波段位置和宽度对河口湿地4种植被NDVI的影响

研究不同波段位置和宽度对植被NDVI的影响,对于进一步认识NDVI指数具有重要的意义。采用ASD(Analytical Spectral Devices)地物光谱仪测定闽江河口互花米草(Spartina alterniflora)、秋茄(Kandelia candel)、芦苇(Phragmites australis)和短叶茳芏(Cyperus malaccensis)冠层光谱,利用ViewSpecPro和Oragin8.0软件对光谱数据进行分析和处理,探讨不同波段位置和波段宽度对河口湿地4种植被NDVI的影响。结果表明:①当红光波段固定,近红外波段以50 nm宽度移动时,4种湿地植被NDVI随近红外波段中心位置增加而迅速增加,之后趋于平稳,在925~1 050 nm出现一个小的谷值,互花米草和短叶茳芏的谷值要比其他两种植物更为明显;不同波段宽度影响表现为:除红边与970 nm附近区域对NDVI的影响较显著外,其他波段影响不显著;②当近红外波段固定,红光波段以10 nm宽度移动时,4种湿地植被NDVI随红光波段中心位置移动先略有增加或变化不大,然后迅速降低;不同波段宽度影响表现为:在650~700 nm波段宽度越宽,NDVI值越小,600~650 nm范围内波段宽度对NDVI的影响不大;③4种湿地植被红光波段宽度对NDVI的影响要大于近红外波段。     

典型煤系岩石的可见-近红外光谱特征研究

为研究基于可见-近红外光谱技术的煤岩识别方法,从山西、山东4个煤矿收集了页岩、砂岩、灰岩三大类11种典型煤系岩石,测定了其可见-近红外波段(400～2 450nm)的反射光谱,分析了其矿物、元素组成对反射光谱特征的影响,获得了碳质物质含量对煤系页岩反射光谱曲线特征参数的影响规律。研究结果表明:①绝大多数煤系岩石的反射光谱曲线在可见光波段(400～780nm)和短波近红外波段(780～1 100nm)呈现出随波长增加的多重吸收谷。在长波近红外波段(1 100～2 450nm),明显的吸收谷主要集中在1 400,1 900,2 200,2 350nm波长,页岩、灰岩吸收谷的波长相对固定,而不同砂岩吸收谷的波长呈现出多种变化。②除碳质物质含量较高的碳质页岩外,同一煤矿各类煤系岩石与煤的可见-近红外波段反射光谱吸收特征差异明显。③当煤系页岩中碳质物质含量增大时,可见-近红外波段反射光谱曲线的光谱斜率和各明显吸收谷深度均呈先快速减小后趋于平缓的特点。     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光G近红外多光谱波段的基础上,新增加的８个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5％。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

BP-ANN在荒漠草地高光谱分类研究中的应用

利用高分辨率光谱仪在实地测得的光谱数据来识别新疆阜康地区的7种典型荒漠草种,对原始高光谱数据作预处理（微分和平滑）,选取典型荒漠植被的光谱特征（红边、绿峰、红谷、RVI等）作为输入数据,植被类型作为输出数据,构建基于BP神经网络模型的典型荒漠草地分类器,进行了三组基于高光谱特征的草地类型分类实验,结果表明：（1）红边特征较其余吸收特征更能获得精确的分类结果;（2）波段550~790 nm间的窄波段光谱分类间隔中,20 nm优于10 nm的间隔;（3）草地分类器中BP网络模型的输入层、隐藏层神经元个数与BP网络训练时间、精度具有复杂的耦合关系,不可一概而论。     

基于数学形态滤波的植被光谱去噪方法研究

光谱维噪声使地物光谱扭曲或变形,中心波长偏移,影响地物信息提取和地表参量反演的精度。对光谱维噪声进行滤波处理,有利于改善遥感数据定量应用的效果。由于数学形态滤波的原理简单且较易实现,被应用到植被光谱以及有机化合物光谱的研究中。运用数学形态滤波对地面实测小麦光谱去噪,一方面对滤波后的光谱进行噪声和波形相似度的直观分析,另一方面通过植被指数反演小麦理化参量进行定量应用评价。结果表明,与传统Savitzky-Golay滤波相比,在可见-近红外波段范围内,数学形态滤波去噪后的光谱能够保持可见—近红外波段原始光谱的固有特征,叶面积指数和叶绿素的反演精度比去噪前有小幅提升,主要原因是实测光谱在该谱段范围的噪声影响很小;在短波红外波段范围内,数学形态滤波能有效去除短波红外大尺度噪声,提高叶片含水量的反演精度。而传统Savitzky-Golay滤波只能削弱短波红外大尺度噪声。广义形态滤波去噪后植被指数和叶片含水量之间的R2最高可达0.5130(去噪前0.3753),叶片含水量的反演值与实测值之间的R2最高可达0.4221(去噪前0.3097),RMSE为0.0243(去噪前0.0318),优于传统Savitzky-Golay滤波。     

Spot5影像统计分析及最佳组合波段选择

遥感影像特征分析是影像融合和解译的基础,而对遥感数据源各个波段进行定量分析是图像融合的前提。本文以SPOT5为遥感数据信息源,在湖南省资兴市天鹅山林场进行了图像特性和波段组合的实验研究。研究主要采用了典型地物的光谱数据采集分析和遥感数据定量分析相结合的方法,计算出各波段之间的熵、相关系数和协方差。研究结果表明:SPOT5各波段的标准差大小顺序为:波段3>波段4>波段2>波段1,熵值的大小顺序则为:波段4>波段1>波段2>波段3;波段3与波段4的协方差最大,而且两波段又处在红外区域,说明红外波段之间独立性较强;从光谱辐射仪采集的数据来看,水体、草地、裸土地的光谱反射率就有很大的差异性。乔木树种的光谱反射率在可见光区域内非常接近,在红外———近红外区域内具有一定的差异,红外———近红外区域波谱对林业遥感研究具有极其重要的意义;利用联合熵和最佳指数方法确定了最佳波段组合为1(R)4(G)3(B)波段。     

高分五号全谱段光谱成像仪影像数据质量评价研究

高分五号卫星上搭载的我国自主研发的全谱段光谱成像仪是从可见光到长波红外光谱范围的星载多光谱成像仪,具有广泛的应用前景。对卫星影像进行质量评价,既是对遥感卫星是否满足设计指标的验证与检验,也可以为影像的处理与应用提供参考。利用信噪比、清晰度、信息量、辐射不均一性4个指标对高分五号全谱段光谱成像仪进行影像质量评价,并以美国陆地卫星Landsat 8影像为参考进行对比分析。结果表明:高分五号卫星全谱段光谱成像仪短波红外谱段的信噪比(320.44~388.42)略高于可见近红外谱段(208.24~238.03);近-短波红外谱段的清晰度(0.82~0.91)要高于其余谱段,尤其是长波红外谱段(0.01~0.21);可见短波红外谱段的信息量(9.01~9.97)要高于中长波红外谱段(5.71~8.31);所有12个谱段的辐射不均一性均小于2%。与Landsat 8的比较结果表明:在清晰度方面,全谱段光谱成像仪可见近红外谱段优于Landsat 8,其他谱段接近Landsat 8;在信息量方面,可见短波红外谱段与Landsat 8比较接近,但是B11、B12两个分裂窗谱段差距较大,分别相差5.23和5.61;在信噪比方面GF-5 VIMI仍有待进一步改善,又以B1、B2、B63个谱段落后Landsat 8最大,分别相差280.41、226.84和151.92。     

高光谱图像大气校正自动化与快速处理北大核心CSCD

全谱段宽幅高分辨率推扫式光谱成像仪作为我国新一代航空高光谱成像仪已进入应用校飞阶段,文章针对其TB/日数量级高光谱图像快速处理问题,对大气校正过程的参数自动化设置方法和并行加速方法进行研究。在传统基于辐射传输模型的大气校正基础上,分析了可见近红外和短波红外通道是否波段合并、重合波段优选、大气类型选择、是否水汽反演、水汽吸收波段选择等方面对反射率反演精度的影响,实现了参数优化自动设置,并开发了并行化大气校正算法。以吉林榆树和辽宁辽中的数据进行验证,结果表明,反演反射率与参考真值反射率一致性高,同时处理速度比串行处理大大提高,可为高光谱反射率数据产品的业务化生产提供有力工具。     

棉花反射光谱与叶片净光合速率的相关分析

针对传统获取植被生长信息对植被的破坏大、费时费力等问题,提出用高光谱实时动态、非损伤地获取棉花的光合作用参数,监测棉花的长势和预测产量。用美国ASD Fieldspec Pro FR 2500高光谱辐射仪和LI-6400便携式光合测试系统测试棉花新陆早33号不同水分处理4个关键生育时期的冠层高光谱数据和叶片净光合速率(Pn)。多元统计分析表明,近红外759nm(r=0.53**,P0.01,n=40)和红光670nm(r=-0.7**,P0.01,n=40)为光谱反射率与Pn的敏感波段;采用红光670nm与近红外759nm波段的光谱反射率组成归一化植被指数(NDVI)、比值植被指数(RVI)与Pn建立幂函数关系模型,RVI与Pn的相关性高于NDVI与Pn的相关性。研究结果表明,叶片净光合速率反映棉花的光合生产能力和生长状况,利用RVI能更好地预测棉花的叶片净光合速率。     

资源一号02D可见近红外和高光谱影像辐射质量评价

资源一号02D（ZY1-02D）卫星搭载了我国自主研制的可见近红外相机（VNIC）和高光谱相机（AHSI）,是我国首颗民用高光谱业务卫星,具有广泛的应用前景。通过整体辐射精度、信噪比、清晰度以及信息熵4个评价指标,对ZY1-02D VNIC和AHSI数据进行辐射质量评价,并分别采用Sentinel-2 MSI和GF-5 AHSI数据进行对比。结果表明： ZY1-02D VNIC数据在可见光波段具有亮度高、信噪比高等优势;在红边近红外等波段,影像具有灰度范围大、信息量大的特点。ZY-1-02D VNIC数据在影像亮度、灰度范围、清晰度和信息量方面均优于Sentinel-2,二者信噪比近似。ZY-1-02D AHSI数据在395—1 341 nm范围内辐射质量良好;在1 929—2 501 nm范围,存在噪声严重的波段,影像质量较差。与GF-5 AHSI数据对比,ZY-1-02D AHSI数据的影像亮度和信噪比相当,但ZY-1-02D AHSI数据在灰度范围方面优势明显,且短波红外谱段的清晰度和信息量优于GF-5 AHSI数据。     

那曲典型草地植被光谱特征分析

遥感是大尺度生态研究的重要工具之一,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。地面实测数据由于其高空间分辨率和高光谱分辨率,能够准确反映地物光谱信息,可以用来指导卫星遥感解译工作,同时为遥感监测草地退化、草地模型建立等提供数据支持。选取西藏那曲地区的优势植被类型作为研究对象,利用ASD FieldSpec 3便携式光谱仪测定优势种的冠层光谱并进行比较,并取其中一种优势种测量其在不同覆盖度和不同生长期的光谱反射特点。研究结果表明:①不同植被群落冠层光谱具有特殊的光谱曲线,可见光波段光谱反射率依次是紫花针茅、小嵩草和藏北嵩草,近红外波段光谱反射率则依次是小嵩草、藏北嵩草和紫花针茅;红边位置可以识别藏北嵩草,但是不能区分小嵩草和紫花针茅;②不同覆盖度的小嵩草红边、“绿峰”位置不随覆盖度的变化而发生变化;连续统去除后得到吸收深度随覆盖度的增加而变大,吸收峰面积随覆盖度的增加而增加;③小嵩草衰退期内,在可见光波段和红边波段,冠层光谱反射率随着叶绿素含量的减少而下降,出现“红边蓝移,绿峰下降”的现象。     

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350?nm and 2500?nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R ²=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750?nm) and longer wavelengths of the red edge (750–780?nm), yielded higher correlations with biomass (mean R ²=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R ²=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.     

Feasibility of estimating leaf water content using spectral indices from WorldView-3’s near-infrared and shortwave infrared bands

A recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from maize, trees, grasses, and broadleaf herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength 1210 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860–1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.     

Radiation Quality Evaluation of Spark Hyperspectral Satellite Image

The quality evaluation of remote sensing data is a bridge for development of sensor and data application.In this paper,we focused on the hyperspectral data acquired by China's self\|developed SPARK satellite launched in December 2016,and evaluated the radiation quality of SPARK 1A data using four objective indicators,namely radiation accuracy,signal\|to\|noise ratio(SNR),information entropy and sharpness.According to the results of each indicator,variance and information entropy show that the main information of SPARK data is concentrated in 81~152 band(542~985 nm),and the average entropy,signal\|to\|noise ratio and definition of this bands are higher than those of other bands,which are 6.28,47.63 dB and 179.5 respectively.The data quality of this spectral data is better than that of other bands,which is beneficial to the spectral identification and spatial feature extraction of different objects.The average SNR of 1~80 band(411~539 nm) was 38.23 dB,and the entropy was 5.28.Image enhancement can be used before processing for the low gray level and smaller gray range of the image in this bands.Because the 153~160 band(1 000~1 105 nm) was uncalibrated,its average SNR is less than 15 dB,and it has the lowest clarity,the spectrum and spatial information are seriously damaged,it is recommended to remove this bands.     

Measuring rangeland vegetation with high resolution airborne videography in the blue-near infrared spectral region

An airborne video system was used to investigate the visible and near-infrared (NIR) spectral properties of soil and vegetation features across a range of common arid landscape types. The four-camera system was equipped with filters of 25mm bandwidth centred on 450nm ('blue'), 550nm ('green'), 650nm ('red') and 770nm ('NIR'). The aim was to determine what vegetation properties could be detected by combining data from the blue part of the spectrum with the green, red and NIR range, thereby utilizing information contained in the first channel of Landsat Thematic Mapper (TM) (450-520nm). Adding information from the blue end of the spectrum did not assist in discriminating between green vegetation and dry vegetation or green vegetation and bare soil. This separation is best done with a red/NIR ratio. Neither was the blue band an improvement over the PD54 red-green perpendicular distance index in distinguishing between soil and vegetation, irrespective of phenological condition. The blue band can help separate soil from dry vegetation when combined with the sum of brightness values in the red and green bands in a perpendicular distance index. These properties of the spectral dataspace lead to a sequential classification procedure by which airborne videography data can be used to measure vegetation components which are much slower to assess with conventional ground-based methods. Videography has great potential for rapidly verifying or calibrating vegetation cover indices derivedfrom satellite data. Vegetation cover derived from classifying high resolution video data acquired from a heterogeneous floodplain area correlated well with vegetation indices computed from contemporary and co-registered TM data. The most effective indices for measuring vegetation cover with TM data are the PD54 index, brightness in the red band and a perpendicular index based on the sum of the red-green bands and the blue band. However, multiple regression indicates that the addition of a red/NIR ratio as an additional predictor of cover does not greatly improve the performance of these indices.     

冬小麦红边参数变化规律及其营养诊断

研究了冬小麦冠层光谱红边参数随作物生育期的变化规律,并进行了红边参数与各组分间的相关分析,发现可利用用红边位置反演叶片可溶性糖和叶绿素含量,利用红边振幅反演叶片全氮含量,利用红谷反演叶面积指数。建立了基于红边参数的各组分的统计回归模型,可为生产上利用遥感手段大面积、无破坏、及时评价冬小麦生长状态及营养诊断提供重要依据。     

Spectral analysis and classification accuracy of coffee crops using Landsat and a topographic‐environmental model

Coffee is an extremely important cash crop, yet previous work indicates that satellite mapping of coffee has produced low classification accuracy. This research examines spectral band combinations and ancillary data for evaluating the classification accuracy and the nature of spectral confusion between coffee and other cover types in a Costa Rican study area. Supervised classification using Landsat Enhanced Thematic Mapper (ETM+) with only red, near‐infrared, and mid‐infrared bands had significantly lower classification accuracy compared to datasets that included more spectral bands and ancillary data. The highest overall accuracy achieved was 65%, including a coffee environmental stratification model (CESM). Producer's and user's accuracy was highest for shade coffee plantations (91.8 and 61.1%) and sun coffee (86.2 and 68.4%) with band combination ETM+ 34567, NDVI, cos (i), and including the use of the CESM. Post‐classification stratification of the optimal coffee growing zone based on elevation and precipitation data did not show significant improvement in land cover classification accuracy when band combinations included both the thermal band and NDVI. A forward stepwise discriminant analysis indicated that ETM+ 5 (mid‐infrared band) had the highest discriminatory power. The best discriminatory subset for all woody cover types including coffee excluded ETM+ 3 and 7; however, the land cover accuracy assessment indicated that overall accuracy, as well as producer's and user's accuracy of shade and sun coffee, were slightly improved with the inclusion of these bands. Although spectral separation between coffee crops and woodland areas was only moderately successful in the Costa Rica study, the overall accuracy, as well as the sun and shade coffee producer's and user's accuracy, were higher than reported in previous research.     

Seasonal characterization and discrimination of savannah physiognomies in Brazil using hyperspectral metrics from Hyperion/EO-1

The seasonal characterization and discrimination of savannahs in Brazil are still challenging due to the high spatial variability of the vegetation cover and the spectral similarity between some physiognomies. As a preparatory study for future hyperspectral missions that will operate with large swath width and better signal-to-noise ratio than the current orbital sensors, we evaluated six Hyperion images acquired over the Estação Ecológica de Águas Emendadas, a protected area in central Brazil. We studied the seasonal variations in spectral response of the savannah physiognomies and tested their discrimination in the rainy and dry seasons using distinct sets of hyperspectral metrics. Floristic and structural attributes were inventoried in the field. We considered three sets of metrics in the data analysis: the reflectance of 146 Hyperion bands, 22 narrowband vegetation indices (VIs), and 24 absorption band parameters. The VIs were selected to represent vegetation structure, biochemistry, and physiology. The depth, area, width, and asymmetry of the major absorption bands centred at 680 nm (chlorophyll), 980, and 1200 nm (leaf water) and 1700, 2100, and 2300 nm (lignin-cellulose) were calculated on a per-pixel basis using the continuum removal method. Using feature selection and multiple discriminant analysis (MDA), we tested the discriminatory capability of these metrics and of their combined use for vegetation discrimination in the rainy and dry seasons. The results showed that the spectral modifications with seasonality were stronger with the savannah woodland-grassland gradient represented by decreasing tree height, basal area, tree density and biomass and by increasing canopy openness. We observed a reflectance increase in the red, red edge, and shortwave (SWIR) intervals towards the dry season. In the near-infrared, the reflectance differences between the physiognomies were smaller in the dry season than in the rainy season. From the 22 VIs, the visible atmospherically resistant index (VARI), visible green index (VIg), and normalized difference infrared index (NDII) were the most sensitive indices to water stress and vegetation cover, presenting the largest rates of changes between the rainy (March) and dry (August) seasons in shrub and grassland areas. Absorption band parameters associated with the lignin-cellulose spectral features in the SWIR increased towards the dry season with great amounts of non-photosynthetic vegetation (NPV) in the herbaceous stratum. The opposite was observed for the 680 nm chlorophyll absorption band and the 980 and 1200 nm leaf water features. In general, the number of selected metrics necessary for vegetation discrimination was lower in the dry season than in the rainy season. The best MDA-classification accuracy was obtained in the dry season using nine VIs (79.5%). The combination of different hyperspectral metrics increased the classification accuracy to 81.4% in the rainy season and to 84.2% in the dry season. This combination added a gain higher than 10% for the classification of shrub savannah, open woodland savannah and wooded savannah.     

Does single broadband or multispectral thermal data add information for classification of visible,near‐ and shortwave infrared imagery of urban areas?

The potential value of combining broadband and multispectral thermal infrared (TIR) data with multispectral and hyperspectral visible, near‐infrared (VNIR) and shortwave infrared (SWIR) data was investigated within the context of urban land‐cover classification. Using a case study of airborne Digital Airborne Imaging Spectrometer (DAIS) imagery of Strasbourg, France, the relative contribution of TIR wavelengths to classification accuracy was investigated for hyperspectral and simulated multispectral IKONOS, SPOT and Landsat Thematic Mapper (TM) bands. A support vector machines (SVM) classifier was used because this method was found to be very effective at handling the complex distributions of the heterogeneous land cover classes. The overall classification accuracy varied greatly with different band combinations. The inclusion of a single broad thermal band increased classification accuracy by as much as 20% for simulated IKONOS bands, but only 4% for hyperspectral VNIR and SWIR data. Adding multispectral TIR data raised the average accuracy approximately a further 10% for each band combination studied. Thermal wavelengths were found to be particularly useful for reducing the confusion between road and roof surfaces.     

Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species

The main focus of recent studies relating vegetation leaf chemistry with remotely sensed data is the prediction of chlorophyll and nitrogen content using indices based on a combination of bands from the red and infrared wavelengths. The use of high spectral resolution data offers the opportunity to select the optimal wavebands for predicting plant chemical properties. In order to test the optimal band combinations for predicting nitrogen content, normalized ratio indices were calculated for all wavebands between 350 and 2200 nm for five different species. The correlation between these indices and the nitrogen content of the samples was calculated and compared between species. The results show a strong correlation between individual normalized ratio indices and the nitrogen content for different species. The spectral regions that are most effective for predicting nitrogen content are, for each individual species, different from the normalized difference vegetation index (NDVI) spectral region. By combining the areas of maximum correlation it was possible to determine the optimal spectral regions for predicting leaf nitrogen content across species. In a cross‐species situation, normalized ratio indices using the combination of reflectance at 1770 nm and at 693 nm may give the best relation to nitrogen content for individual species.