A broad-band leaf chlorophyll vegetation index at the canopy scale (original) (raw)
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Precision Agriculture, 2011
A comparison of the sensitivity of several broad-and narrow-band vegetation indices (VIs) to leaf chlorophyll content in planophile crop canopies is addressed by the analysis of a large synthetic dataset. Broad-band indices included classical slope-based VIs (i.e. NDVI-normalized difference VI and SR-simple ratio) and some indices incorporating green reflectance (i.e. Green NDVI, NIR/green ratio and the newly proposed CVI-chlorophyll vegetation index), whereas narrow-band indices included those specifically proposed to estimate leaf chlorophyll at the canopy scale (i.e. MCARI-modified chlorophyll absorption reflectance index, TCARI-transformed CARI, TCARI/OSAVI ratio-TCARI/optimized soil adjusted VI and REIP-red edge inflection position). Synthetic data were obtained from the coupled PROSPECT ? SAILH leaf and canopy reflectance models in the direct mode. In addition to traditional regression-based statistics (coefficient of determination and root mean square error, RMSE), changes in sensitivity of a VI over the range of chlorophyll content were analyzed using a sensitivity function. The broad-band chlorophyll vegetation index outperformed the other VIs considered as a leaf chlorophyll estimator at the canopy scale, with the exception of the TCARI/OSAVI ratio for some soil conditions.
An optimized broad-band leaf chlorophyll estimator
2010
A comparison of the sensitivity of several broad-band and narrow-band vegetation indices (VI) to leaf chlorophyll concentration is conduced by the analysis of a large synthetic dataset obtained by using in the direct mode the coupled PROSPECT+SAILH leaf and canopy reflectance models. The newly proposed broad-band OCVI (Optimized Chlorophyll Vegetation Index) outperformed as a leaf chlorophyll estimator at the canopy scale both broad-band (i.e., Green NDVI, Green Simple Ratio) and narrow-band VI (i.e.: TCARI - Transformed Chlorophyll Absorption in Reflectance Index, TCARI/OSAVI ratio - TCARI/Optimized Soil Adjusted VI - and REIP, Red Edge Inflection Position), specifically proposed as leaf chlorophyll estimators, with the exception of the TCARI/OSAVI ratio for some soil/solar elevation conditions. Changes in sensitivity of a VI over the range of chlorophyll concentration are analysed, in addition to traditional regression-based statistics, by using a sensitivity function obtained acc...
International Journal of Remote Sensing, 1992
The canopy radiation model EXTRAD was used to quantify the accuracy of Leaf Area Index (LAI) estimations from Vegetation Indices (VIs), derived from green and infra-red crop reflectance. The VIs were the infra-red/green (IR/GR) ratio, the Normalised Difference Vegetation Index NDVI, the Perpendicular Vegetation Index PVI, and the Weighted Difference Vegetation Index WDVI. The accuracy of LAI estimation was calculated in relation to variation in leaf green and infra-red colour, leaf angle distribution, soil background and illumination conditions. The theoretical calculations were supported with a field experiment on sugar beet. Variation in illumination conditions and soil background gave relatively small estimation errors with all four VIs. The largest estimation errors resulted from variation in leaf colour and leaf angle distribution. With variation in green leaf colour, the estimation errors were lowest with the WDVI. With variation in leaf angle distribution, the errors were lowest with the IR/GR ratio and the NDVI. In practice, the magnitude of the error in LAI estimation will depend mostly on the magnitude and combination of occurring variation in leaf colour and leaf angle distribution. In an average of 100 random combinations of disturbing conditions, and in a field experiment with sugar beet, the absolute estimation errors ranged between 0.1 (and less) for 0<LAI<1 and 0.35 for 3<LAI<5.
Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops
Remote Sensing of Environment, 2004
An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R 750 /R 710 and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies. D 2004 Elsevier Inc. All rights reserved.
Remote Sensing of Environment, 2004
A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]
International Journal of Applied Earth Observation and Geoinformation - INT J APPL EARTH OBS GEOINF, 2010
The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm2, similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.
Remote Sensing of Environment, 2011
Many algorithms have been developed for the remote estimation of biophysical characteristics of vegetation, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. However, the most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances, their applicability is specific to species, vegetation types or local conditions. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the green leaf area index (Green LAI) of two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. Among the indices tested, the chlorophyll Indices (the CI Green , the CI Red-edge and the MERIS Terrestrial Chlorophyll Index, MTCI) exhibited strong and significant linear relationships with Green LAI, and thus were sensitive across the entire range of Green LAI evaluated (i.e., 0.0 to more than 6.0 m 2 /m 2). However, the CI Red-edge was the only index insensitive to crop type and produced the most accurate estimations of Green LAI in both crops (RMSE = 0.577 m 2 /m 2). These results were obtained using data acquired with close range sensors (i.e., field spectroradiometers mounted 6 m above the canopy) and an aircraft-mounted hyperspectral imaging spectroradiometer (AISA). As the CI Red-edge also exhibited low sensitivity to soil background effects, it constitutes a simple, yet robust tool for the remote and synoptic estimation of Green LAI. Algorithms based on this index may not require re-parameterization when applied to crops with different canopy architectures and leaf structures, but further studies are required for assessing its applicability in other vegetation types (e.g., forests, grasslands).
Remote Sensing of Environment, 2008
This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, µg cm leaf − 2 ), sun leaf mass per area (LMA, g dry matter m leaf − 2 ), canopy leaf area index (LAI, m 2 leaf m soil − 2 ) and leaf canopy biomass (B leaf , g dry matter m soil − 2 ). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 µg cm − 2 for CHL, 9.1 g m − 2 for LMA, 1.7 m 2 m − 2 for LAI and 50.6 g m − 2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: ND chl = (ρ 925 − ρ 710 )/(ρ 925 + ρ 710 ), for leaf mass per area: ND LMA = (ρ 2260 − ρ 1490 )/(ρ 2260 + ρ 1490 ), for leaf area index: D LAI = ρ 1725 − ρ 970 , and for canopy leaf biomass: ND Bleaf = (ρ 2160 − ρ 1540 )/(ρ 2160 + ρ 1540 ).
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].
Remote Sensing of …, 2008
This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, µg cmleaf− 2), sun leaf mass per area (LMA, gdry matter mleaf− 2), canopy leaf area index (LAI, m2leaf msoil− 2) and leaf canopy biomass (Bleaf, gdry matter msoil− 2). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 µg cm− 2 for CHL, 9.1 g m− 2 for LMA, 1.7 m2 m− 2 for LAI and 50.6 g m− 2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: NDchl = (ρ925 − ρ710)/(ρ925 + ρ710), for leaf mass per area: NDLMA = (ρ2260 − ρ1490)/(ρ2260 + ρ1490), for leaf area index: DLAI = ρ1725 − ρ970, and for canopy leaf biomass: NDBleaf = (ρ2160 − ρ1540)/(ρ2160 + ρ1540).