Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands

Baret, Frédéric; Jacquemoud, Stéphane; Guyot, G.; Leprieur, C.

doi:10.1016/0034-4257(92)90073-s

Cited by 197 publications

(80 citation statements)

References 23 publications

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“…Different methods were designed to extract the desired wavelength position [40,60]. The theoretical analysis of [61] demonstrated that detecting inflection points (and, thus, spectral shifts) in vegetation spectra reduce the negative impacts of variations in soil brightness, irradiance conditions and atmospheric perturbations. Despite this long-lasting knowledge about the prospects of this indicator, currently, no commercial software has implemented suitable algorithms.…”

Section: Shape Descriptors: Inflection Points and Spectral Shiftsmentioning

confidence: 99%

ARCTIS — A MATLAB® Toolbox for Archaeological Imaging Spectroscopy

et al. 2014

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Imaging spectroscopy acquires imagery in hundreds or more narrow contiguous spectral bands. This offers unprecedented information for archaeological research. To extract the maximum of useful archaeological information from it, however, a number of problems have to be solved. Major problems relate to data redundancy and the visualization of the large amount of data. This makes data mining approaches necessary, as well as efficient data visualization tools. Additional problems relate to data quality. Indeed, the upwelling electromagnetic radiation is recorded in small spectral bands that are only about ten nanometers wide. The signal received by the sensor is, thus quite low compared to sensor noise and possible atmospheric perturbations. The often small, instantaneous field of view (IFOV)-essential for archaeologically relevant imaging spectrometer datasets-further limits the useful signal stemming from the ground. The combination of both effects makes radiometric smoothing techniques mandatory. The present study details the functionality of a MATLAB ® -based toolbox, called ARCTIS (ARChaeological Toolbox for Imaging Spectroscopy), for filtering, enhancing, analyzing, and visualizing imaging spectrometer datasets. The toolbox addresses the above-mentioned problems. Its 8618Graphical User Interface (GUI) is designed to allow non-experts in remote sensing to extract a wealth of information from imaging spectroscopy for archaeological research. ARCTIS will be released under creative commons license, free of charge, via website (http://luftbildarchiv.univie.ac.at).

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Section: Shape Descriptors: Inflection Points and Spectral Shiftsmentioning

confidence: 99%

ARCTIS — A MATLAB® Toolbox for Archaeological Imaging Spectroscopy

et al. 2014

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“…PROSAIL (Baret et al 1992;Jacquemoud et al 2009) is one of the most widely used models, especially because it offers a good compromise between realism and simplicity (Verger et al, 2014) and because it is freely available to the community. PROSAIL simulates the canopy reflectance by combining the PROSPECT (Leaf Optical Properties Spectra) model of leaf optical properties (Jacquemoud and Baret 1990) and the SAIL (Scattering by Arbitrarily Inclined Leaves) canopy reflectance model (Verhoef 1984;Verhoef 1985).…”

Section: Introductionmentioning

confidence: 99%

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Jay

Maupas

Bendoula³

et al. 2017

Field Crops Research

198

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Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications.Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content ( ≤ 10 %). In addition, VIs show a great potential to retrieve canopy nitrogen content ( = 10 %). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs ( = 17 %). As expectedAuthor-produced version of the article published in Field Crops Research, 2017, N°210, p.33-46. The original publication is available at http://www.sciencedirect.com Doi: 10.1016Doi: 10. /j.fcr.2017 when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively.Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAVembedded multispectral cameras, with which only a few wavebands are available.

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“…On the other hand, hybrid methods couple statistical with physically-based approaches inverting a database generated by an RTM [16][17][18]. For example, CYCLOPES global products [19] were derived inverting the PROSAIL radiative transfer model [20] using neural networks, while the global EUMETSAT LAI/FAPAR/FVC products are being produced inverting PROSAIL with multi-output Gaussian process regression from the EUMETSAT Polar System (EPS) [21]. The use of RTMs implies modeling leaf and canopy structural and biochemical parameters.…”

Section: Introductionmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R 2 = 0.42 and R 2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.

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Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands

Cited by 197 publications

References 23 publications

ARCTIS — A MATLAB® Toolbox for Archaeological Imaging Spectroscopy

ARCTIS — A MATLAB® Toolbox for Archaeological Imaging Spectroscopy

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Global Estimation of Biophysical Variables from Google Earth Engine Platform

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