Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation

Extensive studies have focused on assessing leaf chlorophyll content through spectral indices; however, the accuracy is weakened by limited wavebands and coarse resolution. With hundreds of wavebands, hyperspectral data can substantially capture the essential absorption features of leaf chlorophyll; however, few such studies have been conducted on same species in various degraded vegetations. In this investigation, complete combinations of either original reflectance or first‐order derivative spectra we conducted a complete combination on either original reflectance or its first‐order derivative value from 350 to 1000 nm to quantify leaf total chlorophyll (Chll), chlorophyll‐a (Chla), and chlorophyll‐b (Chlb) contents. This was performed using three hyperspectral datasets collected in situ from lightly, moderately, and severely degraded vegetations in temperate Helin County, China. Suitable combinations were selected by comparing the numbers of significant correlation coefficients with leaf Chll, Chla, and Chlb contents. The combinations of reflectance difference (D_ij), normalized differences (ND), first‐order derivative (FD), and first‐order derivative difference (FD(D)) were found to be the most effective. These sensitive band‐based combinations were further optimized by means of a stepwise linear regression analysis and were compared with 43 empirical spectral indices, frequently used in the literature. These sensitive band‐based combinations on hyperspectral data proved to be the most effective indices for quantifying leaf chlorophyll content (R² > 0.7, p < 0.01), demonstrating great potential for the use of hyperspectral data in monitoring degraded vegetation at a fine scale.     

Hyperspectral discrimination of coral reef benthic communities in the western Caribbean

Determining a subset of wavelengths that best discriminates reef benthic habitats and their associated communities is essential for the development of remote sensing techniques to monitor them. This study measured spectral reflectance from 17 species of western Caribbean reef biota including coral, algae, seagrasses, and sediments, as well as healthy and diseased coral. It sought to extend the spectral library of reef-associated species found in the literature and to test the spectral discrimination of a hierarchy of habitats, community groups, and species. We compared results from hyperspectral reflectance and derivative datasets to those simulated for the three visible multispectral wavebands of the IKONOS sensor. The best discriminating subset of wavelengths was identified by multivariate stepwise selection procedure (discriminant function analysis). Best discrimination at all levels was obtained using the derivative dataset based on 6–15 non-contiguous wavebands depending on the level of the classification, followed by the hyperspectral reflectance dataset which was based on as few as 2–4 non-contiguous wavebands. IKONOS wavebands performed worst. The best discriminating subset of wavelengths in the three classification resolutions, and particularly those of the medium resolution, was in agreement with those identified by Hochberg and Atkinson (2003) and Hochberg et al. (2003) for reef communities worldwide. At all levels of classification, reflectance wavebands selected by the analysis were similar to those reported in recent studies carried out elsewhere, confirming their applicability in different biogeographical regions. However the greater accuracies achieved using the derivative datasets suggests that hyperspectral data is required for the most accurate classification of reef biotic systems.     

冠层水平互花米草叶片光合色素含量的高光谱遥感估算模型

以闽江河口鳝鱼滩湿地互花米草(Spartina alterniflora)的实测冠层高光谱反射率和叶片光合色素含量(LPPC)为数据源,在分析LPPC与原始光谱反射率、一阶导数光谱反射率、22种已报道光谱指数和14种新构建的植被指数相关性的基础上,利用直线回归、指数回归、对数回归以及乘幂回归方法,系统地比较了36种植被指数在估算互花米草LPPC中的表现。研究表明:(1)一阶导数光谱反射率组合的植被指数用于估算互花米草的LPPC优于原始光谱反射率;(2)红边区域一阶导数光谱是估测互花米草LPPC的最佳波段;(3)对于单一色素含量的估算,叶绿素a(Chla)的最佳估算指数为FDNDVI[723,703];叶绿素b(Chlb)的最佳估算指数为FDRVI[723,525];类胡萝卜素(Cars)的最佳估算指数为FDNDVI[723,703];(4)对于使用统一参量同时估算Chla、Chlb、Cars,由FDRVI[723,703]建立的对数估算模型效果最佳。研究成果可为湿地植物生化参量反演提供参考,也可为闽江河口湿地入侵种互花米草的动态监测和生态评估管理提供有力的科学依据。     

Application of Reflectance Spectroscopy for Analysis of Higher Plant Pigments

Nondestructive techniques developed by the authors for assessment of chlorophylls, carotenoids, and anthocyanins in higher plant leaves and fruits are presented. The spectral features of leaf reflectance in the visible and near infrared regions are briefly considered. For pigment analysis only reflectance values at several specific wavelengths are required. The chlorophyll (Chl) content over a wide range of its changes can be assessed during leaf ontogeny using reflectance near 700 nm and, in the absence of anthocyanins, at 550 nm. The approaches used for elimination of Chl interference in the analysis of carotenoids (reflectance at 520 nm) and anthocyanins (at 550 nm) are described. The suitability of reflectance spectroscopy for estimates of carotenoid/chlorophyll ratios during leaf senescence and fruit ripening is demonstrated. The algorithms developed for pigment analysis are presented, and the conditions of their applicability are considered. Further perspectives for the application of reflectance spectroscopy including remote sensing for estimation of plant pigment content and physiological states are discussed.     

基于小波分析的大豆叶绿素a含量高光谱反演模型

 2003和2004年分别在长春市良种场和中国科学院海伦黑土生态实验站实测了大田耕作与水肥耦合作用下大豆（Glycine max）冠层高光谱反射率 与叶绿素a含量数据,对光谱反射率、微分光谱与叶绿素a含量进行了相关分析;采用归一化植被指数(Normalized diffe rence vegetation index, NDVI)、土壤调和植被指数(Soil-adjusted vegetation index, SAVI)、再归一植被指数(Renormalized difference vegetation index, RDVI)、第二修正比值植被指数(Modified second ratio index, MSRI)等建立了大豆叶绿素a反演模型;应用小波分析对采集的光谱反 射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆叶绿素a进行了估算。研究结果表明,大 豆叶绿素a 与可见光光谱反射率相关性较好,并在红光波段取得最大值(R²>0.70),但在红边处,微分光谱与大豆叶绿素a的相关性较反射率好 得多,在其它波段则相反;由NDVI、SAVI、RDVI、MSRI等植被指数建立的估算模型可以提高大豆叶绿素a的估算精度（R²>0.75）;小波能量系 数回归模型可以进一步提高大豆叶绿素a含量的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆叶绿素a回归决定系数R²高达 0.78;多变量回归分析结果表明,大豆叶绿素a实测值与预测值的线性回归决定系数R²均高达0.85。以上结果表明, 小波分析可以对高光谱进 行特征变量提取,并可在一定程度上提高大豆生理参数反演精度。     

Apple flavonols during fruit adaptation to solar radiation: spectral features and technique for non-destructive assessment

Spectral properties of flavonols of three varieties (Golden Delicious, Antonovka, and Renet Simirenko) of anthocyanin-free apple fruit were investigated with reflectance spectroscopy. The results of spectral and biochemical analyses suggested that fruit reflectance in a broad spectral range 365-430 nm is strongly dependent on and, in sunlit fruit surfaces, governed by flavonols. The build up of peel flavonols (mainly rutin and other quercetin glycosides) resulted in a dramatic decrease of fruit reflectance in this range, flattening of the spectrum, and extending the region with low reflectance (4-5%) to ca. 410 nm. The spectral features observed suggest that flavonols contribute significantly to screening of excessive radiation, not only UV-A, but in the short-wave bands of chlorophyll and carotenoid absorption in the visible part of the spectrum as well. To retrieve quantitatively flavonol content from reflectance spectra, we tested the applicability of an inversion technique developed for non-destructive leaf pigment assessment. The model for flavonol content assessment was suggested in the form (R(-1)410 - R(-1)460)R800, where Rlambda is reflectance at wavelength lambda. The model was linearly related to flavonol content between 8 and 220nmol/cm2 with the coefficient of determination r2=0.92 and root mean square error of flavonol estimation of 20 nmol/ cm2 regardless of cultivar, chlorophyll, and carotenoid content.     

基于冠层反射光谱的棉花干物质积累量估测

通过分析不同施氮水平下棉花地上部干物质积累量与冠层光谱反射率及其衍生的比值植被指数(RVI)、归一化植被指数(NDVI)及差值植被指数(DVI)之间的关系,确立了棉花地上部干物质积累量的敏感波段及预测模型.结果表明:两个可见光波段(560和710 nm)和5个近红外波段(810、870、950、1 100和1 220 nm)组成的植被指数与棉花地上部干物质积累量的相关性较好,其中RVI(1 100, 560)的相关性最好.通过逐步回归分析确立的棉花地上部干物质积累量的预测模型为:地上部干物质积累量(g·m-2)=66.274×RVI(1 100, 560)-148.84.说明通过遥感手段估测棉花地上部干物质积累量是可行的.     

桉树叶片光合色素含量高光谱估算模型

色素在植物的生理生态过程中非常重要,利用高光谱数据,揭示光谱反射率上特征波段与光合色素含量间的关系将有助于理解光合色素光谱反射特征的规律,同时为利用高光谱遥感技术快速无损监测植物叶片光合色素提供了技术支持.利用野外采集的桉树叶片样本,在实验室内测定了叶片的高光谱反射率及对应的叶绿素、类胡萝卜素含量.利用光谱分析技术和统计学方法对光谱数据进行处理分析,提取了光谱特征参量,并建立叶绿素、类胡萝卜素含量与光谱特征参量间的估算模型.通过精度检验,研究结果表明以(SDr-SDb)/(SDr+SDb)为变量建立的指数模型估算效果最佳.     

Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network

Estimation of rice disease using spectral reflectance is important to non-destructive, rapid, and accurate monitoring of rice health. In this study, the rice reflectance data and disease index (DI) were determined experimentally and analyzed by single wave correlation, regression model and neural network model. The result showed that raw spectral reflectance and first derivative reflectance (FDR) difference of the rice necks under various disease severities is clear and obvious in the different spectral regions. There was also significantly negative or positive correlation between DI and raw spectral reflectance, FDR. The regression model was built with raw and first derivative spectral reflectance, which was correlated highly with the DI. However, due to rather complicated non-linear relations between spectral reflectance data and DI, the results of DI retrieved from the regression model was not so ideal. For this reason, an artificial neural network model (BP model) was constructed and applied in the retrieval of DI. For its superior ability for solving the non-linear problem, the BP model provided better accuracy in retrieval of DI compared with the results from the statistic model. Therefore, it was implied that the rice neck blasts could be predicted by remote sensing technology.     

基于光谱指数的植物叶片叶绿素含量的估算模型

叶绿素是光合作用能力和植被发育阶段的指示器,是监测湿地植被生长健康状况的重要指标之一;高光谱遥感技术可以为植物叶绿素含量的定量化诊断提供简便有效、非破坏性的数据采集和处理方法。为保证被探测叶片面积相同,消除背景反射、叶片表面弯曲造成的光谱波动及叶片内部变异造成的影响,研究采用Field Spec 3光谱仪加载手持叶夹式叶片光谱探测器,测定野鸭湖湿地典型植物的叶片高光谱反射率数据,同时通过分光光度计室内测定相应叶片的叶绿素含量。采用相关性及单变量线性拟合分析技术,建立二者的关系模型,包括叶绿素含量与"三边"参数的相关模型以及比值光谱指数(SR)模型和归一化差值光谱指数(ND)模型,并采用交叉检验中的3K-CV方法对估算模型进行模型精度检验。结果表明:植物叶片叶绿素含量与"三边"参数大多都呈极显著相关,相关系数最大达到0.867;计算光谱反射率组成的比值(SR)和归一化(ND)光谱指数与叶绿素含量的决定系数,总体相关性比较高,较好的波段组合均为550—700nm与700—1400nm以及550—700nm与1600—1900nm,与叶绿素含量相关性最好的指数分别是SR(565nm,740nm)和ND(565nm,735nm)。并通过选取相关性最佳的光谱特征参数,分别基于"三边"参数和ND模型指数构建了植物叶片叶绿素含量的估算模型。其中,基于红边位置(WP_r)光谱特征参数和ND(565nm,735nm)光谱指数建立的叶绿素含量估算模型,取得了较好的测试效果,检验拟合方程的决定系数(R2)都达到0.8以上,估算模型分别为y=0.113x-78.74,y=5.5762x+4.4828。通过3K-CV方法进行测试和检验,植物叶绿素含量估算模型均取得了较为理想的预测精度,预测精度的分别为93.9%及90.7%。高光谱遥感技术对植被进行微弱光谱差异的定量分析,在植被遥感研究与应用中表现出强大优势,为植物叶绿素含量诊断中的实际应用提供了重要的理论依据和技术支持。     

Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves

Leaf chlorophyll content provides valuable information about physiological status of plants. Reflectance measurement makes it possible to quickly and non-destructively assess, in situ, the chlorophyll content in leaves. Our objective was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation in leaves with a wide range of pigment content and composition using reflectance in a few broad spectral bands. Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated. It was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda from 520 to 550 nm and 695 to 705 nm related closely to the total chlorophyll content in leaves of all species. Subtraction of near infra-red reciprocal reflectance, (RNIR)-1, from (R lambda)-1 made index [(R lambda)(-1)-(RNIR)-1] linearly proportional to the total chlorophyll content in spectral ranges lambda from 525 to 555 nm and from 695 to 725 nm with coefficient of determination r2 > 0.94. To adjust for differences in leaf structure, the product of the latter index and NIR reflectance [(R lambda)(-1)-(RNIR)-1]*(RNIR) was used; this further increased the accuracy of the chlorophyll estimation in the range lambda from 520 to 585 nm and from 695 to 740 nm. Two independent data sets were used to validate the developed algorithms. The root mean square error of the chlorophyll prediction did not exceed 50 mumol/m2 in leaves with total chlorophyll ranged from 1 to 830 mumol/m2.     

Hyperspectral spectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera: Aphididae)

Although spectral remote sensing techniques have been used to study many ecological variables and biotic and abiotic stresses to agricultural crops over decades, the potential use of these techniques for greenbug, Schizaphis graminum (Rondani) (Hemiptera: Aphididae) infestations and damage to wheat, Triticum aestivum L., under field conditions is unknown. Hence, this research was conducted to investigate: 1) the applicability and feasibility of using a portable narrow-banded (hyperspectral) remote sensing instrument to identify and discern differences in spectral reflection patterns (spectral signatures) of winter wheat canopies with and without greenbug damage; and 2) the relationship between miscellaneous spectral vegetation indices and greenbug density in wheat canopies growing in two fields and under greenhouse conditions. Both greenbug and reflectance data were collected from 0.25-, 0.37-, and 1-m2 plots in one of the fields, greenhouse, and the other field, respectively. Regardless of the growth conditions, greenbug-damaged wheat canopies had higher reflectance in the visible range and less in the near infrared regions of the spectrum when compared with undamaged canopies. In addition to percentage of reflectance comparison, a large number of spectral vegetation indices drawn from the literature were calculated and correlated with greenbug density. Linear regression analyses revealed high relationships (R2 ranged from 0.62 to 0.85) between greenbug density and spectral vegetation indices. These results indicate that hyperspectral remotely sensed data with an appropriate pixel size have the potential to portray greenbug density and discriminate its damage to wheat with repeated accuracy and precision.     

亚热带典型常绿阔叶树种叶片叶绿素含量与其高光谱特征间的关系

叶绿素在植物的生理生态过程中非常重要.利用高光谱数据,揭示光谱反射率上特征光谱与叶绿素含量间的关系将有助于理解叶绿素光谱反射特征的规律.选取了6种亚热带常绿阔叶树种,抽取一定数量的叶片样品,分别测量了叶片的光谱反射率及其叶绿素含量,并建立了光谱反射率曲线上的红边位置附近的光谱反射率和叶绿素总量间的回归模型.结果显示:对所研究的树种而言,红边位置处一窄波长范围内的平均光谱反射率与叶绿素总量间的相关性普遍较高,回归效果显著.从高光谱遥感的角度结合植物的生理生态特点分析了其机理.建立的有关红边位置处的光谱反射率与叶绿素总量间的回归模型普遍具有较高的拟合度,其应用前景较好.     

基于光谱变化特征的小麦叶片不同功能期监测研究

为了探讨小麦叶片不同功能期光谱变化特征与叶绿素含量之间的关系,以4个小麦杂交组合基因型为试验材料,对不同生长发育期的旗叶进行光谱和叶绿素测量,并采用线性外推法计算其红边位置.结果表明：在近红外区（750 nm～850 nm）和可见光区（500 nm～600 nm）处,从抽穗期到扬花期小麦旗叶光谱反射率呈现上升趋势,到灌浆期开始下降.其叶绿素含量也是先上升,进入灌浆期后开始下降.建立旗叶4个不同功能期4月25日、5月1日、5月6日和5月11日叶绿素含量与其红边位置线性回归模型,其R2分别为0.5893、0.8842、0.9379和0.7258,均达极显著水平,表明此模型可用于叶片叶绿素含量无损监测.     

基于高光谱的苹果花磷素含量监测模型

在室内条件下,利用ASD FieldSpec 3地物光谱仪,测定了盛花期苹果花的高光谱反射率,在分析苹果花原始光谱反射率及其一阶导数特征的基础上,分析了高光谱反射率与磷素含量间的相关关系,确定其敏感波段,构建了特征光谱参数,并建立了苹果花磷素含量的监测模型.结果表明：苹果花磷素含量与350～370 nm、670～1385 nm、1620～1760 nm波段的原始光谱反射率以及波段500～520 nm光谱反射率的一阶导数呈极显著正相关,与波段670～730 nm一阶导数呈极显著负相关;光谱参数DVI(936,676)、DVI(977,676)、NDVI(936,676)、NDVI(977,676)与苹果花磷素含量的相关性较好,其相关系数均达到0.77以上,由此建立了以4种光谱参数为自变量的磷素含量监测模型,其中,以NDVI(936,676)为自变量构建的监测模型具有最大的决定系数（R²=0.9385）、最小的均方根误差（RMSE=0.6883）和最小的相对误差（RE=7.6%）,预测精度达到92.4%,为最佳监测模型.     

Effects of iron on growth and reflectance spectrum of the bloom‐forming cyanobacterium Microcystis viridis

Iron is an important factor in algal blooms because it is involved in cyanobacterial pigment biosynthesis and therefore has the ability to influence the pigment status of algal cells. This role in pigment biosynthesis offers the opportunity for rapid monitoring of iron availability to cyanobacteria through spectral reflectance characterization. In the present study, the freshwater cyanobacterium Microcystis viridis was cultured with different levels of iron. Cell density, cellular content of iron and photosynthetic pigments, and spectral reflectivity of M. viridis were determined daily during the course of the culture experiment. The results showed that at the lowest iron concentration (0.01 μM) the growth of M. viridis was seriously limited, and the maximal cell density was only approximately 6.4% of the density observed with an iron concentration of 18 μM. Iron availability dramatically affected chlorophyll a, carotenoid and phycocyanin content, with the greatest impact on chlorophyll a. The iron‐induced changes in content and ratios of pigments were detectable through spectral reflectance. Eleven spectral indices previously developed for the estimation of concentrations and/or ratios of pigments and a newly proposed chlorophyll a/phycocyanin index were found to be suitable for generating sensitive regression models between cellular iron content and spectral parameters. The comprehensive application of key sensitive spectral indices and regression equations should help to support monitoring and diagnosis of iron availability to cyanobacteria via remote sensing.     

植物反射光谱对水分生理变化响应的研究进展

实时、无损伤地探测植物的水分及生理变化是高光谱遥感的深层次应用。由水分胁迫引发的植物一系列反射光谱响应体现了碳-氮-水耦合作用的结果。以往的研究大多集中于单一因素的响应, 而忽略了多因素交互作用。该文综述和分析了植物水分状况变化引起的直接和间接光谱响应机制, 包括植物水分含量、色素、养分状况、光合作用和叶绿素荧光指标的光谱响应及其内在的关联, 探讨了反射光谱在探测植物水分生理活动应用中的主要方法与最新技术, 并指出碳-氮-水多指标、多时空尺度的综合分析对于估测植被生产力及其对气候变化的响应具有重要意义。     

A review of plant spectral reflectance response to water physiological changes

Spectral reflectance is a new, real time and non-destructive hyperspectral remote sensing application to monitor plant water status and physiological changes. The spectral reflectance responses induced by water stress reflect the interaction and coupling of carbon, nitrogen and water cycles. A majority of previous studies focused on a specific structural or physiological effect on spectral reflectance with little attention on their interactions. This paper reviewed and synthesized the direct and indirect spectral responses caused by changes in plant water content, pigments, nutrient status, photosynthesis and chlorophyll fluorescence indices and their internal association. This paper also discussed the common approaches and the new techniques in applying spectral reflectance for detecting water status and physiological activities in plants. This paper concluded that analysis of the spectral reflectance at multiple temporal or spatial scales might have a potential application in projecting vegetation productivities, particularly in the context of climate change.     

Bio-optical modeling of photosynthetic pigments in corals

The spectral reflectance of coral is inherently related to the amounts of photosynthetic pigments present in the zooxanthellae. There are no studies, however, showing that the suite of major photosynthetic pigments can be predicted from optical reflectance spectra. In this study, we measured cm-scale in vivo and in situ spectral reflectance for several colonies of the massive corals Porites lobata and Porites lutea, two colonies of the branching coral Porites compressa, and one colony of the encrusting coral Montipora flabellata in Kaneohe Bay, Oahu, Hawaii. For each reflectance spectrum, we collected a tissue sample and utilized high-performance liquid chromatography to quantify six major photosynthetic pigments, located in the zooxanthellae. We used multivariate multiple regression analysis with cross-validation to build and test an empirical linear model for predicting pigment concentrations from optical reflectance spectra. The model accurately predicted concentrations of chlorophyll a, chlorophyll c ₂, peridinin, diadinoxanthin, diatoxanthin and β-carotene, with correlation coefficients of 0.997, 0.941, 0.995, 0.996, 0.980 and 0.984, respectively. The relationship between predicted and actual concentrations was 1:1 for each pigment, except chlorophyll c ₂. This simple empirical model demonstrates the potential for routine, rapid, non-invasive monitoring of coral-zooxanthellae status, and ultimately for remote sensing of reef biogeochemical processes.     

植物叶绿素含量的遥感模型及用陆地卫星数据所作的二维分布图

为了确定全球和区域尺度的植物叶绿素含量的多少和分布,遥感方法是一种很好的途径。有些遥感科学家直接利用归一化植被指数(NDVI)等植被指数推算叶绿素含量。实践表明,这种做法的缺点有:1.植被指数NDVI中的近红外波段反射率与叶绿素含量基本无关,这样NDVI中的主信息并没有表达叶绿素含量,其信噪比显著降低;2.存在明显的信息不确定性,同一个植被指数NDVI可能有两种状态:较高叶面积指数伴有较低叶绿素含量或者是较低叶面积指数伴有较高叶绿素含量。通过实践,作者推导出可用LANDSAT和NOAA影像图灰度值推算植被叶绿素含量遥感模型。并在中国科学院禹城遥感实验场实际应用中得到验证:用其模型推算植被叶绿素含量的精度比NDVI有较明显提高,不确定性有明显的降低。