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

Féret, Jean‐Baptiste; François, Christophe; Gitelson, Anatoly A.; Asner, Gregory P.; Barry, KM; Panigada, Cinzia; Richardson, Andrew D.; Jacquemoud, Stéphane

doi:10.1016/j.rse.2011.06.016

Cited by 278 publications

(180 citation statements)

References 42 publications

(66 reference statements)

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“…The selection of spectral bands by all techniques was consistent with spectral band selection for foliar chlorophyll estimation performed by PROSPECT-5 ( [58]). It is also consistent with the results of Feilhauer et al [59] using a multi-method ensemble that provides a robust selection of spectral bands related to biochemical traits obtained for foliar chlorophyll content retrieval.…”

Section: Optimal Spectral Samplingmentioning

confidence: 69%

Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

et al. 2017

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Abstract:Informative spectral bands for green leaf area index (LAI) estimation in two crops were identified and generic models for soybean and maize were developed and validated using spectral data taken at close range. The objective of this paper was to test developed models using Aqua and Terra MODIS, Landsat TM and ETM+, ENVISAT MERIS surface reflectance products, and simulated data of the recently-launched Sentinel 2 MSI and Sentinel 3 OLCI. Special emphasis was placed on testing generic models which require no re-parameterization for these species. Four techniques were investigated: support vector machines (SVM), neural network (NN), multiple linear regression (MLR), and vegetation indices (VI). For each technique two types of models were tested based on (a) reflectance data, taken at close range and resampled to simulate spectral bands of satellite sensors; and (b) surface reflectance satellite products. Both types of models were validated using MODIS, TM/ETM+, and MERIS data. MERIS was used as a prototype of OLCI Sentinel-3 data which allowed for assessment of the anticipated accuracy of OLCI. All models tested provided a robust and consistent selection of spectral bands related to green LAI in crops representing a wide range of biochemical and structural traits. The MERIS observations had the lowest errors (around 11%) compared to the remaining satellites with observational data. Sentinel 2 MSI and OLCI Sentinel 3 estimates, based on simulated data, had errors below 8%. However the accuracy of these models with actual MSI and OLCI surface reflectance products remains to be determined.

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Section: Optimal Spectral Samplingmentioning

confidence: 69%

Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

et al. 2017

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“…Better-performing indices developed for Car were capable of describing up to 64% of variability in leaf total Car content (e.g., (R800 − R530)/(R800 + R530) by Féret et al 2011). Despite being developed for Car, these indices were more strongly correlated with leaf chlorophyll content, particularly with Chl a. VIs originally developed for assessing carotenoid to chlorophyll ratio (Car/Chl) were relatively weakly correlated with Car/Chl ratio in our dataset.…”

Section: How Well Available Vis Can Predict Leaf Total Carotenoid Conmentioning

confidence: 99%

Generality of relationships between leaf pigment contents and spectral vegetation indices in Mallorca (Spain)

et al. 2017

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Vegetation indices are calculated from reflectance data of discrete spectral bands. The reflectance signal in the visible spectral range is dominated by the optical properties of photosynthetic pigments in plant leaves. Numerous spectral indices have been proposed for the estimation of leaf pigment contents, but the efficacy of different indices for prediction of pigment content and composition for species-rich communities is unknown. Assessing the ability of different vegetation indices to predict leaf pigment content we identify the most suitable spectral indices from an experimental dataset consisting of field-grown high light exposed leaves of 33 angiosperm species collected in two sites in Mallorca (Spain) with contrasting leaf anatomy and pigment composition. Leaf-level reflectance spectra were recorded over the wavelength range of 400 -900 nm and contents of chlorophyll a, chlorophyll b, total carotenoids, and anthocyanins were measured in 33 species from different plant functional types, covering a wide range of leaf structures and pigment content, fivefold to more than 10-fold for different traits. The best spectral region for estimation of leaf total chlorophyll content with least interference from carotenoids and anthocyanins was the beginning of near-infrared plateau well beyond 700 nm. Leaves of parallel-veined monocots and pinnate-veined dicots had different relationships between vegetation indices and pigments. We suggest that the nature and role of Bfar-redĉ hlorophylls which absorb light at longer wavelengths than 700 nm constitute a promising target for future remote sensing studies.

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“…As a consequence, a number of spectral indices were developed for foliar Chls estimation. Table 1 lists some of the chlorophyll indicators that have been examined by Vogelmann et al (1993), Elvidge and Chen (1995), Blackburn and Ferwerda (2008), Ustin et al (2009), Féret et al (2011), and Hunt et al (2013. Those indices integrate a couple of specific signatures of visible and near-infrared bands, for example, the reflectance at 445,550,680,700,705,710,720,750,780,800, 860 nm for foliar Chls estimation.…”

Section: Indexmentioning

confidence: 99%

“…Many studies indicated that the spectral characteristics of red edge (RE) and green peak (GP) are directly or indirectly correlated to the level of leaf chlorophyll (Horler et al, 1983;Curran et al, 1990;Filella and Peñuelas, 1994;Pinar and Curran, 1996;Jongschaap and Booij, 2004;Mutanga and Skidmore, 2007) and can provide a method to distinguish between water and nutrient stress (Estep and Carter, 2005); they should be helpful in the prediction of leaf chlorophyll concentration. However, little research has examined the effectiveness of remote sensing models in the estimation chlorophyll content of both fresh and water-stressed leaves.…”

Section: Indexmentioning

confidence: 99%

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

et al. 2015

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Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum.Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R 2 ). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer.The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (ρ 645 and ρ 455 ) provided Chls estimates with RMSE around 0.40-0.55 mg g −1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρ REP ) in regression models. An effective chlorophyll indicator with the form of (ρ 645 -ρ 455 ) / ρ REP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R 2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g −1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14 % of the mean absolute error.

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

Abstract: Panigada, Cinzia; and Jacquemoud, Stéphane, "Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling" (2011). Papers in Natural Resources. 311.

Cited by 278 publications

References 42 publications

Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

Generality of relationships between leaf pigment contents and spectral vegetation indices in Mallorca (Spain)

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

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