有重要作用。本文旨在通过对高光谱信息的精细分析和信息提取,探索建立小麦叶片氮含量（LNC,leaf nitrogen content）估算的最佳波段、光谱参数及监测模型。【方法】利用连续4年的系统观测资料,采用精细采样法,详细分析350～2 500 nm波段范围内原始光谱反射率及其一阶导数光谱的任意两两波段组合而成的主要高光谱指数与小麦冠层叶片氮含量的定量关系。【结果】发现小麦叶片氮含量的最佳波段为位于红边的690、691、700和711 nm以及近红外波段的1 350 nm;基于归一化光谱指数NDSI（R1350,R700）和NDSI（FD700,FD690）、比值光谱指数RSI（R700,R1350）和RSI（FD691,FD711）、土壤调节光谱指数SASI（R1350,R700）（L=0.09）和SASI（FD700,FD690）（L=-0.01）构建氮含量监测模型,决定系数（R2）分别为0.851和0.857、0.842和0.893、0.860和0.866。利用独立试验资料对模型检验的结果显示,模型测试的精度（R2）均大于0.758,RRMSE均小于0.266,尤其是高光谱参数RSI（FD691,FD711）和SASI（FD700,FD690）表现最好。【结论】总体上,利用精细采样法确定最佳波段,构建植被指数和氮含量监测模型,可显著提高模型的精确度和可靠性,从而为快速无损诊断小麦叶层的氮素状况提供新的波段选择和技术途径。     

基于不同土壤质地的小麦叶片氮含量高光谱 差异及监测模型构建

【目的】叶片氮素状况是小麦生产中精确施氮管理与调控的前提,实时无损监测叶片氮素状况对小麦生产管理具有重要意义。本文旨在综合分析不同环境下小麦冠层光谱响应差异,进而构建其估测模型,为小麦氮肥合理运筹提供技术支持。【方法】本研究基于3种不同土壤质地（砂土、壤土和黏土）、5种不同施氮水平（0、120、225、330和435 kg•hm-2）及3种河南省主栽小麦品种（矮抗58、周麦22和郑麦366）连续2年的大田试验,于小麦主要生育时期同步测定冠层光谱反射率和叶片氮含量,对3种不同土壤质地条件下小麦冠层叶片氮含量的高光谱响应差异进行比较,系统分析350—1 050 nm 波段范围内任意两波段组合而成的差值（DSI）、比值（RSI）及归一化差值（NDSI）光谱指数与叶片氮含量的量化关系,并建立估算模型。【结果】冠层光谱反射率在不同施氮水平和不同生育时期下存在明显差异,但趋势基本一致;比较3种土壤质地小麦冠层光谱反射率大小表现为：黏土＞壤土＞砂土,可以反映小麦实时田间长势。通过系统分析3种土壤质地小麦冠层反射光谱与对应叶片氮含量间的定量关系,表明在可见光和近红外区域均有较好的相关性,但敏感波段区域有所不同。对3种质地获取的样本进行系统分析表明,砂土、壤土和黏土质地小麦叶片氮含量分别以光谱指数NDSI（FD710,FD690）、DSI（R515,R460）和RSI（R535,R715）建模结果表现最好,决定系数分别达到0.88、0.87和0.87。经不同年份独立资料检验结果显示,基于上述光谱指数估测小麦叶片氮含量的预测决定系数分别为0.87、0.85和0.77,预测均方根误差分别为0.31、0.32和0.26。【结论】利用光谱参数NDSI（FD710,FD690）、DSI（R515,R460）和RSI（R535,R715）为自变量建立的估测模型分别可以较好地预测砂土、壤土和黏土3种质地小麦叶片氮含量。     

Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance

Bacterial leaf blight (BLB) is an important vascular disease of irrigated rice and serious infestations may cause a significant loss of yield. This study analyzed hyperspectral canopy reflectance spectra of two rice cultivars with different susceptibilities to BLB to establish spectral models for assessing disease severity for future site-specific management. The results indicated that wavebands from 757 to 1039 nm were the most sensitive region of the spectrum for the moderately susceptible cultivar TNG 67, whereas most narrow bands showed a significant relationship for the highly susceptible cultivar TCS 10. All the spectral indices (SIs) calculated had significant relationships with proportions of infested area in cultivar TCS 10, but only two SIs correlated significantly with cultivar TNG 67. The relation between the severity of the disease and spectral reflectance for the less susceptible cultivar TNG 67 can be improved by using a multiple linear regression approach.     

一种新的估算水稻上部叶片蛋白氮含量的植被指数

 【目的】阐明水稻顶部4张叶片蛋白氮含量和反射光谱特征的变化规律及其相互关系,建立快速、准确诊断水稻功能叶片蛋白氮含量的方法。【方法】通过3年不同施氮水平和不同品种类型的大田试验,分生育期同步测定顶部4张叶片的光谱反射率及蛋白氮含量,系统分析叶片蛋白氮含量与多种高光谱参数的定量关系。【结果】水稻叶片蛋白氮含量和光谱反射率在不同施氮水平、不同生育期及不同叶位间均存在明显差异,叶片蛋白氮含量的敏感波段主要存在于可见光绿光区530～580 nm及红边区域695～715 nm,其中红边区域表现最为显著。红边区域700 nm附近波段与近红外短波段的比值组合(SRs)可以有效地估算水稻上部功能叶片的蛋白氮含量,其次是绿光区587 nm左右的波段与近红外短波段的比值组合。基于新提出的SR（770,700）及已报道的GM-2、SR705、RI-half光谱指数,线性回归模型的拟合精度（R2）分别达到 0.874,0.873,0.871和0.867。经独立资料的检验表明,这些回归模型可以实时监测叶片蛋白氮含量变化,预测精度R2分别为0.810、0.806、0.804和0.800,相对误差RE 分别为12.1%、12.4%、12.6%和12.9%。【结论】可以利用关键特征光谱指数来诊断水稻上部叶片的蛋白氮含量状况,尤以SR（770,700）、GM-2、SR705和RI-half表现为较强的估测能力。     

基于高光谱的水稻叶片含水量监测研究

【目的】建立快速、无损诊断水稻叶片含水量的估测模型,为水稻水分精确管理提供依据。【方法】基于2年不同土壤水分处理和水稻品种的池栽试验,于水稻主要生育时期同步测定顶部4张叶片的光谱反射率和含水量,系统分析350-2 500 nm波段范围内任意两波段组合而成的比值（RSI）、归一化差值（NDSI）及差值（DSI）光谱指数,并分析其与叶片含水量的量化关系。【结果】不同土壤水分处理和叶位间,叶片反射光谱具有显著的时空变化特征,叶片含水量的敏感光谱波段主要位于近红外及短波红外区域;RSI （R1402, R2272）及NDSI （R1402, R2272）光谱指数与叶片含水量呈现良好的线性相关,线性拟合R2均达到0.80。基于独立试验资料对所建模型进行测试检验也显示,预测值和观察值的拟合R2也均达到0.86。【结论】RSI（R1402, R2272）、NDSI（R1402, R2272）均可用于水稻叶片含水量的定量监测。     

基于高光谱植被指数的马铃薯叶片叶绿素含量估测模型

【目的】筛选相关性好的植被指数构建马铃薯叶片叶绿素a、叶绿素b估测模型,为科学、无损地进行马铃薯叶片叶绿素含量估算提供技术支撑。【方法】采用便携式高光谱地物波谱仪,获取不同施氮水平下不同生育时期的马铃薯植株叶片光谱反射率,提取植被指数,测定马铃薯叶片叶绿素a、叶绿素b含量,并研究叶绿素含量与植被指数的相关性。【结果】12个植被指数与叶绿素a、叶绿素b含量相关性较好,其中修正归一化差异指数(mND_(705))、修正简单比值指数(mSR_(705))、地面叶绿素指数(MTCI)、修改叶绿素吸收反射指数(MCARI)与叶绿素a、叶绿素b含量相关性最好。基于这4个植被指数建立的估测模型中,MTCI构建的乘幂模型估测叶绿素a含量的效果最佳,mND_(705)构建的指数模型估测叶绿素b含量的效果最佳。【结论】MTCI构建的乘幂模型能较为精确地估测叶绿素a含量,mND_(705)构建的指数模型能较为精确地估测叶绿素b含量;这2种模型可用于间接监测马铃薯植株的氮营养亏缺状态。     

Using hyperspectral remote sensing techniques to monitor nitrogen,phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)

In situ, non-destructive and real time mineral nutrient stress monitoring is an important aspect of precision farming for rational use of fertilizers. Studies have demonstrated the ability of remote sensing to monitor nitrogen (N) in many crops, phosphorus (P) and potassium (K) in very few crops and none so far to monitor sulphur (S). Specially designed (1) fertility gradient experiment and (2) test crop experiments were used to check the possibility of mineral N–P–S–K stress detection using airborne hyperspectral remote sensing. Leaf and canopy hyperspectral reflectance data and nutrient status at booting stage of the wheat crop were recorded. N–P–S–K sensitive wavelengths were identified using linear correlation analysis. Eight traditional vegetation indices (VIs) and three proposed (one for P and two for S) were evaluated for plant N–P–S–K predictability. A proposed VI (P_1080_1460) predicted P content with high and significant accuracy (correlation coefficient (r) 0.42 and root means square error (RMSE) 0.180 g m^?2). Performance of the proposed S VI (S_660_1080) for S concentration and content retrieval was similar whereas prediction accuracies were higher than traditional VIs. Prediction accuracy of linear regressive models improved when biomass-based nutrient contents were considered rather than concentrations. Reflectance in the SWIR region was found to monitor N–P–S–K status in plants in combination with reflectance at either visible (VIS) or near infrared (NIR) region. Newly developed and validated spectral algorithms specific to N, P, S and K can further be used for monitoring in a wheat crop in order to undertake site-specific management.     

基于随机森林法的农作物高光谱遥感识别

  目的  不同农作物种类光谱差异小,通过探测众多窄波段范围的细微差别,提取区分不同农作物的特征波段,是目前实现农作物高光谱遥感识别的重要途径。如何提取区分不同农作物的特征波段,进而实现农作物的精确识别是一个挑战。近来出现的随机森林方法在多变量目标的分类识别方法展现了优势,为解决这一难题提供了一个新手段。  方法  利用随机森林法与传统方法分析杭州地区8种典型农作物的反射光谱,提取特征波段并进行分类,对比不同方法的识别效果。  结果  不同作物的反射光谱及其一阶微分、二阶微分、倒数的对数、去包络线法所提取的特征波段只能区分部分作物;随机森林法无需对反射光谱预处理,直接对全波段反射光谱数据处理,不仅筛选出了区分不同作物的特征波段,且运用所选择的波段对作物进行随机森林分类的效果也是最优的。  结论  随机森林法选择的波段（550、2 490、370、770、560、380、540、530、570、350 nm）不仅能区分不同作物,还能反映农作物生化属性的不同,使得用于分类的波段及分类方法体现了不同作物间物化性质的不同,在展现高光谱遥感识别农作物优势的同时,也为大面积农作物遥感精细分类提供借鉴。     

A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands

This paper follows previous research that identified 15 hyperspectral wavebands that were suitable to estimate paddy rice leaf area index (LAI). The objectives of the study were to: (1) test the efficiency of the wavebands selected in the previous study, (2) to evaluate the potential of least squares support vector machines (LS-SVM) to estimate paddy rice LAI from canopy hyperspectral reflectance and (3) to compare multiple linear regression-MLR, partial least squares-PLS regression and LS-SVM to determine paddy rice LAI using the selected wavebands. In the study, measurements of hyperspectral reflectance (350–2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing seasons. On the basis of the wavebands selected previously, models based on MLR, PLS and LS-SVM to estimate rice LAI were compared using the data from 123 observations, which were split randomly for model calibration (2/3) and validation (1/3). Root mean square errors (RMSEs) and the correlation coefficients (r) between measured and predicted LAI values from model calibration and validation were calculated to evaluate the quality of the models. The results showed that the LS-SVM model using the 15 selected wavebands produced more accurate estimates of paddy rice LAI than the PLS and MLR models. We concluded that the LS-SVM approach may provide a useful exploratory and predictive tool for estimating paddy rice LAI when applied to reflectance data using the 15 selected wavebands.     

小麦氮素积累动态的高光谱监测

 【目的】研究小麦地上部氮积累量与冠层高光谱参数的定量关系,分析多种高光谱参数估算地上部氮积累量的效果。【方法】连续3年采用不同蛋白质含量的小麦品种在不同施氮水平下进行大田试验,于小麦不同生育期采集田间冠层高光谱数据并测定植株不同器官生物量和氮含量。【结果】植株氮积累量随着施氮水平的提高而增加,不同地力水平间存在明显差异。植株氮积累量的光谱敏感波段主要存在于近红外平台和可见光区,而地上部氮积累量与冠层光谱的相关性明显降低。对植株氮积累量的光谱估算,在不同品种、氮素水平、生育时期和年度间可以使用统一的光谱模型。在籽粒灌浆期间植株氮积累量自开花期随时间进程的积分累积值与对应时期籽粒氮素积累状况存在显著的定量关系,根据特征光谱参数植株氮素营养籽粒氮积累量这一技术路径,以植株氮积累量为交接点将模型链接,建立高光谱参数与籽粒氮积累量间定量方程。将植株氮积累量与籽粒氮积累量相加,确立了基于高光谱参数的籽粒灌浆期间地上部氮积累量监测模型。经不同年际独立资料的检验表明,利用光谱参数SDr/SDb、VOG2、VOG3、RVI（810,560）、[（R750-800）/（R695-740）]-1和Dr/Db建立模型可以实时监测小麦地上部氮素积累动态变化,预测精度R2分别为0.774、0.791、0.803、0.803、0.802和0.778,相对误差RE分别为16.7%、15.5%、15.6%、18.5%、15.5%和17.3%。【结论】利用关键特征光谱参数可以有效地评价小麦地上部氮素积累状况,其中尤以植被指数VOG2、VOG3和[（R750-800）/（R695-740）]-1的效果更好。     

基于高光谱苹果品种叶片铁含量估测模型

【目的】 在叶片水平上构建基于高光谱的苹果品种叶片铁素含量估测模型,为探寻实时、高效、无损的果树树体营养诊断提供技术途径。【方法】以苹果品种岩富10号为材料,测定岩富10号叶片光谱数据和铁素含量,采用光谱分析和相关分析法,筛选与叶片铁素含量相关性较强的光谱组合,利用偏最小二乘法构建苹果叶片铁素含量光谱估测模型。【结果】岩富10号苹果叶片一阶微分光谱与铁素含量的敏感波段为R′₉₉₀、R′_{1 113}、R′_{1 360}、R′_{1 408},相关系数最高为-0.698 9。对敏感波段两两进行加、减、乘、除运算,最优波段组合形式R′₉₉₀×R′_{1 048}与铁素含量相关系数为0.846 2。估测模型拟合度(R²)最高为0.827 5。【结论】苹果叶片一阶微分光谱组合与铁素含量显著相关(P<0.05),光谱组合能够明显提高其相关性,偏最小二乘法与逐步回归建模相比估算模型的精度更佳,可以用于苹果叶片铁素含量的光谱估算。     

夏玉米光谱特征对其不同色素含量的响应差异

在不同施氮水平夏玉米的6个典型生育期,采用化学方法测定冠层叶绿素含量,利用叶绿素计测定的叶绿素读数以及光谱反射率,系统分析了单波段反射率、可见光和近红外波段组合而成的归一化植被指数(NDVI)、比值植被指数(RVI)等8种常见植被指数与相应时期2种方法测定的叶绿素含量的相关性。结果表明,随着施氮量的增加,叶绿素含量和冠层近红外波段反射率都随之增加;整个生育期中孕穗期在近红外区域反射率最高,与可见光波段反射率相差最大;6个生育期单波段510~1 100 nm反射率、NDVI、RVI等植被指数与叶绿素含量的2种测定结果显著相关或极显著相关,植被指数的表现较单波段更好,且从苗期到乳熟期,各波段反射率与叶绿素的相关性逐渐增强。整体来讲,可见光中560、660 nm和近红外760、810、590和1 300 nm组合的NDVI在各生育期与2个农学指标的相关性较好,选择NDVI(560,760)可以准确拟合夏玉米叶片叶绿素含量,其对化学方法测定的叶绿素含量拟合效果较佳。     

Canopy reflectance response to plant nitrogen accumulation in rice

Tools to quantify the nitrogen (N) status of a rice canopy during inter-nodal elongation (IE) would be valuable for mid-season N management because N accounts for the largest input cost. The objective of this paper was to study canopy reflectance as a potential tool for assessing the mid-season status of N in a rice crop. Three field plot experiments were conducted in 2002 and 2003 on cultivars Wells and Cocodrie to study the canopy reflectance response of rice to plant N accumulation (PNA) during IE and to identify the wavelengths and vegetation indices that are good indicators of PNA. Each experiment included six pre-flood N treatments of 0, 33.6, 67.2, 100.8, 133.4 and 168 kg N ha⁻¹. Rice canopy reflectance, biomass, tissue N concentration and PNA were measured weekly during IE. The wavelengths most strongly correlated to PNA at the beginning of IE were 937 and 718 nm. Several vegetation indices were examined to determine which were strongly correlated (>0.7) with PNA at the beginning of IE. Multiple linear regression models of PNA on selected vegetation indices explained 53–85% of the variation in PNA during the first week of IE. This study identifies the best combinations of vegetation indices for estimating PNA in rice.     

基于光谱指数的棉花黄萎病叶片氮素含量提取

[目的]研究棉花黄萎病叶片氮素含量与高光谱的关系,以期用简便、无损的遥感技术提取病害棉叶氮素含量,为大面积遥感监测棉花病害提供理论依据.[方法]通过小区和大田同步调查棉花黄萎病,在不同生育期测定病叶光谱及其氮素含量.将病叶光谱特征参数与氮素含量进行相关分析,建立病叶氮素含量估测模型并检验.[结果]随着病害严重度的增加,棉叶氮素含量逐渐减小.病叶氮素含量与光谱指数FD731、NDVI[670,890]、DVI[FD554,FD731]、PVI[FD554,FD731]、RDVI[702,758]、RDVI[FD554,FD731]、SAVI、OSAVI、PRI[570,531]、PRI[702,758]、REP、Lo、Depth672和Area672呈极显著正相关,与R550、R680、R702、SD737、DVI[450,560]、NDVI[702,758]、DVI[702,758]、RVI[702,758]、SIPI、TCARI、CCII、PPR[550,450]、Lwidth和ND672均呈极显著负相关,与Dr未达显著相关.选取相关系数较大的光谱参数建立的病叶氮素含量估测模型均达到显著水平,整体上利用DVI[702,758]、PVI[FD554,FD731]和NDVI[702,758]进行氮素含量的估测精度最高,模型的预测的相对误差均小于2;.[结论]考虑到DVI[702,758]建立的模型更为实用,可作为病害棉叶氮素含量的最佳估测模型.     

Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields

Remote sensing-based nitrogen (N) management has been evaluated in many crops. The water background and wide range of varieties in rice (Oryza sativa), are unique features that require additional consideration when using sensor technology. The commonly calculated normalized difference vegetation index is of limited use when the crop has reached complete canopy closure. The objective of this research was to evaluate mid-season agronomic parameter and grain yield prediction models along with the effect of water background and of different varieties using a red- and red-edge-based vegetation index. Varieties × N trials were established at the LSU AgCenter Rice Research Station located in Crowley, Louisiana in 2011 and 2012. Canopy spectral reflectance under clear and turbid water, biomass yield, N content, plant coverage, and water depth were collected each week for three consecutive weeks beginning 2 weeks before panicle differentiation. Grain yield was also determined. Water turbidity had an influence on spectral reflectance when canopy coverage was less than 50 %. While water depth influenced red reflectance, this was not carried over when reflectance was transformed to vegetation indices. The red-edge-based vegetation indices, especially those computed by ratio, had stronger relationships with measured agronomic parameters as compared with red-based indices. Furthermore, the effect of variety on the yield prediction model was observed using derivative-based red-edge indices but not with other ratio-based indices. Future researches should focus on developing a generalized yield prediction model using ratio-based red-edge indices across different varieties to extend its applicability in production fields.     

基于EFAST方法的苹果叶片叶绿素含量估算

为了快速、准确地估算叶绿素含量,使用2012年和2013年在山东省肥城市潮泉镇获取的整个生育期苹果叶片叶绿素含量和配套的光谱数据,利用PROSPECT模型和EFAST方法探讨了对叶绿素含量敏感的波段,然后采用经验统计方法实现了单波段高光谱对苹果叶片叶绿素含量的监测。结果表明:以571 nm和697 nm波段光谱参数为自变量所建立的估测模型拟合精度较高,其决定系数(R2)分别为0.71和0.69,均方根误差(RMSE)分别为1.14、1.17 mg/dm~2,相对误差(RE)分别为-1.07%和-1.01%。以PROSPECT模型和EFAST方法整合筛选的敏感波段建立的估算模型监测叶绿素含量效果较好,为利用高光谱技术监测苹果长势提供了理论依据。     

Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate

Glyphosate is a non-selective, systemic herbicide highly toxic to sensitive plant species. Its use has seen a significant increase due to the increased adoption of genetically modified glyphosate-resistant crops since the mid-1990s. Glyphosate application for weed control in glyphosate-resistant crops can drift onto an off-target area, causing unwanted injury to non-glyphosate resistant plants. Thus, early detection of crop injury from off-target drift of herbicide is critical in crop production. In non-glyphosate-resistant plants, glyphosate causes a reduction in chlorophyll content and metabolic disturbances. These subtle changes may be detectable by plant reflectance, which suggests the possibility of using optical remote sensing for early detection of drift damage to plants. In order to determine the feasibility of using optical remote sensing, a greenhouse study was initiated to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were treated with glyphosate using a pneumatic track sprayer in a spray chamber. The three treatment groups were control (0 kg ae/ha), low dosage (0.086 kg ae/ha), and high dosage (0.86 kg ae/ha), each with four 2-plant pots. Hyperspectral images were taken at 4, 24, 48, and 72 h after application. The extracted canopy reflectance data was analyzed with vegetation indices. The results indicated that a number of vegetation indices could identify crop injury at 24 h after application, at which time visual inspection could not distinguish between glyphosate injured and non-treated plants. To improve the results a modified method of spectral derivative analysis was proposed and applied to find that the method produced better results than the vegetation indices. Four selected first derivatives at wavelength 519, 670, 685, and 697 nm could potentially differentiate crop injury at 4 h after treatment. The overall false positive rate was lower than the vegetation indices. Furthermore, the derivatives demonstrated the ability to separate treatment groups with different dosages. The study showed that hyperspectral imaging of plant canopy reflectance could be a useful tool for early detection of soybean crop injury from glyphosate, and that the modified spectral derivative analysis had a better performance than vegetation indices.     

Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data

In the context of a growing interest in remote sensing for precision agriculture applications, the utility of space-borne hyperspectral imaging for the development of a crop-specific spectral library and automatic identification and classification of three cultivars for each of rice (Oryza sativa L.), chilli (Capsicum annuum L.), sugarcane (Saccharum officinarum L.) and cotton (Gossipium hirsutum L.) crops have been investigated in this study. The classification of crops at cultivar level using two spectral libraries developed using hyperspectral reflectance data at canopy scale (in-situ hyperspectral measurements) and at pixel scale (Hyperion data) has shown promising results with 86.5 and 88.8% overall classification accuracy, respectively. This observation highlights the possible integration of in-situ hyperspectral measurements with space-borne hyperspectral remote sensing data for automatic identification and discrimination of various crop cultivars. However, considerable spectral similarity is observed between cultivars of rice and sugarcane crops which may pose problems in the accurate identification of various crop cultivars.     

不同氮处理春玉米叶片光谱反射率与叶片全氮和叶绿素含量的相关研究

 采用盆栽试验研究了不同氮营养水平下的春玉米叶片叶绿素和全氮含量与叶片光谱反射率的相关性。结果表明，拔节期和喇叭口期是玉米氮素光谱营养诊断的敏感时期；利用绿峰处叶片最大光谱反射率反演玉米叶片氮素含量和叶绿素含量的精度为：喇叭口期＞拔节期＞开花吐丝期；不同生育时期诊断玉米叶片氮素含量和叶绿素含量时所采用的光谱波段也不同，拔节期和喇叭口期采用可见光波段的光谱反射率可靠性较高，而开花吐丝期采用近红外波段的光谱反射率可靠性较高；两波段组合光谱变量对叶片叶绿素和全氮含量的判别精度高于单一波段的判别精度。     

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

The use of remote sensing to monitor nitrogen(N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden. In this study, we model the total leaf N concentration(TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution(VND). The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model. The results show that:(1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer.(2) The effective layer for remote sensing detection varied at different growth stages. The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively.(3) The TLNC model considering the VND has high predicting accuracy and stability. For models based on the greenness index(GI), mND705(modified normalized difference 705), and normalized difference vegetation index(NDVI), the values for the determining coefficient(R2), and normalized root mean square error(nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively. Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.