基于多时相和多角度光谱信息的作物株型遥感识别初探

作物群体具有一定的冠层几何结构(株型)，对于不同株型的品种，在相同的叶面积指数时冠层反射光谱往往不同，使得利用冠层反射光谱来反演叶面积指数等生物物理和生物化学参数时存在不同株型产生的误差，该文定量研究了不同叶面积指数条件下，作物株型对冠层反射光谱的影响，并提出运用波长800 nm处起身期的冠层反射光谱与该波长处拔节期和起身期冠层反射光谱的比值，可以初步实现高密度披散型品种、低密度披散型品种、高密度中间型品种、低密度中间型品种、高密度直立型品种和低密度直立型品种的遥感识别，结合一定条件下选取的15°、30°和45°观测天顶角下，与可见光和近红外波段(波长)处的二向反射冠层反射光谱数值大小进行结合，可以初步实现作物株型的遥感识别。     

不同筋力冬小麦冠层反射光谱的差异分析

为了利用冠反射光谱特征监不同筋力小麦品种的生理特征差异,利用不同筋力小麦冠层反射光谱的差异,可对不同小麦品种进行遥感识别与监测。试验以低筋小麦品种扬麦13和高筋小麦品种徐麦31为材料,结合不同生育时期两品种叶面积指数(LAI)、叶绿素含量和叶片氮含量的变化,以及相应的光谱参数,分析不同筋力小麦冠层反射光谱的变化特征。结果表明,在近红外和可见光波段,从拔节期到蜡熟期,扬麦13的冠层光谱反射率均高于徐麦31,在孕穗期两品种的差异最显著;LAI、叶片叶绿素和氮含量均在开花时达最大值,扬麦13的叶绿素含量明显高于徐麦31,而LAI和叶片氮含量则低于徐麦31。比值植被指数(RVI)、归一化植被指数(NDVI)与LAI;红边位置(λr)、红边幅值(Dr)与叶绿素含量,氮素反射指数(NRI)、抗大气植被指数(VARIgreen)与叶片氮含量极显著相关,表明RVI、NDVI可以反演LAI;λr、Dr可以反演叶绿素;NRI、VARIgreen可以反演叶片氮含量的变化。以上光谱参数能反映小麦相关指标的变化情况,不同时期可运用小麦冠层反射光谱进行不同筋力小麦品种识别,孕穗期为最佳识别时期。通过本研究,以期为不同筋力小麦品种的遥感识别提供依据。     

利用无人机多光谱估算小麦叶面积指数和叶绿素含量

利用无人机遥感的方式进行农作物长势监测是目前精准农业、智慧农业发展的重要方向,为了探究无人机多光谱反演小麦叶面积指数(Leaf Area Index,LAI)和叶绿素含量的模型估算潜力,该研究在3个飞行高度(30、60、120 m)采集多光谱影像,通过使用全波段差值光谱指数(Difference Spectral Index,DSI)、比值光谱指数(Ratio Spectral Index,RSI)、归一化光谱指数(Normalized Spectral Index,NDSI)和经验植被指数与地面实测数据进行相关性分析,获得不同高度下的光谱指数与LAI和叶绿素含量的关系模型及其决定系数,以决定系数为依据分别构建多元逐步回归、偏最小二乘回归和人工神经网络模型,分析不同飞行高度无人机多光谱反演小麦冠层LAI和叶绿素含量SPAD(Soil and Plant Analyzer Development)值的精度。结果表明:1)30 m高度下,绿-红比值光谱指数与小麦LAI的相关性最高,相关系数为0.84;60 m高度下,红-蓝比值光谱指数与小麦叶绿素含量的相关性最高,相关系数为0.68;2)在60 m高度下,经验植被指数与小麦LAI和叶绿素含量的相关性较好,最大相关系数分别为0.77和0.50;3)利用偏最小二乘回归反演小麦LAI的精度最高,决定系数为0.732,均方根误差为0.055;利用人工神经网络模型反演小麦叶绿素含量的精度最高,决定系数为0.804,均方根误差0.135。该研究成果可为基于无人机平台的高通量作物监测提供理论依据,并为筛选无人机多光谱波段实现作物长势参数快速估测提供应用参考。     

基于棉花红边参数的叶绿素密度及叶面积指数的估算

利用野外非成像高光谱仪,测试棉花两个品种4种配置种植方式两年关键生育时期的冠层反射光谱数据,应用光谱微分技术,获取棉花微分光谱680～750 nm波段的红边参数：红边面积（SDr）、红边斜率（Dr）以及红边位置（λr）变量;将棉花红边面积、红边斜率分别与其冠层叶绿素密度（CH.D）、叶面积指数（LAI）进行相关分析,它们的相关性均达到1%极显著水平,其中红边面积与叶绿素密度的相关性最好（RCH.D=0.8787**,n=137）;并且红边面积较红边斜率对叶绿素密度、叶面积指数的预测精度更高。以棉花新陆早13号和19号为建模样本,通过红边面积与叶绿素密度的线性相关模型,分别反演新陆早13号、19号冠层叶片的叶绿素密度,结果表明对这两个棉花品种的叶绿素密度估算精度分别达87.4%和83.3%,说明高光谱红边参数是估算棉花叶绿素密度和叶面积指数的一种简单、快捷、非破坏性的有效方法。     

冬小麦条锈病的光谱特征及遥感监测

该文通过人工田间诱发不同等级条锈病，在不同生育期内对不同发病等级(不同病情指数)的冬小麦条锈病冠层光谱进行测定，并同步进行条锈病病情指数的调查。定性和定量地分析了病害区与对照区处理的冬小麦冠层光谱在绿光区、黄光区和近红外区反射特征差异及叶绿素含量变化；并将病情指数及光谱数据进行相关分析，研究表明：630～687nm、740～890 nm及976～1350 nm为遥感监测条锈病的敏感波段；绿光区、近红外平台处及黄光区的冠层光谱反射率分别随病情的加重呈明显的上升、下降与上升趋势；条锈病的红边发生蓝移；叶绿素含     

无人机影像反演玉米冠层LAI和叶绿素含量的参数确定

小型低空无人机（unmanned aerial vehicle, UAV）机动灵活、操作简便，可以按需获取高空间分辨率影像，是育种玉米长势监测的一种重要技术手段。针对UAV影像反演玉米冠层叶面积指数（LAI, Leaf Area Index）和叶绿素含量的参数确定问题，该研究以DJI S1000+无人机为平台，搭载法国Parrot Sequoia相机，获取海南三亚市崖城玉米育种基地的多光谱影像。基于预处理后的UAV影像，采用重采样的方式获得不同分辨率下（0.1～1 m）的不同植被指数，所构建的植被指数包括归一化植被指数（normalized difference vegetation index，NDVI）、叶绿素指数（grassland chlorophyll index，GCI）、比值植被指数（ratio vegetation index，RVI）、归一化红边红指数（normalized difference rededge-red index，NDIrer）、归一化红边绿指数(normalized difference rededge-green index，NDIreg)和重归一化植被指数(renormalized difference vegetation index，RDVI)，通过将不同分辨率下的不同植被指数与地面实测数据进行回归分析，以获得各分辨率下植被指数与冠层LAI和叶绿素含量的关系模型及其决定系数，以决定系数的大小为依据来确定玉米冠层LAI和叶绿素含量反演的最优空间分辨率和最优植被指数。通过试验发现，在分辨率为0.6 m时，NDVI与地面实测LAI之间的决定系数R2为0.80，决定系数达到了最大，利用该分辨率下的NDVI反演得到的LAI验证精度R2达到了0.73；在分辨率为0.1 m时，NDIreg与地面实测叶绿素含量之间的决定系数R2为0.70，决定系数达到了最大，利用该分辨率下的NDIreg反演得到的叶绿素含量验证精度R2达到了0.63。因此得出结论：1）植被指数的选择：① 对于玉米冠层LAI的反演来说，不包含绿波段的植被指数的LAI反演精度较高，这说明绿波段对LAI的变化不敏感；② 对于玉米冠层叶绿素含量反演来说，包含红边波段的植被指数的反演精度较高，因此影像的红边波段对叶绿素含量的变化非常敏感。2）UAV影像空间分辨率的选择：反演LAI的最优分辨率是0.6 m，此时NDVI与实测LAI的决定系数达到最大；反演冠层叶绿素含量的最优分辨率是0.1～0.3 m范围内，此时NDIreg与实测叶绿素含量的决定系数达到最大。该研究可为UAV反演玉米表型参数时的分辨率和植被指数选择提供参考。     

基于高光谱的冬小麦氮素营养指数估测

为了准确定量诊断氮素状况,为施肥和产量、品质的估测提供参考,该文通过设置不同氮素水平和品种类型的冬小麦田间试验,分析孕穗至灌浆初期不同光谱参数在小麦氮素营养状况监测上的差异,筛选叶片氮素含量和冠层氮素密度反演效果较好的参数,建立其与氮营养指数(NNI,nitrogen nutrition index)的经验模型。研究表明,线性内插法红边位置(REPLI)、修正红边单比指数(mSR705)、比值指数(RI-1dB)、简单比值色素指数(SRPI)、红边指数(VOG)等光谱参数与氮素营养指标具有良好的相关性(r0.85),且不受生育期影响,可用来反演评价冠层氮素营养状况;研究对筛选的光谱参数与各氮素指标进行回归建模,并用独立试验数据对所建模型进行验证,结果显示,REPLI在氮营养指数估测方面表现较好(r=0.93),估测模型精度较高(决定系数R2=0.86,均方根误差RMSE=0.08)。NNI在氮素营养状况诊断方面有一定的优势,通过高光谱反演氮营养指数进行氮素营养状态的定性定量诊断有一定的可行性。     

利用日光诱导叶绿素荧光监测水稻叶片叶绿素含量

快速准确地监测作物叶片叶绿素含量对于研究作物光合作用、氮素营养以及胁迫状况至关重要。该研究基于不同品种、不同密度、不同氮素水平的水稻田间小区试验,分别获取冠层和单叶的辐亮度光谱、反射率光谱及生理生态指标等,计算日光诱导叶绿素荧光（Sun-Induced Chlorophyll Fluorescence,SIF）指数和植被指数,进一步基于线性回归和辐射传输模型2种方法来建立叶绿素含量监测模型,评估多个叶绿素监测模型的精度及适用性。结果表明,1）在冠层尺度,冠层761 nm处SIF强度（F761）与冠层叶绿素含量相关性最高,决定系数（Determination coefficient,R2）为0.72,略高于表现最好的红边叶绿素指数（Red edge Chlorophyll index,CIred edge）（R2=0.63）;2）在单叶尺度,归一化下行SIF指数（↓FY NDFI）与单叶叶绿素含量相关性最高,R2为0.77,比表现最好的上行荧光产量双峰比值指数（↑FY687/↑FY741）R2高出0.10,与表现最好的植被指数CIred edge效果相当（R2=0.81）;3）基于SCOPE（Soil Canopy Observation, Photochemistry and Energy fluxes ）模型反演水稻冠层叶绿素含量的验证R2为0.57,均方根误差（Root Mean Squared Error,RMSE）为56.54 μg·cm-2,效果差于PROSAIL模型（模型检验的R2为0.91,RMSE为22.59 μg·cm-2）;4）单叶Fluspect-B模型反演水稻单叶叶绿素含量的验证R2为0.55,均方根误差RMSE为19.45 μg·cm-2,效果差于PROCWT模型反演结果（R2为0.72,RMSE为6.42 μg·cm-2）。综上,SIF指数在监测冠层和单叶叶绿素含量时效果较好,基于SIF的辐射传输模型也可以用来反演水稻冠层和单叶的叶绿素含量。研究结果可为SIF监测作物叶绿素含量提供理论依据,并对未来利用SIF进行植物光合作用研究提供理论支持。     

基于高光谱的夏玉米冠层SPAD值监测研究

开展夏玉米冠层SPAD值监测技术研究,建立叶绿素含量与敏感波段、光谱指数间的定量关系模型,以促进高光谱技术在玉米快速、无损长势监测及水肥精准管理的应用。以小型蒸渗仪夏玉米光谱反射率与植株冠层SPAD值的监测为基础,研究了夏玉米植株冠层光谱信息与SPAD值的响应关系,并优选出监测夏玉米冠层SPAD值的敏感波段与最优光谱指数。结果表明:夏玉米冠层光谱反射率在可见光波段随玉米冠层SPAD值增加而下降,在近红外波段却与之相反;采用原始光谱反射率、一阶微分光谱监测夏玉米冠层SPAD值的最敏感波段分别为700,690nm,与SPAD值的相关性分别为-0.498(p<0.05)和-0.538(p<0.01);而根据多元逐步回归分析获得的最优波段组合由405,408,700nm波段构成;从已报道的73个光谱指数中筛选出与夏玉米冠层SPAD值相关性较高的(SDr-SDb)/(SDr+SDb)、MCARI∥OSAVI、TCARI/OSAVI、SDr/SDb和MTCI等5个光谱指数,光谱指数(SDr-SDb)/(SDr+SDb)与SPAD值的相关性在各生育期均达极显著正相关,且在全生育期相关系数高达0.697(p<0.01),进一步优选出监测夏玉米冠层SPAD值最适宜的光谱指数为(SDr-SDb)/(SDr+SDb);在基于敏感波段、光谱指数和最优波段组合建立的夏玉米SPAD值的回归模型中,按照模拟效果由高到低排序依次为最优波段组合、光谱指数、原始光谱反射率、一阶微分光谱,其决定系数分别为0.777,0.539,0.351,0.282;推荐以(SDr-SDb)/(SDr+SDb)指数构建的二次多项式模型与基于405,408,700nm波段组合建立的线性回归监测模型作为夏玉米植株冠层SPAD值光谱监测适宜模型,且R2大于0.539,RMSE及MAE分别小于6.194和4.702。     

用多角度光谱信息反演冬小麦叶绿素含量垂直分布

由于作物叶片具有一定的叶位空间垂直结构(倒一叶、倒二叶、倒三叶、倒四叶、倒五叶等)，且存在不同叶位叶绿素等生化组分垂直分布的特性，该研究提出利用遥感数据反演作物养分垂直分布，尤其是作物中、下层信息的方法。运用多角度光谱信息，通过不同角度条件下，反映的作物上层、中层、下层信息的差异等通过构建基于不同观测天顶角条件下的冠层叶绿素反演指数的组合值，形成上层叶绿素反演光谱指数、中层叶绿素反演光谱指数和下层叶绿素反演光谱指数来反演作物叶绿素的垂直分布，达到了极显著的水平。表明运用基于多角度光谱信息的光谱指数组合能够较好的反演作物叶绿素含量的垂直分布。对于生产上迫切需要对作物中、下层叶片氮素或叶绿素状况的监测来指导适时和适量施肥，保证获得既定的作物产量和品质目标，提高肥料利用率有重要意义。     

Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation

Leaf chlorophyll content, a good indicator of photosynthesis activity, mutations, stress and nutritional state, is of special significance to precision agriculture. Recent studies have demonstrated the feasibility of retrieval of chlorophyll content from hyperspectral vegetation indices composed by the reflectance of specific bands. In this paper, a set of vegetation indices belonged to three classes (normalized difference vegetation index (NDVI), modified simple ratio (MSR) index and the modified chlorophyll absorption ratio index (MCARI, TCARI) and the integrated forms (MCARI/OSAVI and TCARI/OSAVI)) were tested using the PROSPECT and SAIL models to explore their potentials in chlorophyll content estimation. Different bands combinations were also used to derive the modified vegetation indices. In the sensitivity study, four new formed indices (MSR[705,750], MCARI[705,750], TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750]) were proved to have better linearity with chlorophyll content and resistant to leaf area index (LAI) variations by taking into account the effect of quick saturation at 670 nm with relatively low chlorophyll content. Validation study was also conducted at canopy scale using the ground truth data in the growth duration of winter wheat (chlorophyll content and reflectance data). The results showed that the integrated indices TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750] are most appropriate for chlorophyll estimation with high correlation coefficients R² of 0.8808 and 0.9406, respectively, because more disturbances such as shadow, soil reflectance and nonphotosynthetic materials are taken into account. The high correlation between the vegetation indices obtained in the developmental stages of wheat and Hyperion data (R² of 0.6798 and 0.7618 for TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750], respectively) indicated that these two integrated index can be used in practice to estimate the chlorophylls of different types of corns.     

基于赤池信息量准则的冬小麦叶面积指数高光谱估测

冬小麦叶面积指数(leaf area index,LAI)是描述冠层结构的重要参数之一,对评价其长势和预测产量具有重要意义。该文利用灰色关联分析(grey relational analysis,GRA)对植被指数进行排序,用偏最小二乘法(partial least squares regression,PLS)选择不同的植被指数个数作为自变量进行回归建模,通过赤池信息量准则(Akaike’s information criterion,AIC)选择AIC值最小的模型作为冬小麦LAI最优估算模型,即GRA、PLS和AIC 3种方法整合建立冬小麦LAI最优估算模型。使用2008-2009年在中国北京通州区和顺义区获取的整个生育期冬小麦LAI和配套的光谱数据进行建模,利用2009-2010相关数据进行验证。研究表明:采用GRA评价标准与冬小麦LAI关联度最大的植被指数是VOG1,关联度最小的植被指数是SR;通过AIC建立的以8个植被指数作为自变量的冬小麦LAI模型效果最优,建模集的决定系数R2和标准误SE分别为0.76和0.009,验证集的R2和相对均方根误差RRMSE分别为0.63和0.004,预测模型和验证模型均具有较高的精度和可靠性。结果表明采用GRA-PLS-AIC方法进行冬小麦LAI反演是可行的,为提高冬小麦LAI遥感预测精度提供了一种有效的方法。     

Physiological interpretation of a hyperspectral time series in a citrus orchard

Hyperspectral remote sensing for monitoring horticultural production systems requires the understanding of how plant physiology, canopy structure, management and solar elevation affect the retrieved canopy reflectance during different stages of the phenological cycle. Hence, the objective of this study was to set up and to interpret a hyperspectral time series for a mature and healthy citrus orchard in the Western Cape province of South Africa considering these effects. Based on the remotely sensed data, biophysical parameters at the canopy level were derived and related to known observed physiological and phenological changes at the leaf level and to orchard management. Fractions of mature fruit, flowers, and sunburnt leaves were considered, and indices related to canopy structure chlorophyll content and canopy water status were calculated.Results revealed small cover fractions of mature fruit, flowers and sunburnt leaves of respectively 2.1%, 3.1% and 7.0%, but the high spectral contrast between flowers and leaves allowed a successful classification of flowering intensity. Furthermore, it was shown that canopy level time series of vegetation indices were sensitive to changes in solar elevation and soil reflectance which could be reduced by applying an empirical soil line correction for the most affected indices. Most trends in vegetation indices at the canopy level could be explained by a combination of changes at the leaf level (chlorophyll, carotenoids, dry matter), changes in canopy structure (leaf area index and leaf angle distribution) and changes in cover fractions of vegetative flushes, flowers and sunburnt leaves. The transformed chlorophyll absorption ratio index over the optimised soil adjusted vegetation index (MCARI/OSAVI) was best related to leaf level trends in chlorophyll content. Seasonal changes in the photochemical reflectance index (PRI) were linked to inverse changes in the carotenoid-to-chlorophyll ratio. Canopy structure indices (the modified triangular vegetation index or MTVI2 and the standardized leaf area index determining index or sLAIDI) were sensitive to changes in leaf area index, average leaf angle as well to management interactions (pruning and harvest). Canopy water status was highly impacted during the spring flush due to expanding leaves that concealed trends in the underlying mature leaves. Seasonal trends in soil and weeds reflectance were related to changes in volumetric soil water content and to the earlier and reduced growth period of non-irrigated weeds.     

Estimation of Chlorophyll Contents by Correlations between SPAD‐502 Meter and Chroma Meter in Butterhead Lettuce

Abstract

Chlorophyll content of butterhead lettuce leaves was estimated by correlations between the Minolta SPAD‐502 and Minolta chroma meter CR 300. The SPAD‐502 readings and extractable chlorophyll (Chl a, Chl b, and total Chlorophyll) contents were related. High linear correlations were obtained for chlorophyll a (R²=0.90), chlorophyll b (R²=0.85), and total chlorophyll (R²=0.92). After that, during the growth cycle of butterhead lettuce grown in soilless culture, leaves were selected and SPAD‐502 and chroma meter readings were made. Finally, correlations between SPAD‐502 readings and leaf color (L*, a*, b*, C*, and hue angle) determined by the chroma meter were calculated. Hue angle (H°; R²=0.75) and lightness index (L*; R²=0.68) were the parameters that better relate with SPAD‐502 readings, so these parameters can also be used for rapid and nondestructive estimates of leaf chlorophyll in situ.     

Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John’s Wort Leaves

This research investigates and compares artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John’s wort leaves (Hypericum perforatum L.). Plants were fertilized with 0, 30, 60, 90, and 120 kg ha^?1 nitrogen [34% nitrogen ammonium nitrate (NH₄NO₃)]. Chlorophyll concentration index of each leaf was measured using SPAD meter. Afterwards, rgb (red, green, and blue color) values of all leaf images were determined by image processing. Values obtained were modeled using both multiple regression analysis and artificial neural networks. Using multiple regression analysis R² values were between 0.61 and 0.97. Coefficient of determination values (R²) using artificial neutral network values were found to be 0.99. Artificial neutral network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values.     

小麦品质指标与冠层光谱特征的相关性的初步研究

该文通过研究冬小麦体内生化组分之间的相互关系得出，开花期叶片全氮含量与类胡萝卜素与叶绿素a的比值之间存在极显著的负相关，决定系数为0.7245，通过研究光谱指数与叶片生化组分的关系得出开花期冬小麦叶片的类胡萝卜素与叶绿素a的比值与光谱结构不敏感植被指数(SIPI)之间存在极显著的正相关，决定系数达到0.7207，且研究了冠层生化组分与籽粒品质指标间的关系，得出开花期叶片全氮与籽粒蛋白质、湿面筋、干面筋和沉降值之间存在极显著正相关。小麦不同品质指标间存在相关性，其中蛋白质品质性状间的粗蛋白含量与湿面筋含量、     

基于GF-1卫星数据的冬小麦叶片氮含量遥感估算

以陕西关中地区大田和小区试验下的冬小麦为研究对象,探讨基于国产高分辨率卫星GF-1号多光谱数据的冬小麦叶片氮含量估算方法和空间分布格局。基于GF-1号光谱响应函数对地面实测冬小麦冠层高光谱进行重采样,获取GF-1号卫星可见光-近红外波段的模拟反射率,并构建光谱指数,利用与叶片氮含量在0.01水平下显著相关的8类光谱指数,分别建立叶片氮含量的一元线性、一元二次多项式和指数回归模型。通过光谱指数与叶片氮含量的敏感性分析,以及所建模型的综合对比分析,获取适合冬小麦叶片氮含量估算的最佳模型。结果表明:模拟卫星宽波段光谱反射率和卫星实测光谱反射率间的相关系数高于0.95,具有一致性;改进型的敏感性指数综合考虑了模型的稳定性、敏感性和变量的动态范围,敏感性分析表明比值植被指数对叶片氮含量的变化响应能力最强;综合模拟方程决定系数、模型敏感性分析、精度检验和遥感制图的结果,认为基于比值植被指数建立的叶片氮含量估算模型适用性最强,模拟结果与实际空间分布格局最为接近,为基于GF-1卫星数据的区域性小麦氮素营养监测提供了理论依据和技术支持。     

Estimating the Nitrogen Concentration of Strawberry Plants from Its Spectral Response

Abstract

Estimating the nitrogen (N) status of plants as a function of their spectral response is a promising technique to diagnose and optimize N fertilization. An experiment was conducted in Jiquilpan (Michoacán, México) in which three N levels (0.3, 3, and 20 mM of NO₃ ^? in the irrigation water) were applied to strawberry (Fragaria vesca) in a randomized complete block design with three replicates. The spectral response of strawberry was measured at both the canopy and leaf level using individual wavebands as well as vegetation indices. Individual leaves were separated into three strata (young, mature, and old) and leaf dry matter, leaf area, and N content (% dry matter) were measured in each stratum. Leaf area, biomass, and N content differed significantly between strata. Leaf area, biomass, and N content in all strata were affected by N fertilization. At the canopy level, N content was highly correlated with green reflectance (R550) (r²=0.50) and red reflectance (R680) (r²=0.60) as well as the vegetation indices simple ratio (SR) (r²=0.56), normalized difference vegetation index (NDVI) (r²=0.56), and hyperspectral NDVI (HNDVI) (r²=0.56). For individual leaves, significant differences between strata were found with normalized total pigment to chlorophyll a ratio index (NPCI) and MERIS terrestrial chlorophyll index (MTCI) (p<0.001) as well as R550, photochemical reflectance index (PRI), red edge position (REP), and REP calculated using the MERIS satelite wavelengths (p<0.01). Relationships between spectral indices and N content at the leaf level were found with the youngest leaves only, with NPCI (p<0.01) and MTCI (p<0.05), whereas only R550 responded to N fertilization (p<0.05).