Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data

Recent studies have demonstrated that the decomposition of hyperspectral data using wavelet analysis is able to generate wavelet coefficients that can be used for estimating leaf chlorophyll (chl) concentrations. However, there is considerable scope for refining such techniques and this study addresses this issue by identifying the optimal spectral domain for use in constructing predictive models. Leaf reflectance spectra were simulated with the PROSPECT model (a model of leaf optical properties spectra) using randomly selected values for the input parameters. From reflectance and first derivative spectra different spectral wavelength domains were extracted, ranging from 400–450 to 400–2500 nm, using increments of 50 nm for the upper wavelength limit. Using the data for each wavelength domain, continuous wavelet decomposition was applied using 53 different wavelets, in turn. The resulting wavelet coefficients, from scales 1 to 128, were used as independent factors to construct predictive models for leaf chl concentration. Wavelet coefficients (at a specific scale generated by a given wavelet) in the chl absorption region remain constant when using spectral wavelength domains of 400–900 nm and broader, but narrower domains cause variability in the coefficients. Lower scale wavelet coefficients (scales 1–32) contain little information on chl concentration and their predictive performance does not vary with the spectral wavelength domain used. The higher scale wavelet coefficients (scales 64 and 128) can capture information on chl concentration, and predictive capability increases rapidly when the spectral wavelength domains vary from 400–700 to 400–900 nm but it can decrease or fluctuate for broader domains. In terms of accuracy and computational efficiency, models derived from the spectral wavelength domain 400–900 nm which use wavelet coefficients from scale 64 are optimal and a range of wavelet functions are suitable for performing the decomposition. The importance of optimizing the spectral wavelength domain highlighted by these findings has broader significance for the use of wavelet decomposition of hyperspectral data in quantifying other vegetation biochemicals and in other remote sensing applications.     

Airborne hyperspectral mapping of trees in an urban area

The management of trees in urban areas requires accurate maps, which are difficult to build in the dense patchwork of numerous material properties. Remote sensing is a useful technique that measures the response of all vegetation occurrences, including trees, when high spatial resolution is available. The continuous narrow spectral bands of hyperspectral images enable the detection of the oxygen and water content, which ensures a perfect correction of the atmospheric effect. When calibrated, sunlight reflectance images can be used to map surface chemical compositions by the detection of diagnostic and sharp absorption features. In the visible and near- infrared, the vegetation is detected by chlorophyll-a absorption features that are characteristic of the pigment content. The reflectance intensities due to the texture of leaves occur between 450 and 920 nm while the water content imprint is detectable beyond 920 nm. The sharp spectral feature intensities of the main associated pigments, not only chlorophylls, are well quantified by indices measuring a normalized difference of reflectance in a spectral interval between two bounding wavelengths. A regression line calculated on all bands within that interval ensures a low sensitivity of the indices to the smaller variations in reflectance intensity. Such unbiased indices may be combined, using successive index thresholds deduced from a training spectral library, to divide the spectra into subsets, minimizing the confusion between the numerous vegetation types with almost identical compositions. Therefore, for each subset of the spectra, a classic spectral angle mapping (SAM) method can be used on the corresponding sub-selection of the spectral library to measure angles at full spectral resolution and map tree types with great accuracy, grouped according to their spectral similarity. In this study, chemical and physical information is carefully separated. The tree crown physical properties are studied by comparing the local juxtaposition of pixel sets to a characteristic texture identifiable by image segmentation into objects. Instead of looking for objects in the reflectance image or any statistical compression of its information, a 25 channel co-image, built from 11 information layers of chemical sharp spectral feature indices and 14 information layers of SAM indices matching a spectral library of reference vegetation groups, was used. Tree canopies also present wide internal variations due to (i) a complex mixture with a background in the case of sparse foliage, or (ii) pigment content adaptation to light exposure intensity from one side to another. Both effects are minimized by using the mean spectrum of each object, assuming that less significant spectra, being at plus or minus one or two standard deviations from an object mean spectrum, would be less affected by anomalous pixel data. Thus, two overlapping hierarchic layers at the pixel scale and the object scale are available to describe the main chemistry or pigment content that identifies the vegetation types. The final classification is given by the upper layer at the object scale but in such an organization, the pixel scale layers can be used to analyse the data further and reorder them to obtain other parameters potentially useful for management purposes.     

Spectral discrimination of vegetation types in a coastal wetland

Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra. For this purpose, the field reflectance spectra of 27 saltmarsh vegetation types of the Dutch Waddenzee wetland were analysed in three steps. Prior to analysis, the spectra were smoothed with an innovative wavelet approach.In the first stage of the analysis, the reflectance spectra of the vegetation types were tested for differences between type classes. It was found that the reflectance spectra of saltmarsh vegetation types are statistically significantly different for various spectral regions.Secondly, it was tested whether this statistical difference could be enhanced by using continuum removal as a normalisation technique. For vegetation spectra, continuum removal improves the statistical difference between vegetation types in the visible spectrum, but weakens the statistical difference of the spectra in the near-infrared and shortwave infrared part of the spectrum.Thirdly, after statistical differences were found, it was determined how distant in spectral space the vegetation type classes were from each other, using the Bhattacharyya (BH) and the Jeffries-Matusita (JM) distance measures. We selected six wavelengths for this, based on the statistical analysis of the first step. The potential of correct classification of the saltmarsh vegetation types using hyperspectral remote sensing is predicted by these distance measures.It is concluded that the reflectance of vegetation types is statistically different. With high quality radiometric calibration of hyperspectral imagery, it is anticipated that vegetation species may be identified from imagery using spectral libraries that were measured in the field during the time of image acquisition.     

Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis

The dynamics of foliar chlorophyll concentrations have considerable significance for plant-environment interactions, ecosystem functioning and crop growth. Hyperspectral remote sensing has a valuable role in the monitoring of such dynamics. This study focussed upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Leaf-scale radiative transfer models were used to generate very large spectral data sets with which to develop and rigorously test refinements to the approach and compare it with existing spectral indices. The results demonstrated that by decomposing leaf spectra, the resultant wavelet coefficients can be used to generate accurate predictions of chlorophyll concentration, despite wide variations in the range of other biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and a range of spectral indices. The paper discusses the possibilities for further refining the wavelet approach and for extending the technique to the sensing of a variety of vegetation properties at a range of spatial scales.     

A spectral matching quality indicator for material mapping using spectral library search methods

Spectral library search methods are being used increasingly as an efficient approach for exploiting hyperspectral remotely sensed data in material identification and mapping applications. The aim of this study was to develop a quantitative method, using an indicator called the Quality factor (Q-factor), for providing quantitative information on the reliability of spectral identifications in the interpretation (classification) of unknown spectra by library search methods. This was achieved by summing the two main requirements of a typical reflectance spectral library search for material mapping: (1) a reliable correlation between spectral matching scores and material similarity, and (2) a reliable separation ability between the relevant and non-relevant parts of the candidate reference spectra. These form a metric whose values reflect the closeness of the output reference spectra to the input unknown spectra for a chosen library search method. The Q-factor was tested as an indicator of the reliability of the material identifications by the library search for a range of unknown reflectance spectra of various types of vegetation, soils and minerals collected from the US Geological Survey (USGS) Spectral Library and from our in-house spectral database. The results indicate that this approach has the potential to separate correct and incorrect spectral identifications resulting from a particular spectral library search method using a reference similarity logic. The method may be applied to any combination of deterministic spectral matching alternatives using reflectance spectra. Spectrum-level quality information provided by the Q-factor is useful for optimizing a particular search method or for choosing the most appropriate method for distinct identification and classification problems.     

Towards the Remote Sensing of Matorral Vegetation Physiology: Relationships between Spectral Reflectance, Pigment, and Biophysical Characteristics of Semiarid Bushland Canopies

This study investigates the relationships between the spectral reflectance characteristics and the concentrations of photosynthetic pigments and biophysical attributes of a structurally complex, spatially heterogeneous vegetation canopy with varying background properties. A field experiment was performed in the Guadalentin basin, Spain using matorral vegetation canopies dominated by Rosmarinus officinalis, Cistus albidus, and Anthyllis cytosoides. A spectroradiometer was used to record the reflectance of a series of sites at which measurements were made of the concentrations per unit ground area and per unit leaf mass of chlorophyll a and b and the carotenoids, together with leaf area index and percent canopy cover. A range of spectral characteristics was examined which have been found previously to be related to pigment concentrations and biophysical properties of vegetation. For matorral vegetation many of these spectral characteristics were unrelated or only weakly related to canopy properties. However, it was found that pigment concentrations per unit ground area were related to ratios of reflectance in narrow spectral bands within the near-infrared region, ratios of bands within the red region, and characteristics of the amplitude of first derivative spectra in the red edge region. Pigment concentrations per unit leaf mass were correlated with ratios of bands around the nearinfrared “shoulder” and the amplitude of the first derivative in certain visible wavelengths. LAI and percent cover were related to ratios of reflectance in narrow bands on the near-infrared plateau and red edge features of canopy reflectance spectra, as well as with the amplitude of the first derivative in the red edge and visible regions respectively.     

A new vegetation index derived from the pattern decomposition method applied to Landsat‐7/ETM+ images in Mongolia

The goal of this study was to estimate vegetation coverage and map the land‐cover in an experimental field (60×60 km) near Mandalgobi, Mongolia using Landsat‐7/ETM+ data for ground truthing in the Advanced Earth Observing Satellite II (ADEOS‐II) Mongolian Plateau Experiment (AMPEX). We measured soil moisture, vegetation coverage, and vegetation moisture in the field at 49 grid points around the time that the Aqua satellite passed over the area. We also surveyed the land‐cover in the field. Using ground‐based data and characteristics of spectral reflectance, we attempted to extract vegetation information from satellite data using the pattern decomposition method, which is a type of spectral mixture analysis. This method uses normalized spectral shapes as endmembers, which do not change between scenes. We defined an index using the pattern decomposition coefficients to analyse sparsely vegetated areas. The index showed a linear relationship with vegetation coverage. The vegetation coverage was estimated for the study site, and the average coverage at the site was 21.4%. Land‐cover types were classified using the index and the pattern decomposition coefficients; the kappa coefficient was 0.75. The index was useful for estimating vegetation coverage and land‐cover mapping for semiarid areas.     

Pattern decomposition method for hyper-multi-spectral data analysis

The ‘pattern decomposition method’ (PDM) is a new analysis method originally developed for Landsat Thematic Mapper (TM) satellite data. Applying the PDM to the radiospectrometer data of ground objects, 121 dimensional data in the wavelength region 350–2500?nm were successfully reduced into three-dimensional data. The nearly continuous spectral reflectance of land cover objects could be decomposed by three standard spectral patterns with an accuracy of 4.17% per freedom. We introduced a concept of supplementary spectral patterns for the study of specific ground objects. As an example, availability of a supplementary spectral pattern that can rectify standard spectral pattern of vivid vegetation for spectra of withered vegetation was studied. The new Revised Vegetation Index based on Pattern Decomposition (RVIPD) for hyper-multi-spectra is proposed as a simple function of the pattern decomposition coefficients including the supplementary vegetation pattern. It was confirmed that RVIPD is linear to the area cover ratio and also to the vegetation quantum efficiency.     

Smoothing vegetation spectra with wavelets

In hyperspectral remote sensing, spectra are increasingly analysed using methods developed for laboratory studies, such as derivative analysis. These techniques require smooth reflectance spectra. Therefore, there is a need for smoothing algorithms that fulfil the requirement of preserving local spectral features while simultaneously removing noise. Noise occurs in variable intensity and over different band widths.

Several methods for smoothing a signal exist, including the widely used median and mean filters, the Savitzky–Golay filter applied to laboratory spectra, the cubic spline, and the recently developed transform-based thresholding using the wavelet transform. We compare all these methods using reflectance spectra of the canopy of salt marsh vegetation.

The best trade-off between noise reduction and the preservation of spectral features was found to be the wavelet transform, specifically using a translation invariant de-noising based on the non-decimated or stationary wavelet transform.     

Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery

Hyperspectral remote sensing provides great potential to monitor and study biodiversity of tropical forests through species identification and mapping. In this study, five species were selected to examine crown-level spectral variation within and between species using HYperspectral Digital Collection Experiment (HYDICE) data collected over La Selva, Costa Rica. Spectral angle was used to evaluate the spectral variation in reflectance, first derivative and wavelet-transformed spectral domains. Results indicated that intra-crown spectral variation does not always follow a normal distribution and can vary from crown to crown, therefore presenting challenges to statistically define the spectral variation within species using conventional classification approaches that assume normal distributions. Although derivative analysis has been used extensively in hyperspectral remote sensing of vegetation, our results suggest that it might not be optimal for species identification in tropical forestry using airborne hyperspectral data. The wavelet-transformed spectra, however, were useful for the identification of tree species. The wavelet coefficients at coarse spectral scales and the wavelet energy feature are more capable of reducing variation within crowns/species and capturing spectral differences between species. The implications of this examination of intra- and inter-specific variability at crown-level were: (1) the wavelet transform is a robust tool for the identification of tree species using hyperspectral data because it can provide a systematic view of the spectra at multiple scales; and (2) it may be impractical to identify every species using only hyperspectral data, given that spectral similarity may exist between species and that within-crown/species variability may be influenced by many factors.     

Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data

One of the major environmental problems resulting from the regular flooding of rivers in Europe is the heavy metal contamination of soils. Various studies have shown that soil contamination may influence plant physiology and, through changes in leaf pigment concentrations, influence reflectance spectra. The main objective of this case study was to study whether the red-edge position (REP) of vegetation spectra may provide information on soil contamination by heavy metals in river floodplains. The use of the maximum first derivative, smoothing methods (like polynomial fitting and the inverted Gaussian function) and interpolation methods based on just a few spectral bands were evaluated for a test site in the floodplain of the river Waal in the Netherlands. On selected transects, heavy metal concentrations of soil samples and reflectance spectra of the growing vegetation using a field spectroradiometer were measured. A significant negative correlation between the REP and heavy metal concentration was found using the maximum first derivative method (R ²=0.64). The first derivative spectra in this study showed the presence of more than one peak within the red-edge region, as found by other authors. This phenomenon requires further detailed research using very fine spectral measurements.     

Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data

View angle effects present in spectral vegetation indices can either be regarded as an added source of uncertainty for variable retrieval or as a source of additional information, enhancing the variable retrieval; however, the magnitude of these angular effects remains for most indices unknown or unquantified. We use the ESA-mission CHRIS-PROBA (Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy) providing spaceborne imaging spectrometer and multiangular data to assess the reflectance anisotropy of broadband as well as recently developed narrowband indices. Multiangular variability of Hemispherical Directional Reflectance Factor (HDRF) is a prime factor determining the indices´ angular response. Two contrasting structural vegetation types, pine forest and meadow, were selected to study the effect of reflectance anisotropy on the angular response. Calculated indices were standardized and statistically evaluated for their varying HDRF. Additionally we employ a coupled radiative transfer model (PROSPECT/FLIGHT) to quantify and substantiate the findings beyond an incidental case study. Nearly all tested indices manifested a prominent anisotropic behaviour. Apart from the conventional broadband greenness indices [e.g. Simple Ratio Index (SRI), Normalized Difference Vegetation Index (NDVI)], light use efficiency and leaf pigment indices [e.g. Structure Insensitive Pigment Index (SIPI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index (ARI)] did express significant different angular responses depending on the vegetation type. Following the quantification of the impact, we conclude that the angular-dependent fraction of non-photosynthetic material is of critical importance shaping the angular signature of these VIs. This work highlights the influence of viewing geometry and surface reflectance anisotropy, particularly when using light use efficiency and leaf pigment indices.     

小波包信息熵特征矢量光谱角高光谱影像分类

目的 针对高光谱数据波段多、数据存在冗余的特点,将小波包信息熵特征引入到高光谱遥感分类中。方法 通过对光谱曲线进行小波包分解变换,定义了小波包信息熵特征矢量光谱角分类方法(WPE-SAM),基于USGS光谱库中4种矿物光谱数据的分析表明,WPE-SAM可增大类间地物的可区分性。在特征矢量空间对Salina高光谱影像进行分类计算,并讨论了小波包最佳分解层的确定,分析了WPE-SAM与光谱角制图(SAM)方法的分类精度。结果 Salina数据实例计算表明：小波包信息熵矢量能较好地描述原始光谱特征,WPE-SAM分类方法可行,总体分类精度(OA)由SAM的78.62%提高到WPE-SAM的78.66%,Kappa系数由0.769 0增加到0.769 5,平均分类精度(AA)由83.14%提高到84.18%。此外,通过Pavia数据验证了WPE-SAM分类方法具有较强的普适性。结论 小波包信息熵特征可较好地表示原始光谱波峰、波谷等特征信息,定义的小波包信息熵特征矢量光谱角分类方法(WPE-SAM)可增大类间地物可区分性,有利于分类。实验结果表明,WPE-SAM分类方法技术可行,总体精度及Kappa系数较SAM有一定的提高,且有较强的普适性。但WPE-SAM方法精度与效率有待进一步提高。     

Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef,Australia

Studies investigating the spectral reflectance of coral reef benthos and substrates have focused on the measurement of pure endmembers, where the entire field of view (FOV) of a spectrometer is focused on a single benthos or substrate type. At the spatial scales of the current satellite sensors, the heterogeneity of coral reefs even at a sub-metre scale means that many individual image pixels will be made up of a mixture of benthos and substrate types. If pure endmember spectra are used as training data for image classification, there is a spatial discrepancy, because many pixels will have a mixed endmember spectral reflectance signature. This study investigated the spectral reflectance of coral reef benthos and substrates at a spatial scale directly linked to the pixel size of high spatial resolution imaging systems, by incorporating multiple benthos and substrate types into the spectrometer FOV in situ. A total of 334 spectral reflectance signatures were measured of 19 assemblages of the coral reef benthos and substrate types. The spectra were analysed for separability using first derivative values, and a discrimination decision tree was designed to identify the assemblages. Using the decision tree, it was possible to identify 15 assemblages with a mean overall classification accuracy of 62.6%.     

Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress

Chlorophyll content can be used as an indicator to monitor crop diseases. In this article, an experiment on winter wheat stressed by stripe rust was carried out. The canopy reflectance spectra were collected when visible symptoms of stripe rust in wheat leaves were seen, and canopy chlorophyll content was measured simultaneously in laboratory. Continuous wavelet transform (CWT) was applied to process the smoothed spectral and derivative spectral data of winter wheat, and the wavelet coefficient features obtained by CWT were regarded as the independent variable to establish estimation models of chlorophyll content. The hyperspectral vegetation indices were also regarded as the independent variable to build estimation models. Then, two types of models above-mentioned were compared to ascertain which type of model is better. The cross-validation method was used to determine the model accuracies. The results indicated that the estimation model of chlorophyll content, which is a multivariate linear model constructed using wavelet coefficient features extracted by Mexican Hat wavelet function processing the smoothed spectrum (WSMH1 and WSMH2), is the best model. It has the highest estimation accuracy with modelled coefficient of determination (R²) of 0.905, validated R² of 0.913, and root mean square error (RMSE) of 0.288 mg fg^?1. The univariate linear model built by wavelet coefficient feature of WSMH1 is secondary and the modelled R² is 0.797, validated R² is 0.795, and RMSE is 0.397 mg fg^?1. Both estimation models are better than those of all hyperspectral vegetation indices. The research shows that the feature information of canopy chlorophyll content of winter wheat can be captured by wavelet coefficient features which are extracted by the method of CWT processing canopy reflectance spectrum data. Therefore, it could provide theoretical support on detecting diseases of crop by remote sensing quantitatively estimating chlorophyll content.     

Estimation of foliar pigment concentration in floating macrophytes using hyperspectral vegetation indices

Foliar pigment concentrations of chlorophylls and cartenoids are important indicators of plant physiological status, photosynthesis rate, and net primary productivity. Although the utility of hyperspectral derived vegetation indices for estimating foliar pigment concentration has been documented for many vegetation types, floating macrophytes have not been assessed despite their ecological importance. This study surveyed 39 wetland species (12 floating macrophytes (FM), 8 grasses/sedges/rushes (GSR), and 19 herbs/wildflowers (HWF)) to determine whether foliar pigment concentrations could be estimated from hyperspectral reflectance. Hyperspectral reflectance of samples was recorded using an ASD FieldSpec3 Max portable spectroradiometer with the plant probe attachment or via a typical laboratory set-up. A semi-empirical relationship was established using either a linear, second-degree polynomial or logarithmic function between 13 candidate vegetation indices and chl-a, chl-b, Car, and chl-a + b pigment concentrations. Vegetation indices R-M, CI-Red, and MTCI were strongly correlated with foliar pigment concentrations using a linear fitting function. Chl-a + b and chl-b concentrations for all samples were reasonably estimated by the R-M index (R² = 0.66 and 0.64), although Chl-a and Car concentration estimates using CI-Red were weaker (R² = 0.63 and 0.51). Regression results indicate that pooled samples to estimate individual foliar pigments were less correlated than when each type of vegetation type was treated separately. For instance, chl-a + b was best estimated by CI-Red for FM (R² = 0.80), MTCI for HWF (R² = 0.77), and R-M for GSR (R² = 0.67). Although floating macrophytes feature unique adaptions to their aquatic environment, their foliar pigment concentrations and spectral signatures were comparable to other wetland vegetation types. Overall, vegetation indices that exploit the red-edge region were a reasonable compromise, having good explanatory power for estimation of foliar pigments across the sampled wetland vegetation types and with CI-Red the best suited index for floating macrophytes.     

Interband structure modeling for Pan-sharpening of very high-resolution multispectral images

This paper addresses the modeling of wavelet coefficients for multispectral (MS) band sharpening based on undecimated multiresolution analysis (MRA). The coarse MS bands are sharpened by injecting highpass details taken from a high-resolution panchromatic (Pan) image. Besides the MRA, crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. Once calculated at the coarser resolution, where both types of data are available, such a model shall be extrapolated to the finer resolution in order to weight the Pan details to be injected. The goal is that the merged MS images are most similar to what the MS sensor would collect if it had the same resolution as the broadband Pan imager. Three injection models embedded in an “à trous” wavelet decomposition will be described and compared on a test set of very high-resolution QuickBird MS + Pan data. Two models work on approximation and detail coefficients, respectively, and provide a certain degree of unmixing of coarse MS pixels. The third model is based on spectral fidelity of the merged image to the (resampled) original MS data, that is, no unmixing is attempted. It is much simpler than the other two because it does not require calculation of local statistics. Fusion comparisons on spatially degraded data, of which higher-resolution true MS data are available for reference, show that the former two models yield better results than the latter, in terms of both radiometric and spectral fidelity. The latter, however, yields a reliable sharpening unaffected by local artifacts, regardless of landscape complexity. When local statistics of wavelet coefficients are used, the model estimated on approximation yields slightly better but considerably stabler results than that calculated starting from bandpass details.     

Detecting photosynthetic algal pigments in natural populations using a high-spectral-resolution spectroradiometer

Reflectance data from a high spectral resolution spectroradiometer were obtained onboard a ship in Plymouth coastal waters. These data were analysed to detect algal photosynthetic accessory pigments for comparison with absorption spectra as measured in the laboratory by a spectrophotometer. The overall spectral characteristics of Plymouth waters allowed identification as to population composition. Derivative analysis of the spectra was used to resolve characteristic peaks of specific pigments. It was determined that chlorophyll pigments, a specific carotenoid and sea water absorption bands were detectable in the reflectance data. Absorption bands of photosynthetic and accessory pigments were assessed through chromatographic pigment analysis.     

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.