Vegetation Reflectance Spectra Research Articles

Spectroradiometer data of dominant tree species on the mangrove ecosystem in Central Java, Indonesia

Abstract Remote sensing applications in precision identification of agriculture and forest vegetation have been steadily increasing in recent years due to improvements in spatial and spectral resolutions of remotely sensed imagery. This research aims to analyse and identify the dominant species on the mangrove ecosystem in Timbulsloko Village, Central Java Province (Indonesia) and to develop the spectral library of individual tree of mangrove-forest using a ground-based spectroradiometer. The measurements of the reflectance spectra of ground vegetation were performed by an Analytical Spectral Devices (ASD) FieldSpec Pro-FR Spectroradiometer (FieldSpec 3 FR) with a spectral range of 350-2500 nm and a rapid data collection time of 0.1 second per spectrum. The instrument uses three detectors spanning the visible and near infrared (VNIR) and shortwave infrared (SWIR1 and SWIR2). The FieldSpec spectroradiometer is specifically designed for field environment remote sensing to acquire visible near-infrared (VNIR) and short-wave infrared (SWIR) spectra. The results of field measurements have succeeded in collecting more than 1,500 spectral data from the dominant species of the mangrove ecosystem at the research location, namely Avicennia alba, Avicennia marina, Rhizophora mucronata, and the white reference. We found that the significant spectrum information found in hyperspectral remote sensing data will be very useful for plant mapping, monitoring, and categorization. An effective approach for classifying plant species is the automated interpretation of remote sensing data using a spectral library.

Detecting drought stress occurrence using synergies between Sun induced fluorescence and vegetation surface temperature spatial records

Drought stress occurrence and recovery from drought can be detected using a single spatial set of simultaneous observations of SIF and canopy temperature records. Temporal and spatial responses to drought and heat stresses by plant stands of a drought-adapted diverse grassland ecosystem were studied using sun induced fluorescence (SIF,O2A and O2B bands) and further ecophysiological (canopy temperature (Tsurf), spatially modeled evapotranspiration, vegetation reflectance spectra) variables collected along spatial sampling grids while also utilizing eddy covariance measured carbon dioxide (net ecosystem exchange: NEE, gross primary production: GPP) and water flux (evapotranspiration: ET) data. The grids were of 0.5 and 5 ha spatial extents and contained 78 sampling points. Data were collected in four spatial sampling campaigns, two under drought (early summer) and another two during and after recovery (midsummer) at both spatial resolutions.Small values of spatial SIF_A averages (around 0.5 mW m−2 nm−1 sr−1) under strong early summer drought increased (to around 2 mW m−2 nm−1 sr−1) due recovery upon rain arrivals, showing high (R2: 0.8–0.88) positive temporal correlations to eddy covariance measured carbon (GPP, NEE) and water (ET) fluxes. Spatial averages of LAI, vegetation indices (NDVI, NIRv) and modeled ET followed similar temporal patterns.While SIF was depressed by drought, it showed higher values in high canopy temperature vegetation patches than in vegetation patches with lower Tsurf. The spatial pattern of higher SIF in higher Tsurf patches was persistent (2 weeks) under drought. The positive SIF_A-Tsurf spatial correlation turned into negative/not significant after recovery of the grassland from the drought, while hot summer weather persisted. It is proposed that, by using a single set of simultaneously measured spatial SIF and Tsurf data it is possible to infer whether the studied vegetation is under drought (and heat) stress while it could not be decided on the base of SIF data alone. Evaluation of the slope of the above relationship seems therefore beneficial before e.g. starting the (stress) classification procedure based on SIF.

Biomimetic multilayer film simulating solar spectrum reflection characteristics of natural vegetations for optical camouflage.

The camouflage for developed hyperspectral detection technology, which can accurately distinguish the spectrum between object and background, has emerged as an important unsolved challenge. In this study, a biomimetic film (Ge/ZnS multilayer structure) for optical camouflage of hyperspectral and laser with color simulation has been proposed and experimentally demonstrated. By taking advantage of the wavelength selective property of Ge/ZnS multilayer through film interference, the biomimetic film which can simulate the reflection spectral characteristics of vegetation background and eliminate laser signal has been realized based on inverse design. The selective narrowband absorption can manipulate the contrary condition for hyperspectral camouflage (high reflectance in 0.8-1.3 µm) and laser camouflage (low reflectance at 1.06 µm) in the same waveband. The planarized biomimetic multilayer film presents several distinct advantages: (1) elaborate simulation of vegetation reflectance spectrum for hyperspectral camouflage (the spectral similarity coefficient of 92.1%), and efficient absorption at 1.06 µm for laser camouflage (reflectance of 17.8%); (2) tunable color chrominance of various vegetation types for visual camouflage; (3) thermally robust camouflage performance (up to 250 °C) due to temperature endurable property of Ge and ZnS. The hyperspectral-laser camouflage film expands the design strategy of optical camouflage application.

Combining Chlorophyll Fluorescence and Vegetation Reflectance Indices to Estimate Non-Photochemical Quenching (NPQ) of Rice at the Leaf Scale

Non-photochemical quenching (NPQ) is an indicator of crop stress. Until now, only a limited number of studies have focused on how to estimate NPQ using remote sensing technology. The main challenge is the complicated regulatory mechanism of NPQ. NPQ can be divided into energy-dependent (qE) and non-energy-dependent (non-qE) quenching. The contribution of these two components varies with environmental factors, such as light intensity and stress level due to the different response mechanisms. This study aims to explore the feasibility of estimating NPQ using photosynthesis-related vegetation parameters available from remote sensing by considering the two components of NPQ. We concurrently measured passive vegetation reflectance spectra by spectrometer, as well as active fluorescence parameters by pulse-amplitude modulated (PAM) of rice (Oryza sativa) leaves. Subsequently, we explored the ability of the selected vegetation parameters (including the photochemical reflectance index (PRI), inverted red-edge chlorophyll index (IRECI), near-infrared reflectance of vegetation (NIRv), and fluorescence quantum yield (ΦF)) to estimate NPQ. Based on different combinations of these remote sensing parameters, empirical models were established to estimate NPQ using the linear regression method. Experimental analysis shows that the contribution of qE and non-qE components varied under different illumination conditions. Under high illumination, the NPQ was attributed primarily to the qE component, while under low illumination, it was equally attributed to the qE and non-qE components. Among all tested parameters, ΦF was sensitive to the qE component variation, while IRECI and NIRv were sensitive to the non-qE component variation. Under high illumination, integrating ΦF in the regression model captured NPQ variations well (R2 > 0.74). Under low illumination, ΦF, IRECI, and NIRv explained 24%, 62%, and 65% of the variation in NPQ, respectively, while coupling IRECI or NIRv with ΦF considerably improved the accuracy of NPQ estimation (R2 > 0.9). For all the samples under both low and high illumination, the combination of ΦF with at least one of the other parameters (including IRECI, NIRv and PAR) offers a more versatile and reliable approach to estimating NPQ than using any single parameter alone. The findings of this study contribute to the further development of remote sensing methods for NPQ estimation at the canopy scale in the future.

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

AbstractThis study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8‐day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

A novel integrated approach for monitoring drought stress in an aeolian desertification area using Vegetation Drought Status Index

Abstract Drought is a costly natural disaster. The accuracy and applicability of different drought indexes in drought monitoring at different research areas are also different. Based on remote sensing (RS) and geographic information system technology, we propose a new RS-based drought index, Vegetation Drought Status Index (VDSI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and data from 81 verification points regarding the in situ soil drought status. The model was applied to drought monitoring in Xinjiang Uygur Autonomous Region of China. Based on the comprehensive influence of water absorption on the reflectance spectrum of vegetation and soil, the reflectance of soil in MODIS bands B6 and B7 is generally higher than that of vegetation, so the model can directly obtain the surface soil moisture index. The correlation analysis (R2 > 0.79) was valid; the change trends were the same; i.e., VDSI is a reliable referential index for agricultural drought monitoring.

Disentangling the effects of phosphorus, nitrogen and species identity on the vegetation reflectance spectrum

AbstractQuestionsEffective and successful restoration of species‐rich semi‐natural grasslands requires knowledge of the soil nutrient status, including soil phosphorus availability. Plants are solid indicators of soil nutrient status, because their growth reflects nutrient availability integrated over a certain period. The use of reflectance spectroscopy to estimate vegetation nutrient content has the potential to offer a fast and efficient approach to provide information about soil nutrient availability for plants. Here, we investigated the effect and relative importance of vegetation phosphorus content compared with vegetation nitrogen content and species identity in explaining variation in vegetation reflectance.LocationPot experiment mimicking Western European grassland communities on a restoration trajectory.MethodsWe combined a pot experiment with a broad range of mesotrophic grassland species growing along a soil phosphorus gradient with a multivariate modelling approach. We measured vegetation spectra and vegetation phosphorus and nitrogen content in monocultures and mixtures.ResultsAlthough vegetation biochemistry explained a considerable part of the variation in vegetation reflectance, we found no pronounced absorption features for vegetation nitrogen and phosphorus content across the reflectance spectrum. The relatively large effect of species and community identity suggest that other drivers, for example vegetation architecture, overruled the effect of vegetation biochemistry on the reflectance spectrum. Our findings indicate that species detection and indirect case‐specific estimation of nutrients is possible, especially in structurally less‐complex canopies such as monospecific grass swards.ConclusionsDisentangling the specific drivers of variation in spectral reflectance is challenging. Many studies confound the effect of species identity on the vegetation reflectance spectrum with the effect of vegetation biochemistry. Here, we showed the importance of explicitly taking species identity into account. Gaining insight into light–vegetation interactions and the in‐depth integration of ecological theory in remote sensing are the way forward.

Nitrogen Inversion Model in a Wetland Environment Based on the Canopy Reflectance of Emergent Plants

Reuse of reclaimed water in constructed wetlands is a promising way to conserve water resources and improve water quality, and it is playing a very important role in wetland restoration and reconstruction. This study utilized reflectance spectra of wetland vegetation to estimate nitrogen content in water in the Beijing Bai River constructed wetland, a typically constructed wetland that uses reclaimed water. Canopy reflectance spectra of two dominant plants in the wetland, including reed and cattail, were acquired using a spectrometer (350–2500 nm). Simultaneously, water samples were collected to measure water quality. To establish the appreciate relationship between total nitrogen content (TN) and reflectance spectra, both simple and multiple regression models, including simple ration spectral index (SR), normalized difference spectral index (ND), stepwise multiple linear regression (SMLR) model, and partial least squares regression (PLSR), were adopted in this study. The results showed that (1) compared with simple regression models (SR and ND), multiple regressions models (SMLR and PLSR) could provide a more accurate estimation of TN concentration in the wetland environment. Among these models, the PLSR model had the highest accuracy and was proven to be the most useful tool to reveal the relationship between the spectral reflectance of wetland plants and the total nitrogen consistency of wetland at the canopy scale. (2) The inversion effect of TN concentration in water is slightly better than that of wetland vegetation, and the reflection spectrum of the reed can predict TN concentration more accurately than that of cattail. The finding not only provides solid evidence for the potential application of remote sensing to detect water eutrophication but also enhances our understanding of the monitoring and management of water quality in urban wetlands using recycled water.

Impact of Structural, Photochemical and Instrumental Effects on Leaf and Canopy Reflectance Variability in the 500–600 nm Range

Current rapid technological improvement in optical radiometric instrumentation provides an opportunity to develop innovative measurements protocols where the remote quantification of the plant physiological status can be determined with higher accuracy. In this study, the leaf and canopy reflectance variability in the PRI spectral region (i.e., 500–600 nm) is quantified using different laboratory protocols that consider both instrumental and experimental set-up aspects, as well as canopy structural effects and vegetation photoprotection dynamics. First, we studied how an incorrect characterization of the at-target incoming radiance translated into an erroneous vegetation reflectance spectrum and consequently in an incorrect quantification of reflectance indices such as PRI. The erroneous characterization of the at-target incoming radiance translated into a 2% overestimation and a 31% underestimation of estimated chlorophyll content and PRI-related vegetation indexes, respectively. Second, we investigated the dynamic xanthophyll pool and intrinsic Chl vs. Car long-term pool changes affecting the entire 500–600 nm spectral region. Consistent spectral behaviors were observed for leaf and canopy experiments. Sun-adapted plants showed a larger optical change in the PRI range and a higher capacity for photoprotection during the light transient time when compared to shade-adapted plants. Outcomes of this work highlight the importance of well-established spectroscopy sampling protocols to detect the subtle photochemical features which need to be disentangled from the structural and biological effects.

VIRS based detection in combination with machine learning for mapping soil pollution.

Widespread soil contamination threatens living standards and weakens global efforts towards the Sustainable Development Goals (SDGs). Detailed soil mapping is needed to guide effective countermeasures and sustainable remediation operations. Here, we review visible and infrared reflectance spectroscopy (VIRS) based detection methods in combination with machine learning. To date, proximal, airborne and spaceborne carrier devices have been employed for soil contamination detection, allowing large areas to be covered at low cost and with minimal secondary environmental impact. In this way, soil contaminants can be monitored remotely, either directly or through correlation with soil components (e.g. Fe-oxides, soil organic matter, clay minerals). Observed vegetation reflectance spectra has also been proven an effective indicator for mapping soil pollution. Calibration models based on machine learning are used to interpret spectral data and predict soil contamination levels. The algorithms used for this include partial least squares regression, neural networks, and random forest. The processes underlying each of these approaches are outlined in this review. Finally, current challenges and future research directions are explored and discussed.

The influence of mineral content on spectral features of vine leaves

ABSTRACT The reflectance spectra of vegetation carry potentially useful information that can be used to determine chemical composition and discriminate between vegetation classes. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on leaves and branches of Vitis vinifera L. cv. Merlot and Cabernet Sauvignon from two vineyards in south Brazil. The vineyards had different geological origins but were subjected to the same management. The objectives were to detect spectral differences between the vineyards, and to correlate these differences to variations in foliar traits like the chemical composition of vine leaves. To that end, seven vine parcels were selected for reflectance measurements and chemical analyses (of eleven elements) of vine leaves, and correlations between reflectance and chemical composition were looked for. An initial investigation by discriminant analysis applied to reflectance data of leaves and branches and to grape varieties as well allowed for good separation between vineyards and varieties (> 90% accuracy). By further investigating the correlations between leaf chemical composition and reflectance along the wavelength domain covered by the measurements, we found several well-determined wavelengths with Pearson correlation coefficients r > 0.7. Concentrations of elements could be modelled up to 94% accuracy. These preliminary results, which have to be validated, suggest that variations in soil properties induce chemical differences in vine leaves that can be detected by reflectance measurements. Applications of this observation include the assessment of the chemical content of vine leaves by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.

COMSPECT: a compact model for green vegetation reflectance spectra in the 400–900 nm wavelength range

COMSPECT: a compact model for green vegetation reflectance spectra in the 400–900 nm wavelength range

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

ABSTRACTDue 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.

Wavelet-based coupling of leaf and canopy reflectance spectra to improve the estimation accuracy of foliar nitrogen concentration

The leaf or canopy reflectance spectra of vegetation have been widely employed in estimating foliar nitrogen (N) concentration; however, they alone may not actually reflect the spectral and detailed information at a sampling plot. In this study, the potential spectral details of Carex (C. cinerascens) at a plot scale were derived using discrete wavelet transform, in which a simple operation of addition was employed to combine the reconstructed leaf and canopy reflectance at the fourth decomposition level (named “leaf-canopy d4 reflectance”). Partial least squares regression (PLSR), successive projections algorithm-based multiple linear regression (SPA-MLR) and random forest regression (RFR) models with leaf, canopy and leaf-canopy d4 reflectance were established and validated for foliar N estimation, respectively. The results showed that the PLSR (R2CV=0.718, determination coefficient of cross-validation; R2Val=0.743, determination coefficient of independent validation; RPD=1.91, residual prediction deviation), SPA-MLR (R2CV=0.709, R2Val=0.747, RPD=1.97) and RFR (R2CV=0.714, R2Val=0.783, RPD=2.16) models with leaf-canopy d4 reflectance outperformed their corresponding models with leaf or canopy reflectance. We conclude that the wavelet-based coupling of leaf and canopy reflectance spectra has great potential in the accurate estimation of foliar N concentration. This proposed strategy helps to understand the spectral details of vegetation at a plot scale, providing the potential for improving the plot-based estimation of plant nutrients in grassland, precision agriculture or forestry.

Reflectance spectroscopy: a novel approach to better understand and monitor the impact of air pollution on Mediterranean plants.

The Mediterranean basin can be considered a hot spot not only in terms of climate change (CC) but also for air quality. Assessing the impact of CC and air pollution on ecosystem functions is a challenging task, and adequate monitoring techniques are needed. This paper summarizes the present knowledge on the use of reflectance spectroscopy for the evaluation of the effects of air pollution on plants. First, the history of this technique is outlined. Next, we describe the vegetation reflectance spectrum, how it can be scaled from leaf to landscape levels, what information it contains, and how it can be exploited to understand plant and ecosystem functions. Finally, we review the literature concerning this topic, with special attention to Mediterranean air pollutants, showing the increasing interest in this technique. The ability of spectroscopy to detect the influence of air pollution on plant function of all major and minor Mediterranean pollutants has been evaluated, and ozone and its interaction with other gases (carbon dioxide, nitrogen oxides, and sulfur dioxide) have been the most studied. In the recent years, novel air pollutants, such as particulate matter, nitrogen deposition, and heavy metals, have drawn attention. Although various vegetation types have been studied, few of these species are representative of the Mediterranean environment. Thus, major emphasis should be placed on using vegetation spectroscopy for better understanding and monitoring the impact of air pollution on Mediterranean plants in the CC era.

STRESS MONITORING OF MULBERRY PLANTS BY FINDING REP USING HYPERSPECTRAL DATA

Abstract. Stressed on crop can be monitored using different indices. Red Edge position is good estimator for stress monitoring. The red edge position (REP) is strongly correlated with foliar chlorophyll content. Strong chlorophyll absorption causes the abrupt change in the 680–800 nm region of reflectance spectra of green vegetation. The red edge consist the point of maximum slope between red and near-infrared wavelengths. REP can be used to recognize green zone of the observation area. The REP is present in spectra for vegetation recorded by remote sensing methods. REP is clearer and significant in hyper spectral data as hyper spectral consist of more and continuous bands data. In this paper experiments were carried out for mulberry crop using USGS EO-1 Hyperion data. Atmospheric corrected data is used for classification. Classification is carried out on small cluster of 14 field samples. Ground truth is verified and classified by comparing with Hyperion data. REP is different for different stressed condition of crops and shows healthy and diseased crop condition. Nutritional stresses, diseases, drought of plants are detected using REP. Stressed and healthy field of mulberry are estimated by calculating REP using maximum first derivative, linear interpolation, linear extrapolation method. Finally REP is compared using above methods. It is noticeable difference of REP for healthy and stressed crop. The research indicates that overall accuracy using maximum first derivative was 92.85 % and it was more compared to other methods. Linear extrapolation gives less accuracy compared to linear interpolation method.

Sunlight induced chlorophyll fluorescence in the near‐infrared spectral region in natural waters: Interpretation of the narrow reflectance peak around 761 nm

AbstractSunlight induced chlorophyll a fluorescence (SICF) can be used as a probe to estimate chlorophyll a concentrations (Chl) and infer phytoplankton physiology. SICF at ∼685 nm has been widely applied to studies of natural waters. SICF around 740 nm has been demonstrated to cause a narrow reflectance peak at ∼761 nm in the reflectance spectra of terrestrial vegetation. This narrow peak has also been observed in the reflectance spectra of natural waters, but its mechanism and applications have not yet been investigated and it has often been treated as measurement artifacts. In this study, we aimed to interpret this reflectance peak at ∼761 nm and discuss its potential applications for remote monitoring of natural waters. A derivative analysis of the spectral reflectance suggests that the 761 nm peak is due to SICF. It was also found that the fluorescence line height (FLH) at 761 nm significantly and linearly correlates with Chl. FLH(761 nm) showed a tighter relationship with Chl than the relationship between FLH(∼685 nm) and Chl mainly due to weaker perturbations by nonalgal materials around 761 nm. While it is not conclusive, a combination of FLH(761 nm) and FLH(∼685 nm) might have some potentials to discriminate cyanobacteria from other phytoplankton due to their different fluorescence responses at the two wavelengths. It was further found that reflectance spectra with a 5 nm spectral resolution are adequate to capture the spectral SICF feature at ∼761 nm. These preliminary results suggest that FLH(761 nm) need to be explored more for future applications in optically complex coastal and inland waters.

Estimating heavy metal concentrations in topsoil from vegetation reflectance spectra of Hyperion images: A case study of Yushu County, Qinghai, China.

In this study, we explored the feasibility of estimating the soil heavy metal concentrations using the hyperspectral satellite image. The concentration of As, Pb, Zn and Cd elements in 48 topsoil samples collected from the field in Yushu County of the Sanjiangyuan regions was measured in the laboratory. We then extracted 176 vegetation spectral reflectance bands of 48 soil samples as well as five vegetation indices from two Hyperion images. Following that, the partial least squares regression (PLSR) method was employed to estimate the soil heavy metal concentrations using the above two independent sets of Hyperion-derived variables, separately constructed the estimation model between the 176 vegetation spectral reflectance bands and the soil heavy metal concentrations (called the vegetation spectral reflectance-based estimation model), and between the five vegetation indices being used as the independent variable and the soil heavy metal concentrations (called synthetic vegetation index-based estimation model). Using RPD (the ratio of standard deviation from the 4 heavy metals measured values of the validation samples to RMSE) as the validation criteria, the RPDs of As and Pb concentrations from the two models were both less than 1.4, which suggested that both models were incapable of roughly estimating As and Pb concentrations; whereas the RPDs of Zn and Cd were 1.53, 1.46 and 1.46, 1.42, respectively, which implied that both models had the ability for rough estimation of Zn and Cd concentrations. Based on those results, the vegetation spectral-based estimation model was selected to obtain the spatial distribution map of Zn concentration in combination with the Hyperion image. The estimated Zn map showed that the zones with high Zn concentrations were distributed near the provincial road 308, national road 214 and towns, which could be influenced by human activities. Our study proved that the spectral reflectance of Hyperion image was useful in estimating the soil concentrations of Zn and Cd.

Extraction of REPs from leaves reflectance spectrum for estimation of chlorophyll content

Abstract: The red edge parameters which include red edge amplitude, red edge position, red edge area and the reflectance of red edge position can be used as an indicator of stress and senescence of vegetation. Among these parameters, the red edge position (REP) is the best one to estimate tomato chlorophyll content according to statistical analysis. The REP is defined as the point of maximum slope at the 680 ~ 740nm region in a vegetation reflectance spectrum. In this research, the 6 algorithms could be used to extract the REP, and the quantitative relationships between REP on various algorithms and leaf chlorophyll status were analyzed, and then the prediction models of the chlorophyll content for each REP extraction algorithm were developed using Linear regression, Logarithmic regression, Power regression, Exponential regression and Quadratic polynomial regression. The result showed that the Logarithmic model of the Linear Extrapolation had the best accuracy and reliability. The calibration R-Square was 0.62 and the validation R-Square was 0.77 and the root mean squared error (RMSE) was 8.36.

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2=0.95, RMSE=1.0%, n=56), fresh leaf (r2=0.79, RMSE=2.1%, n=56), and canopy (r2=0.78, RMSE=1.0%, n=13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).