Estimation of Canopy Photosynthetic and Nonphotosynthetic Components from Spectral Transmittance (original) (raw)
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Optical–Biophysical Relationships of Vegetation Spectra without Background Contamination
Remote Sensing of Environment, 2000
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Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties
Remote Sensing of Environment, 1994
T~ spectral variation of camgpy reflectance is mostly governed by the optical properties of the elements such as the leaves. Since leaf intrinsic scattering properties show very little spectral variation, leaf optical properties are related to their absorption properties. Spectral analogies are thus observed between two wavelengths for which the optical properties (absorption, reflectance, or transmittance) of the elements are similar. The red edge for green plants shows the full range of variation of leaf optical properties. The relationship between canopy reflectance and leaf reflectance measured concurrently at the red edge over sugar beet canopies was thus used to simulate canopy reflectance over the whole spectral domain from leaf reflectance spectra measured over the whole spectral domain. The results show that the spectral analogies found allows accurate reconstruction of canopy reflectance spectra. Explicit assumptions about the very low spectral variation of leaf intrinsic scattering properties are thus indirectly justified. The sensitivity of canopy reflectance (Pc)
Plant Reflected Spectra Depending on Biological Characteristics and Growth Conditions
Proceedings of the 7th International Scientific Conference Rural Development 2015, 2015
Sustainable and economically based forestry needs modern inventory and monitoring techniques. One of the most common technologies for identification of forest tree species and monitoring of forest growth conditions is the hyperspectral remote sensing. This technology gives an opportunity to economize human resources and time for data collecting and processing. The spectral behaviour of plant leaves depends on number of factors, including environmental background. The aim of this study was to assess the tree reflectance spectra in relation to the growth conditions to take into account potential differences for increasing precision of species identification in Latvian forests and for estimating of forest growth conditions. Remote sensing data were obtained using a specialized aircraft (Pilatus PC-6), which is equipped with a high-performance airborne VNIR pushbroom hyperspectral system (AisaEAGLE). The study area was flown at 1000 m altitude. Data was recorded in the 400-970 nm spectral range, spectral resolution was 3.3 nm, ground resolution 0.5 m. Data processing consisted of manually selecting trees with a recognizable tree crowns in the airborne images. Tree centres were adjusted by putting them in the accurate position according to the situation in aerial photography. All trees with a diameter at breast height DBH of more than 5 cm were measured and for each tree coordinates, its species, height, DBH, crown width and length were recorded. Differentially corrected Global Positioning System measurements were used to determine the position of each plot centre. Data from different hyperspectral bands were compared using ANOVA at confidence level 95 %. Four species: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst), silver birch (Betula pendula Roth), and European aspen (Populus tremula L.)were examined in distinct forest site types. The spectral response of studied species was 1) different between species and 2) different between site types within each species, correlating with soil fertility gradient and soil moisture gradient. Differences between species occurred most in the intensity of reflected electromagnetic radiation rather than distinctive locations of maximums or minimums in spectrum curve, and near infrared (NIR) region of spectrum showed more differences between species than visible light zone. Most informative wavebands for distinguishing differences between site types were 805 nm and 644 nm.
Remote Sensing, 2021
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. S...
Journal of Geophysical Research, 1992
Leaves of the dominant grass species of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site reflect and transmit radiation in a similar manner to other healthy green leaves. Visible reflectance factors (RFs) and transmittance factors (TFs) were lower for older leaves than younger leaves except during senescence, when RF and TF values were higher. Near-infrared (NIR) RF values increased and TF values decreased with leaf age, with the reverse occurring as the leaf underwent senescence. Leaf optical properties were not found to be dependent on leaf water potential in the range from-0.5 to-3.0 MPa. Canopy bidirectional reflectance factor (BRF) values generally increased with increasing view zenith angle (Ov). Maximum values were in the backscatter direction, whereas BRF values in the visible region were lowest at oblique off-nadir Ov in the forward scatter direction and at or near nadir in the NIR region. Solar principal plane BRF values varied most at large solar zenith angles (Os). Visible and mid-infrared canopy BRF values decreased and NIR BRF values increased with leaf area index (LAI). Soil BRF distributions in the solar principal plane varied slightly with Os and Ov and varied considerably for wet and dry surfaces. Spectral vegetation indices (SVIs) varied with Os and Ov; values were lowest in the backscatter direction and highest in the forward scatter direction. The fraction of absorbed photosynthetically active radiation (APAR) increased with increasing Os. APAR had a strong linear relationship to nadir-derived SVI values but not to oblique off-nadir-derived SVI values. The relatively small dependence of off-nadir SVI values on Os should allow daily APAR values to be estimated from measurements made at any time of the day. Factors which affect reflectance from vegetated surfaces and contribute to the non-Lambertian nature of these surfaces include (1) spectral properties of canopy elements and substrate; (2) the canopy architecture (that is, leaf area index (3) illumination and viewing directions [Norman et al., 1985]. Canopy architecture plays a vital role in determining the BRFs from a vegetative canopy. Leaves are oriented at a variety of inclination angles, thereby varying effective illumination and viewing angles. The result is a complex pattern of reflected and transmitted radiation. Canopy architecture can change due to wind, heliotropism, and water stress.
Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices
Remote Sensing of Environment, 1996
Litter is frequently present within vegetation canopies and thus contributes to the overall spectral response of a canopy. Consequently, litter will affect spectral indices designed to be sensitive to green vegetation, soil brightness or other features. The main objectives of the current research were to 1) evaluate the spectral properties of green vegetation and litter and 2) quantify the effect of standing litter on the performance of spectral indices. The SAIL (scattering by arbitrarily inclined leaves) model was used to generate canopy reflectance "mixtures" and to estimate fractions of absorbed photosynthetically active radiation (fAPAR) with varying leaf area index (LAI), soil background, combinations of vegetation component spectral properties, and one or two horizontal vegetation layers. Spectral measurements of different bare soils and mature green and senescent leaves of representative plant species at the HAPEX-Sahel (Hydrological Atmospheric Pilot Experiment) study sites were used as input. The normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SA VI), and the modified NDVI (MNDVI) and mixture model spectral indices were selected to evaluate their performance with respect to standing litter and green vegetation mixtures. Spectral reflectance signatures of leaf litter varied significantly, but strongly resembled soil spectral characteristics. The biophysical parameters (LAI, fAPAR), derived from spectral vegetation indices, tended to be overestimated for randomly distributed, sparse green and litter vegetation cover mixtures, and underestimated for randomly distributed dense green and litter vegetation cover mixtures. All spectral indices and their biophysical interpretation were significantly altered by variability in 1) green leaf, leaf litter, and bark optical properties, 2) the amount and position of standing leaf litter, 3) leaf angle distribution, and 4) soil background. The NDVI response to these variables was inconsistent, and was the most affected by litter. The spectral mixture model indices, designed to be sensitive to litter, were shown to be promising for the identification of litter present among different ecosystems.
Spectral Data for Plant Chlorophyll Assessment
An increasing role in plant phytodiagnostics becomes to play different spectrometric techniques used as a part of remote sensing applications. The radiation behavior of land covers and the spectral response to changing conditions lies at the root of these studies. The visible and near infrared (400 -900 nm) measurements have proved abilities in vegetation monitoring. The reason is that this wavelength range reveals significant sensitivity to plant biophysical properties. The information is carried by the specific vegetation spectral characteristics which depend on such plant parameters as chlorophyll content, biomass amount, leaf area, etc. These parameters are associated with plant devel-opment and stress factors being closely related to vegetation physiological state. In our study, multispectral data of reflected, transmitted and emitted irradiance have been used to show the possibility for plant chlorophyll assess-ment. Different methods such as vegetation indices, red edge analy...
Remote Sensing of Environment, 2003
Because of the high water content of vegetation, water absorption features dominate spectral reflectance of vegetation in the near-infrared region of the spectrum. In comparison to indices based on chlorophyll absorption features (such as the normalized difference vegetation index (NDVI)), indices based on the water absorption bands are expected to ''see'' more deeply into thick canopies and have a preferential sensitivity to thin as opposed to thick tissues. These predictions are based on the much lower absorption coefficients for water in the short wavelength water bands as compared to chlorophyll. Thus, the water bands may have advantages over NDVI for remote sensing of photosynthetic tissues. Previous studies have primarily related water band indices (WI) to leaf area index (LAI). Here we expand the definition of photosynthetic tissues to include thin green stems and fruits and measure a wide range of species to determine the influence of variable tissue morphologies and canopy structures on these relationships. As expected, indices based on reflectance in the water absorption bands in the near infrared were best correlated with the water content of thin tissues (less than 0.5-cm thickness). The choice of wavelength for a water index was much more important for thick than for thin canopies, and the best wavelengths were those where water absorptance was weak to moderate. We identified three wavelength regions (950-970, 1150-1260 and 1520-1540 nm) that produced the best overall correlations with water content. Comparison of these wavelength regions with the atmospheric ''windows'' where water vapor absorption is minimal suggests that the 1150-1260 and 1520-1540 nm regions would be the best wavelengths for satellite remote sensing of water content. We also developed and tested a new Canopy Structure Index (CSI) that combines the low absorptance water bands with the simple ratio vegetation index (SR) to produce an index with a wider range of sensitivity to photosynthetic tissue area at all canopy thicknesses. CSI was better than either WI or SR alone for prediction of total area of photosynthetic tissues. However, SR was best for prediction of leaf area when other green tissues were excluded. All of these relationships showed good generality across a wide range of species and functional types.
Review of spectral vegetation indices and methods for estimation of crop biophysical variables
Aerospace Research in Bulgaria, 2017
In present article a brief overview is presented on spectral vegetation indices and methods for estimation of crop main biophysical variables and their proxies. The main VIs used in estimation of nitrogen and chlorophyll, biomass, LAI and fAPAR, fCover, and photosynthesis are summarized. Biophysical variables and vegetation indices A number of techniques have evolved to derive the biophysical variables of vegetation using remote sensing data; these can be grouped into three broad categories: the inversion of radiative transfer models [39], machine learning (for example neural networks) [4] and the use of vegetation Indices. There are generally few ways of deriving the biophysical estimates using empirical or semi-empirical relationships: 1) single regression; 2) stepwise linear regression; 3) partial least squares (PLS) regression; 4) artificial neural networks [12]. Methods based on vegetation indices (VIs) have the benefit of being computationally simple while they are generally less site specific and more universally applicable than the other methods. The performance of the different indices and selected "optimal" wavebands depends on vegetation and land cover type, the variables to be retrieved, sun/view geometry to name but a few [12]. Satellite spectral data has the potential to measure the reflected radiation from many plants, thus making assessment of biophysical variables feasible on canopy level. The regression models relate in situ measurements and VIs. The VIs are mathematical transformations of the original spectral reflectance that are designed to reduce the additive and multiplicative errors associated with atmospheric effects, solar illumination, soil background effects, and sensor viewing geometry [29].
Crop canopy spectral reflectance
International Journal of Remote Sensing, 1988
A simple model based on the Kubelka-Munk theory of scattering is used to describe a relationship between reflection ratios (p I / p,) in two contrasting wavebands and fractional light absorption by a canopy. The analysis reveals that whilst the relationship betweenratio and vegetation is curvilinear, it varies linearly with the fraction of photosynthetically active radiation absorbed by vegetation.