落叶松早落病危害的光谱特征及与叶绿素浓度的关系研究

森林病虫害是影响森林健康的主要因素之一，全面、准确、迅速地对森林病虫害进行监测管理必须依靠先进的技术手段。利用光谱特征研究落叶松受害情况及叶绿素浓度变化情况，将落叶松的受害程度分为4个等级，选取了11组不同受害程度叶片的叶绿素、类胡萝卜素的浓度及相应的光谱反射率数据进行分析。结果表明，不同健康程度的光谱反射率有4个明显差别之处，分别在绿峰、吸收谷、“红边”位置及水分吸收带；随着受害程度的加重，“红边”位置“蓝移”，叶绿素反射峰“红移”明显。不同健康程度的落叶松叶片的“红边”拐点波长位置、吸收谷与其叶绿素浓度之间具有较强的相关性，为高光谱数据研究森林病虫害提供了方法和途径。     

水稻叶片叶绿素含量的光谱反演研究

通过研究不同叶绿素含量的水稻叶片的光谱特性，发现叶绿素含量与光谱特性之间具有明显的相关性，并建立了水稻叶片叶绿素含量的光谱反演模式。研究表明，水稻叶片光谱反射率及其一阶导数的峰值参数与叶绿素含量之间具有很强的相关性，复相关系数均达到0．4以上，经多元线性回归分析显示回归显著，线性相关密切，回归方程的复相关系数为0.63，可作为水稻叶片叶绿素含量反演的方法，并为大面积水稻冠层叶绿素含量遥感监测提供理论依据。     

松毛虫危害的光谱特征与虫害早期探测模式

根据生态学特征，本文将松毛虫危害的针叶样品分为５个等级，对其反射光谱和叶绿素含量进行了测量分析。结果表明，随受害程度加重，叶绿素含量降低，５５０ｎｍ处的反射率、近红外肩反射率与红光最低反射率之差及红界一阶导数谱最大值均呈下降趋势，６３０ｎｍ处反射率呈上升趋势，红界光谱蓝移、叶绿素反射峰红移明显。应用逐步判别分析法对比分析证实了细分光谱特征参量比绿、红、近红外三波段反射率参量有更强的判别分类能力，这     

烃类物质的反射光谱特征研究及其油气勘探意义

通过新疆塔里木盆地西北缘柯坪隆起东部奥陶系石灰岩中晶洞油苗的反射光谱的浏试及其反射光谱特征的分析表明，烃类物质在1720nm附近具有吸收峰，在2310nm附近具有明显的双重吸收峰，Z150nm附近的吸收峰也可能由烃类物质所致;碳酸盐岩在2340nm附近具有CO^2-₃基团引起的强吸收谱带，随着其表面烃类物质的增加，其光谱反射率明显降低。     

不同遮荫条件下苦槠和木荷的叶绿素含量及光谱特性

研究了遮荫处理对苦槠、木荷的叶绿素(SPAD相对值)、高光谱特性的影响。结果表明:遮荫处理提高了这两种亚热带常绿阔叶植物的叶绿素含量,80%遮荫处理下的叶绿素含量最大;随遮荫度的提高红光位置则会相应出现"红移"现象;但过高的遮荫度会抑制其生长,表现出"蓝移"现象。对所研究的树种而言,红边位置处—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,相关系数大于0.7。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,应用前景广泛。     

太湖水体叶绿素浓度与反射光谱特征关系的研究

根据2004年10月在太湖实测的水体反射光谱及实验室分析得到的叶绿素浓度数据,对太湖水体反射光谱特征与叶绿素浓度之间的关系进行探讨与分析。研究结果表明:水体叶绿素浓度与各波长处的反射光谱相关性不大,但是反射光谱经过比值法和微分处理后,两者具有很好的相关性,而且叶绿素浓度与反射光谱700nm附近波峰几何形态特征(波峰位置、面积、净高度)相关性很好,建立太湖水体叶绿素浓度估算模型。     

冬小麦品质的影响因素及高光谱遥感监测方法

研究了小麦品质的分类及其构成因素与环境条件之间的关系，各品质因素之间的关系。运用相同栽培条件下不同品种品质指标间的关系和变化规律，研究了品种因素对小麦品质的影响程度以及品种因素与品质指标之间的相关性，得出相同环境条件下籽粒的蛋白质含量与湿面筋含量、沉降值、吸水率、形成时间和稳定时间之间存在极显著的相关性。并利用不同品种、不同肥水条件下的作物关键生育时期的生化参量与光谱指数进行分析，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，冬小麦体内的全氮含量与类胡萝卜紊与叶绿素口的比值之间存在极显著的负相关，决定系数为0．7245，并通过分析开花期冠层生化组分与籽粒品质指标间的相关性，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的正相关，表面运用开花期光谱指数来反演叶片全氮含量，进而用来预测预报籽粒品质是切实可行的。     

基于反射光谱数据的茶树叶片SPAD值估算模型研究

实验中将茶树的叶片分为3个部位进行研究,每个部位各采集50个叶片,30组数据作为预测样本,20组数据作为试验模拟样本。设计绿峰位置、绿峰最大反射率、红谷位置、红谷最小反射率、红边位置、红边对应的最大一阶导数反射率、绿峰对应的最大反射率和红谷对应的最小反射率的比值指数以及它们的归一化指数等8个反射光谱参数。首先对茶树叶片的反射光谱参数和茶树叶片的SPAD值进行相关分析;其次以8个反射光谱参数作为自变量,茶树叶片的SPAD值为因变量,进行逐步回归分析,确定茶树不同部位叶片的回归方程.茶树A部位嫩叶片的SPAD值预测模型以λr、Rg/Ro为自变量,其模拟的调整决定系数为0.461;茶树B部位的成熟叶子的SPAD值预测模型以Rg、Rg/Ro、Rg-Ro/Rg+Ro为自变量,其模拟的调整决定系数为0.882;茶树C部位的老叶子的SPAD值预测模型以λr、Dr为自变量,其模拟的调整决定系数为0.407。结果表明,利用反射高光谱参数预测茶树不同部位叶片的SPAD模型是成功的。     

松花湖水体叶绿素a含量与反射光谱特征关系初探

根据2008年7月在松花湖实测的水体反射光谱及实验室分析得到的叶绿素浓度数据,对松花湖水体反射光谱特征与叶绿素浓度之间的关系进行探讨与分析。研究结果表明:水体叶绿素浓度与各波长点处反射率相关性均较好,并选择700 nm处反射率建立单波段模型。而700 nm和677 nm波长处反射率比值、685 nm处光谱一阶微分、700 nm波长处波峰几何特征具有较好的相关性,给出了松花湖水体叶绿素浓度估算模型,为松花湖水体叶绿素浓度反演监测提供了一定的理论基础与参考。     

水稻叶绿素浓度与光谱反射率关系研究

结合田间小区实验,获取了水稻生育期9组叶片叶绿素浓度和相应的光谱反射率数据,进行了相关分析研究,结果表明:水稻叶片叶绿素浓度与其光谱反射率之间具有相关性,在450～686nm和750～770 nm光谱区内相关性较好,且在686 nm处两者达到最高的相关性;水稻叶片的“红边”拐点波长位置与其叶绿素浓度之间具有很强的相关性,复相关系数为0.88,并由此进一步讨论了高光谱遥感定量探测水稻叶绿素浓度的可行性。
     

Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations

This study focuses on the potential of satellite hyperspectral imagery to monitor vegetation biophysical and biochemical characteristics through narrow-band indices and different viewing angles. Hyperspectral images of the CHRIS/PROBA sensor in imaging mode 1 (5 observation angles, 62 bands, 410-1005 nm) were acquired throughout a two-year period for a Mediterranean ecosystem fully covered by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident ecophysiological field measurements were conducted. Leaf area index (LAI), leaf biochemical content (chlorophyll a, chlorophyll b, carotenoids) and leaf water potential were measured. The hyperspectral images were corrected for coherent noises, cloud and atmosphere, in order to produce ground reflectance images. The reflectance spectrum of each image was used to calculate a variety of vegetation indices (VIs) that are already published in relevant literature. Additionally, all combinations of the 62 bands were used in order to calculate Normalized Difference Spectral Indices (NDSI_(x,y)) and Simple Subtraction Indices (SSI_(x,y)). The above indices along with raw reflectance and reflectance derivatives were examined for linear relationship with the ground-measured variables and the strongest relationships were determined. It is concluded that higher observation angles are better for the extraction of biochemical indices. The first derivative of the reflectance spectra proved to be very useful in the prediction of all measured variables. In many cases, complex and improved spectral indices that are proposed in the literature do not seem to be more accurate than simple NDSIs such as NDVI. Even traditional broadband NDVI is proved to be adequate in LAI prediction, while green bands seem also very useful. However, in biochemical estimation narrow bands are necessary. Indices that incorporate red, blue and IR bands, such as PSRI, SIPI and mNDVI presented good performance in chlorophyll estimation, while CRI did not show any relevance to carotenoids and WI was poorly correlated to water potential. Moreover, analyses indicated that it is very important to use a near red-edge band (701 nm) for effective chlorophyll index design. SSIs that incorporate 701 nm with 511 or 605 nm showed best performance in chlorophyll determination. For carotenoid estimation, a band on the edge of carotenoid absorption (511 nm) combined with a red band performed best, while a normalized index of two water absorption bands (945, 971 nm) proved to be an effective water index. Finally, the attempt to investigate stress conditions through pigment ratios resulted in the use of the band centred at 701 nm.     

Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices

A field experiment with wheat was conducted with four different nitrogen and four different water stress levels, and hyperspectral reflectances in the 350–2500 nm range were recorded at six crop phenostages for two years (2009–2010 and 2010–2011). Thirty-two hyperspectral indices were determined using the first-year reflectance data. Plant nitrogen (N) status, characterized by leaf nitrogen content (LNC) and plant nitrogen accumulation (PNA), showed the highest R ² with the spectral indices at the booting stage. The best five predictive equations for LNC were based on the green normalized difference vegetation index (GNDVI), normalized difference chlorophyll index (NDCI), normalized difference₇₀₅ (ND₇₀₅) index, ratio index-1dB (RI-1dB) and Vogelman index a (VOGa). Their validation using the second-year data showed high R ² (>0.80) and ratio of performance to deviation (RPD; >2.25) and low root mean square error (RMSE; <0.24) and relative error (<10%). For PNA, five predictive equations with simple ratio pigment index (SRPI), photochemical reflectance index (PRI), modified simple ratio₇₀₅ (mSR₇₀₅), modified normalized difference₇₀₅ (mND₇₀₅) and normalized pigment chlorophyll index (NPCI) as predicting indices yielded the best relations with high R ² > 0.80. The corresponding RMSE and RE of these ranged from 1.39 to 1.13 and from 24.5% to 33.3%, respectively. Although the predicted values show good agreement with the observed values, the prediction of LNC is more accurate than PNA, as indicated by higher RMSE and very high RE for the latter. Hence, the plant nitrogen stress of wheat can be accurately assessed through the prediction of LNC based on the five identified reflectance indices at the booting stage.     

Determination of leaf pigment content in Calluna vulgaris shoots from spectral reflectance

Leaf pigment concentrations are indicative of a range of plant physiological properties and processes. The measurement of leaf spectral reflectance is a rapid, non-destructive method for determining pigment content, and a large number of spectral indices have been developed for the estimation of leaf pigment content. Despite their ‘applicability’ across many species types, some ecologically important species remain to be explored. The objective of this paper was to investigate a wide range of hyperspectral indices for determining the chlorophyll and carotenoid content in a microphyllous and sclerophyllous species, Calluna vulgaris. We carried out spectral measurements on individual heather shoots with a handheld GER-1500 spectroradiometer, and sampled each measured shoot for biochemical analysis using high-performance liquid chromatography (HPLC). We found that several previously published indices performed relatively poorly and yielded coefficients of determination (R²) for chlorophyll ranging from 0.34 to 0.66, with the first derivative of reflectance at the red edge yielding the highest correlation with chlorophyll content (R² = 0.66). Only one of the carotenoid indices we tested (the Photochemical Reflectance Index, PRI) provided a strong correlation with the de-epoxidation state of the xanthophyll cycle (R² = 0.78). The other previously published carotenoid indices performed poorly within our data set. We concluded that only a few of the so-called ‘widely applicable’ indices were applicable to use with this data set, which would present limitations when working with remotely sensed data at a larger scale where a mix of species, including Calluna vulgaris, is present.     

Estimating leaf carotenoid contents of shade-grown tea using hyperspectral indices and PROSPECT–D inversion

Quantifying carotenoid contents has many applications in agriculture, ecology, and health science. Hyperspectral reflectance has been one of the promising tools for this purpose. However, previous studies were based on measurements under relatively low light–stress conditions. Therefore, assessing its robustness by using measurements under various levels of stress is required. In this study, the measurements of reflectance and carotenoid contents were carried out with four shading treatments including open–0%, 35%, 75%, and 90% shading to generate various chlorophyll/carotenoid ratios. Then the performances of 15 published hyperspectral indices and PROSPECT–D inversion were evaluated based on our data set for estimating leaf carotenoid contents. According to the ratio of performance to deviation, R_NIR/R₅₁₀, R₇₂₀/R₅₂₁–1, and PROSPECT–D inversion were applicable for this purpose, although calibration of the absorption coefficients was required for PROSPECT–D. Using them, root mean square percentage errors of 4.53–5.46% were achieved. Given that total chlorophyll/carotenoid ratios could be a good indicator for evaluating environmental stress in plants, PROSPECT–D, which also estimates total chlorophyll and anthocyanin contents, could be a strong tool for controlling the qualities of shade-grown tea.     

Red edge spectral measurements from sugar maple leaves

Many sugar maple stands in the northeastern United States experienced extensive insect damage during the 1988 growing season. Chlorophyll data and high spectral resolution spectrometer laboratory reflectance data were acquired for multiple collections of single detached sugar maple leaves variously affected by the insect over the 1988 growing season. Reflectance data indicated consistent and diagnostic differences in the red edge portion (680-750 nm) of the spectrum among the various samples and populations of leaves. These included differences in the red edge inflection point (REIP), a ratio of reflectance at 740-720 nm (RE3/RE2), and a ratio of first derivative values at 715-705 nm (D715/D705), All three red edge parameters were highly correlated with variation in total chlorophyll content. Other spectral measures, including the Normalized Difference Vegetation Index (NDVI) and the Simple Vegetation Index Ratio (VI), also varied among populations and over the growing season, but did not correlate well with total chlorophyll content. Leaf stacking studies on light and dark backgrounds indicated REIP, RE3/RE2 and D715/D705 to be much less influenced by differences in green leaf biomass and background condition than either NDVI or VI.     

Remote estimation of chlorophyll content in higher plant leaves

Indices for the non-destructive estimation of chlorophyll content were formulated using various instruments to measure reflectance and absorption spectra in visible and near-infrared ranges, as well as chlorophyll contents from several non-related species from different climatic regions. The proposed new algorithms are simple ratios between percentage reflectance at spectral regions that are highly sensitive (540 to 630nm and around 700nm) and insensitive (nearinfrared) to variations in chlorophyll content: R NIR / R 700 and R NIR / R 550. The developed algorithms predicting leaf chemistry from the leaf optics were validated for nine plant species in the range of chlorophyll content from 0.27 to 62.9mug cm -2. An error of less than 4.2 mugcm -2 in chlorophyll prediction was achieved. The use of green and red (near 700nm) channels increases the sensitivity of NDVI to chlorophyll content by about five-fold.     

Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data

Hyperspectral/multiangular data allow the retrieval of important vegetation properties at canopy level, such as the Leaf Area Index (LAI) and Leaf Chlorophyll Content. Current methods are based on the relationship between biophysical properties and retrievals from those spectral bands (from the complete hyperspectral/multiangular information) where specific absorption features are present within the considered spectral range. Furthermore, new sensors such as PROBA/CHRIS provide continuous hyperspectral reflectance measurements that can be considered as a continuous function of wavelength. The mathematical analysis of these continuous functions allows a new way of exploiting the relationships between spectral reflectance and biophysical variables by more powerful and stable mathematical tools, in particular for the retrieval of LAI and chlorophyll content. Within the overall context of the European Space Agency (ESA) Spectra Barrax Campaign (SPARC) experiment, an extensive field study was carried out in La Mancha, Spain, simultaneously to the overflight of airborne imaging spectrometers (AHS, HyMAP, ROSIS) and the overpass of CHRIS‐PROBA and MERIS sensors. During the SPARC‐2003 and SPARC‐2004 campaigns, numerous ground measurements were made in the Barrax study area (covering LAI, fCover, leaf chlorophyll a+b, leaf water content and leaf biomass), together with other complementary data, and a total of 17 CHRIS‐PROBA images were acquired. Representative points have been selected from a total of nine different crops, and also retrieved from the CHRIS‐PROBA images acquired within the days of the field campaign. About 250 reflectance spectra from five different observation angles have been analysed. Hyperspectral reflectance spectra have been adjusted by means of third‐degree polynomial functions between 500 nm and 750 nm, and correlations observed between LAI values and the coefficients of these polynomials yielded LAI as a result of the mathematical fitting. On the other hand, the area under the spectral reflectance curves has been calculated in the interval from 600 nm to 700 nm, the region of the red spectral interval where strong absorption features for chlorophyll have been observed, though areas under the curves are also strongly correlated to the chlorophyll content of the crops. Furthermore, a linear relationship between these areas and the chlorophyll content is proposed in this work. This relationship allows the retrieval of leaf chlorophyll by satellite data, based on the spectral information. Both of the proposed methods are almost independent of the observation angles employed. The high number of in situ measurements acquired simultaneously to satellite overpasses, and the broad available range of data, have allowed validation of both methods, with a large number of data and in a statistically consistent manner.     

Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops

An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R₇₅₀/R₇₁₀ and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies.     

Simulating models for Phaeozem hyperspectral reflectance

It is difficult to analyse soil properties quantitatively with multispectral remote sensing data. An alternative solution is to determine the main spectral characteristic control points of soil hyperspectral reflectance curves by sensitivity analysis methods. Hyperspectral reflectance is simulated using the control points based on multispectral reflectance collected from satellites in this study. The laboratory hyperspectral reflectance and its continuum-removed curve of Phaeozem and the parent material (PM) of samples collected from Heilongjiang Province, China were analysed, and the spectral characteristic control points determined. Hyperspectral simulating linear and quadratic models based on laboratory reflectance were then built. Results show that montmorillonite and illite are the dominant minerals in Phaeozem PM. Organic matter content determines the spectral characteristics of Phaeozem and makes it suitable for reflectance simulation in the spectrum range of 1000 nm and less; the higher the organic matter content the greater the spectral absorption area. There are two absorption valleys at 500 and 660 nm, which determine the spectral characteristic control points of Phaeozem between 450 and 930 nm, namely 450, 500, 590, 660 and 930 nm. Both the linear and quadratic simulation models built with the characteristic control points accurately describe Phaeozem reflectance, which proves that the characteristic control points are selected reasonably and representatively. The hyperspectral simulation method based on multispectral reflectance closely represents the characteristics of Phaeozem hyperspectral reflectance, partly removes noise and improves the precision of predicting organic matter content. Therefore the method is feasible and useful for data compression of Phaeozem hyperspectral reflectance, soil and vegetation indices building, and quantitative remote sensing in the Phaeozem Zone, northeast China.     

Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery

Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25-4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical-optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%-1.07% and 0.36%-1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R² = 0.89 to R² = 0.97 and discrepancies of 0.02%-3.63% and 0.24%-7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R² = 0.47, RMSE = 4.34 μg/cm², and jackknifed RMSE of 5.69 μg/cm² for needle chlorophyll content ranging from 24.9 μg/cm² to 37.6 μg/cm². The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R² = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.