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.     

Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation

This paper reports a new approach for quantifying vegetation pigment concentrations through wavelet decomposition of hyperspectral remotely sensed data. Wavelets are a group of functions that vary in complexity and mathematical properties, that are used to dissect data into different frequency components and then characterize each component with a resolution appropriate to its scale. Wavelet analysis of a reflectance spectrum is performed by scaling and shifting the wavelet function to produce wavelet coefficients that are assigned to different frequency components. By selecting appropriate wavelet coefficients, a spectral model can be established between the coefficients and biochemical concentrations. Hence, wavelet analysis has the potential to capture much more of the information contained within high‐resolution spectra than previous approaches and offers the prospect of developing robust, generic methods for pigment determinations. The capabilities of the wavelet‐based technique were examined using reflectance spectra and pigment data collected for a range of plant species at leaf and canopy scales. For the combined data set and all of the individual vegetation types, methods based on wavelet decomposition appreciably outperformed narrowband spectral indices and stepwise selection of narrowband reflectance. However, there was variation between vegetation types in the relative performance of the three different feature extraction techniques employed for selecting the wavelet coefficients for use in predictive models. There was also considerable variability in the performance of predictive models according to the wavelet function used for spectral decomposition and the optimum wavelet functions differed between vegetation types and between individual pigments within the same vegetation type. The research indicates that wavelet analysis holds promise for the accurate determination of chlorophyll a and b and the carotenoids, but further work is needed to refine the approach.     

Spectroscopic determination of leaf water content using continuous wavelet analysis

The gravimetric water content (GWC, %), a commonly used measure of leaf water content, describes the ratio of water to dry matter for each individual leaf. To date, the relationship between spectral reflectance and GWC in leaves is poorly understood due to the confounding effects of unpredictably varying water and dry matter ratios on spectral response. Few studies have attempted to estimate GWC from leaf reflectance spectra, particularly for a variety of species. This paper investigates the spectroscopic estimation of leaf GWC using continuous wavelet analysis applied to the reflectance spectra (350-2500 nm) of 265 leaf samples from 47 species observed in tropical forests of Panama. A continuous wavelet transform was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength and scale. Linear relationships were built between wavelet power and GWC expressed as a function of dry mass (LWC_D) and fresh mass (LWC_F) in order to identify wavelet features (coefficients) that are most sensitive to changes in GWC. The derived wavelet features were then compared to three established spectral indices used to estimate GWC across a wide range of species.Eight wavelet features observed between 1300 and 2500 nm provided strong correlations with LWC_D, though correlations between spectral indices and leaf GWC were poor. In particular, two features captured amplitude variations in the broad shape of the reflectance spectra and three features captured variations in the shape and depth of dry matter (e.g., protein, lignin, cellulose) absorptions centered near 1730 and 2100 nm. The eight wavelet features used to predict LWC_D and LWC_F were not significantly different; however, predictive models used to determine LWC_D and LWC_F differed. The most accurate estimates of LWC_D and LWC_F obtained from a single wavelet feature showed root mean square errors (RMSEs) of 28.34% (R² = 0.62) and 4.86% (R² = 0.69), respectively. Models using a combination of features resulted in a noticeable improvement predicting LWC_D and LWC_F with RMSEs of 26.04% (R² = 0.71) and 4.34% (R² = 0.75), respectively. These results provide new insights into the role of dry matter absorption features in the shortwave infrared (SWIR) spectral region for the accurate spectral estimation of LWC_D and LWC_F. This emerging spectral analytical approach can be applied to other complex datasets including a broad range of species, and may be adapted to estimate basic leaf biochemical elements such as nitrogen, chlorophyll, cellulose, and lignin.     

Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling

We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.     

高光谱技术提取不同作物叶片类胡萝卜素信息

以棉花、玉米、大豆、甘薯四种作物为材料，各采集叶片30张(处于不同部位、不同功能期)，分别测定其反射光谱和叶绿素、类胡萝卜素含量。目的在于探讨利用高光谱技术提取类胡萝卜素信息的可行性方法。结果表明，由于叶绿素与类胡萝卜素间存在显的相关性，在叶片水平，利用高光谱反射率估测叶片类胡萝卜素绝对量是可行的。与类胡萝卜素／叶绿素比值或类胡萝卜素含量相比，类胡萝卜素密度(单位叶片面积类胡萝卜素总量，Cardens)与光谱反射率间的相关性更为稳定。类胡萝卜素光谱吸收峰(470nm)附近的反射光谱与Cardens间的相关性较差，基于类胡萝卜素吸收峰附近反射光谱的光谱指数(如PSSRc、PSNDc)与Cardens间也表现出较弱的相关性。叶绿素光谱指数(如SR705、ND705)与Cardens间存在良好的相关性，红边光谱区的微分光谱、包络线归一化吸收深度等高光谱指数与Cardens间也表现出了良好的相关性。     

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.     

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.     

The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings

Field spectroscopy is a rapid and non-destructive analytical technique that may be used for assessing plant stress and disease. The objective of this study was to develop spectral indices for detection of Ganoderma disease in oil palm seedlings. The reflectance spectra of oil palm seedlings from three levels of Ganoderma disease severity were acquired using a spectroradiometer. Denoizing and data transformation using first derivative analysis was conducted on the original reflectance spectra. Then, comparative statistical analysis was used to select significant wavelength from transformed data. Wavelength pairs of spectral indices were selected using optimum index factor. The spectral indices were produced using the wavelength ratios and a modified simple ratio method. The relationship analysis between spectral indices and total leaf chlorophyll (TLC) was conducted using regression technique. The results suggested that six spectral indices are suitable for the early detection of Ganoderma disease in oil palm seedlings. Final results after regression with TLC showed that Ratio 3 is the best spectral index for the early detection of Ganoderma infection in oil palm seedlings. For future works, this can be used for the development of robust spectral indices for Ganoderma disease detection in young and mature oil palm using airborne hyperspectral imaging.     

A novel restoration approach for vegetation reflectance spectra at noisy bands using the principal component analysis method

ABSTRACT

Due to the signal-to-noise ratio (SNR) of sensors, as well as atmospheric absorption and illumination conditions, etc., hyperspectral data at some bands are of poor quality. Data restoration for noisy bands is important for many remote sensing applications. In this paper, we present a novel data-driven Principal Component Analysis (PCA) approach for restoring leaf reflectance spectra at noisy bands using the spectra at effective bands. The technique decomposes the leaf reflectance spectra into their principal components (PCs), selects the leading PCs that describe the most variance in the data, and restores the data from these components. First, the first 10 PCs were determined from a training dataset simulated by the leaf optical properties model (PROSPECT-5) that contained 99.998% of the total information in the 3636 training samples. Then, the performance of the PCA method for restoration of the reflectance at noisy bands was investigated using the ANGERS leaf optical properties dataset; the results showed the spectral root mean squared error (RMSE) is in the range 6.46 × 10^?4 to 6.44 × 10^?2, which is about 3 ? 34 times more accurate than the stepwise regression method and partial least squares method (PLSR) for the ANGERS dataset. The results also showed that if the noisy bands are far away from the effective bands, the accuracy of the restored leaf reflectance spectra will decrease. Thirdly, the reliability of the restored reflectance spectra for retrieving leaf biochemical contents was assessed using the ANGERS dataset and leaf optical properties dataset established by the Beijing Academy of Agriculture and Forestry Sciences (BAAFS). Three water-sensitive vegetation indices ? normalized difference water index (NDWI), normalized difference infrared index (NDII) and Datt water index (DWI), derived from the restored leaf spectra ? were employed to retrieve the equivalent water thickness (EWT). The results showed that the leaf water content can be accurately retrieved from the restored leaf reflectance spectra. In addition, the PCA method to restore vegetation spectral reflectance can be applied on canopy level as well. The results showed that the spectral root mean squared error (RMSE) is in the range 8.22 × 10^?4 to 1.87 × 10^?2. The performance of the restored canopy spectra was investigated according to the results of retrieving canopy equivalent water thickness (CEWT) using the five spectral indices NDWI, NDWI₁₃₇₀, NDWI₁₈₉₀, NDII and DWI. The results indicated that the restored canopy spectra can be used for retrieving CEWT reliably and improve the accuracy of retrieval compared to the results using original canopy reflectance spectra.     

Detecting near-surface moisture stress in Sphagnum spp.

Estimating near-surface moisture conditions from the reflectance spectra (400-2500 nm) of Sphagnum moss offers great opportunities for the use of remote sensing as a tool for large-scale detailed monitoring of near-surface peatland hydrological conditions. This article investigates the effects of changes in near-surface and surface moisture upon the spectral characteristics of Sphagnum moss. Laboratory-based canopy reflectance data were collected from two common species of Sphagnum subjected to drying and subsequent rewetting. Several spectral indices developed from the near infra-red (NIR) and shortwave infra-red (SWIR) liquid water absorption bands and two biophysical indices (REIP and the chlorophyll index) were correlated with measures of near-surface moisture. All spectral indices tested were significantly correlated with near-surface moisture (with r between 0.27 and 0.94). The strongest correlations were observed using indices developed from the NIR liquid water absorption features (fWBI₉₈₀ and fWBI₁₂₀₀). However, a hysteretic response was observed in both NIR indices when the canopies were re-hydrated, a finding which may have implications for the timing of remote sensing image acquisition. The Moisture Stress Index (MSI), developed from the SWIR liquid water absorption feature also showed strong correlations with near-surface wetness although the range of moisture conditions over which the index was able to detect change was highly dependent on Sphagnum species. Of the biophysical spectral indices tested (REIP and the chlorophyll index), the most significant relationships were observed between the chlorophyll index and near-surface wetness. All spectral indices tested were species specific, and this is attributed to differences in canopy morphology between Sphagnum species. The potential for developing estimations of surface and near-surface hydrological conditions across northern peatlands using remote sensing technology is discussed.     

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.     

全球典型植被叶片光谱特征及其对LAI反演的影响分析

在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。     

Continuous wavelets for the improved use of spectral libraries and hyperspectral data

Spectral libraries are commonly established as a means to archive representative signatures of natural materials. Such signatures can then be used to train feature extraction and classification algorithms applied to imagery, for comparison with unlabeled spectra. A number of spectral libraries are publicly available and widely used in the community. Disparities in viewing and illumination measurement configurations between libraries generally preclude the direct comparison of spectra for the same materials. Within libraries, measurements may be reported for varying sample properties, such as grain size in the case of powdered minerals or leaf or canopy structure in the case of vegetation. In such instances, use of the library and the selection of representative spectra to identify an unknown material may require a priori knowledge or an educated guess of the physical properties of the unknown material to conduct the comparison.This study demonstrates that continuous wavelet analysis can provide a new and useful representation of spectral libraries and minimize these disparities amongst libraries. In the context of spectral mixture analysis we suggest that the selection of representative endmember spectra from spectral libraries can be more readily defined in the wavelet domain than using reflectance data. In the context of sensing target compositional variability, for example changes in the chemistry of a given mineral, spectral differences due to distinct sample composition are more readily identified using wavelets. The examples provided in this paper are mainly for powdered mineral spectra because there are a number of widely known public spectral libraries of powdered minerals that have been in common use in the hyperspectral community but the principles apply to a range of natural materials including vegetation.     

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.     

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.     

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。     

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.     

Estimation of Lambert parameter based on leaf-scale hyperspectral images using dichromatic model-based PCA

Low-altitude hyperspectral observation systems are promising sensing tools for acquisition of optical remote-sensing data under the humid subtropical climate in Japan. The system is also capable of acquiring leaf-scale optical information free from atmospheric effect. However, the leaf-scale hyperspectral data are affected by shading and various illumination conditions such that it is difficult to obtain consistent characteristics of the spectral information. The aim of this article is the extraction of Lambert coefficients as an inherent leaf spectral profile. In this work, we propose a dichromatic model-based principal component analysis on hyperspectral data by utilizing leaf-scale hyperspectral data in order to diminish the spectral difference caused by the illumination condition and bidirectional reflectance distribution function. The results show that indices of chlorophyll content based on the estimated Lambert coefficients are consistent with the growth stages of a paddy field, whether the illumination condition is clear sky or overcast.     

Variations of ocean colour parameters with nonuniform vertical profiles of chlorophyll concentration

Based on radiative transfer simulations, the effects of nonuniform chlorophyll profiles in case 1 waters on the penetration depth, the above‐surface spectral remote‐sensing reflectance, and the optically weighted chlorophyll concentration are investigated. The simulations for nonuniform chlorophyll profiles are compared with those for homogeneous ocean whose chlorophyll concentrations are identical to the surface chlorophyll concentrations in the inhomogeneous ocean. Due to influence of the nonuniformity of chlorophyll profile, the maximum relative error for the penetration depth at 445 nm is more than 60%, the spectral remote‐sensing reflectance is about 40% and the optically weighted chlorophyll concentration is about 40% within the range of our simulations. However, the simulation shows that there is always a spectral band where the value of above‐surface remote‐sensing reflectance is not influenced by the nonuniformity. Depending on this band, a new model for retrieving sea surface chlorophyll concentration is designed by adding a compensation term into the variable in SeaWiFS OC2V4 algorithm. By using an iterative method with this new model, sea surface chlorophyll concentration can be well retrieved even in an area where the vertical chlorophyll distribution is unknown.