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1. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning

Author

Gabriel Hmimina, Albert Porcar-Castell, Sylvain Jay, G. Le Maire, Yann Nouvellon, Christophe Proisy, A. Cheraiet, Ryad Bendoula, Jérôme Chave, J.C. Oliveira, M.-J. Lefevre-Fonollosa, F. de Boissieu, Jean-Philippe Gastellu-Etchegorry, Daniel Berveiller, Twinkle Solanki, Jean-Baptiste Féret, Flávio Jorge Ponzoni, Kamel Soudani, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Universidade Estadual de Campinas = University of Campinas (UNICAMP), Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Ecologie Systématique et Evolution (ESE), Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Instituto Nacional de Pesquisas Espaciais (INPE), Institute for Atmospheric and Earth System Research (INAR), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Evolution et Diversité Biologique (EDB), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut Français de Pondichéry (IFP), Ministère de l'Europe et des Affaires étrangères (MEAE)-Centre National de la Recherche Scientifique (CNRS), Centre National d'Études Spatiales [Toulouse] (CNES), TOSCA program grant of the French Space Agency (CNES) (HyperTropik project), International Network for Terrestrial Research and Monitoring in the Arctic (INTERACT project), CAPES, EUCFLUX project of Forestry Science and Research Institute (IPEF), SOERE F-ORE-T, ANAEE-F, (CEBA: ANR-10-LABX-25-01), (ANAEE-France: ANR-11-INBS-0001), Ecofor, Allenvi, ANR-10-LABX-0025,CEBA,CEnter of the study of Biodiversity in Amazonia(2010), ANR-11-INBS-0001,ANAEE-FR,ANAEE-Services(2011), Institut National de la Recherche Agronomique (INRA)-Institut de Recherche pour le Développement (IRD)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), SEG-CEMUC, Université de Coimbra, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), University of Helsinki, Centre National de la Recherche Scientifique (CNRS)-Ministère de l'Europe et des Affaires étrangères (MEAE), ANR: 10-LABX-0025,CEBA,CEnter of the study of Biodiversity in Amazonia(2010), ANR: 11-INBS-0001,ANAEE-FR,ANAEE-Services(2011), University of Campinas [Campinas] (UNICAMP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Paris-Sud - Paris 11 (UP11), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and ANR-10-LABX-25-01/10-LABX-0025,CEBA,CEnter of the study of Biodiversity in Amazonia(2010)

Subjects
Support vector machine, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, Radiative transfer model, 02 engineering and technology, computer.software_genre, 01 natural sciences, Shortwave infrared, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], MODELING, Biomasse, apprentissage machine, OPTICAL PROPERTIES, ComputingMilieux_MISCELLANEOUS, Mathematics, Leaf spectroscopy, U10 - Informatique, mathématiques et statistiques, Indice de surface foliaire, Prévention des incendies, Geology, Regression analysis, VEGETAÇÃO, Biophysical properties, Informatique, Reflectivity, Propriété optique, MACHINE LEARNING, RISK MANAGEMENT, F40 - Écologie végétale, Spectroscopie infrarouge, Soil Science, Machine learning, fire hazards [EN], Atmospheric radiative transfer codes, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, LEAF, LMA, [INFO]Computer Science [cs], Computers in Earth Sciences, EWT, Modélisation environnementale, 0105 earth and related environmental sciences, Remote sensing, Leaf mass per area, Vegetation, Optimal estimation, business.industry, Inversion (meteorology), 15. Life on land, Végétation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 020801 environmental engineering, 13. Climate action, ECOSYSTEM, Artificial intelligence, Réduction des risques d'incendie, U30 - Méthodes de recherche, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, computer
Abstract

International audience; Leaf mass per area (LMA) and leaf equivalent water thickness (EWT) are key leaf functional traits providing information for many applications including ecosystem functioning modeling and fire risk management. In this paper, we investigate two common conclusions generally made for LMA and EWT estimation based on leaf optical properties in the near-infrared (NIR) and shortwave infrared (SWIR) domains: (1) physically-based approaches estimate EWT accurately and LMA poorly, while (2) statistically-based and machine learning (ML) methods provide accurate estimates of both LMA and EWT. Using six experimental datasets including broadleaf species samples of >150 species collected over tropical, temperate and boreal ecosystems, we compared the performances of a physically-based method (PROSPECT model inversion) and a ML algorithm (support vector machine regression, SVM) to infer EWT and LMA based on leaf reflectance and transmittance. We assessed several merit functions to invert PROSPECT based on iterative optimization and investigated the spectral domain to be used for optimal estimation of LMA and EWT. We also tested several strategies to select the training samples used by the SVM, in order to investigate the generalization ability of the derived regression models. We evidenced that using spectral information from 1700 to 2400 nm leads to strong improvement in the estimation of EWT and LMA when performing a PROSPECT inversion, decreasing the LMA and EWT estimation errors by 55% and 33%, respectively. The comparison of various sampling strategies for the training set used with SVM suggests that regression models show limited generalization ability, particularly when the regression model is applied on data fully independent from the training set. Finally, our results demonstrate that, when using an appropriate spectral domain, the PROSPECT inversion outperforms SVM trained with experimental data for the estimation of EWT and LMA. Thus we recommend that estimation of LMA and EWT based on leaf optical properties should be physically-based using inversion of reflectance and transmittance measurements on the 1700 to 2400 nm spectral range.

Published
2019

2. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments

Author

Roberta E. Martin, C. François, Stéphane Jacquemoud, Gregory P. Asner, Susan L. Ustin, Luc P.R. Bidel, Guerric Le Maire, Jean-Baptiste Féret, Anatoly A. Gitelson, Univ Paris Sud, UMR 8079, Lab Ecol Systemat & Evolut, F-91405 Orsay, France, Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA, University of Nebraska [Lincoln], University of Nebraska System, Univ Nebraska, Sch Nat Resource Sci, Ctr Adv Land Management Informat Technol, Lincoln, NE 68588 USA, Diversité et adaptation des plantes cultivées (UMR DIAPC), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), University of California [Davis] (UC Davis), University of California, Fonctionnement et pilotage des écosystèmes de plantations (UPR Ecosystèmes de plantations), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS), and ational remote sensing programs forfunding this research: INSU Programme National de TélédétectionSpatiale (PNTS), CNES Terre Océan Surfaces Continentales Atmosphère(TOSCA) program, NASA Earth Observing System and TerrestrialEcology programs, the Carnegie Institution of Washington, and theUniversity of California's CalSpace and USDA Hatch programs.

Subjects
Chlorophyll b, Modèle, PROSPECT, Chlorophyll a, Materials science, 010504 meteorology & atmospheric sciences, pigments, Caroténoïde, [SDU.ASTR.EP]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Earth and Planetary Astrophysics [astro-ph.EP], 0211 other engineering and technologies, Soil Science, F62 - Physiologie végétale - Croissance et développement, 02 engineering and technology, Photosynthetic pigment, Photosynthesis, 01 natural sciences, leaf optical properties, chemistry.chemical_compound, Transmittance, Chlorophylle, radiative transfer model, Computers in Earth Sciences, Absorption (electromagnetic radiation), 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Biological pigment, Radiation, [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], hyperspectral data, Geology, Pigment, chemistry, [SDU]Sciences of the Universe [physics], Chlorophyll, U30 - Méthodes de recherche, Propriété optique
Abstract

The PROSPECT leaf optical model has, to date, combined the effects of photosynthetic pigments, but a finer discrimination among the key pigments is important for physiological and ecological applications of remote sensing. Here we present a new calibration and validation of PROSPECT that separates plant pigment contributions to the visible spectrum using several comprehensive datasets containing hundreds of leaves collected in a wide range of ecosystem types. These data include leaf biochemical (chlorophyll a, chlorophyll b, carotenoids, water, and dry matter) and optical properties (directional–hemispherical reflectance and transmittance measured from 400 nm to 2450 nm). We first provide distinct in vivo specific absorption coefficients for each biochemical constituent and determine an average refractive index of the leaf interior. Then we invert the model on independent datasets to check the prediction of the biochemical content of intact leaves. The main result of this study is that the new chlorophyll and carotenoid specific absorption coefficients agree well with available in vitro absorption spectra, and that the new refractive index displays interesting spectral features in the visible, in accordance with physical principles. Moreover, we improve the chlorophyll estimation (RMSE = 9 µg/cm2) and obtain very encouraging results with carotenoids (RMSE = 3 µg/cm2). Reconstruction of reflectance and transmittance in the 400–2450 nm wavelength domain using PROSPECT is also excellent, with small errors and low to negligible biases. Improvements are particularly noticeable for leaves with low pigment content.

Published
2008

3. Estimation of inherent optical properties using in situ hyperspectral radiometer and MODIS data along the east coast of New Caledonia

Author

Hiroshi Murakami, Rüdiger Röttgers, Cécile Dupouy, Robert Frouin, Frouin, R.J. (ed.), Ebuchi, N. (ed.), Pan, D. (ed.), and Saino, T. (ed.)

Subjects
PHYTOPLANCTON, Radiometer, Backscatter, DONNEES SATELLITE, PARTICULE, Irradiance, TELEDETECTION SPATIALE, Hyperspectral imaging, IOPS, Solar irradiance, CALIOPE, CAMPAGNE OCEANOGRAPHIQUE, REFLECTANCE, Geography, MODIS, PROPRIETE OPTIQUE, Radiance, MATIERE ORGANIQUE DISSOUTE, Spectral resolution, Remote sensing
Abstract

Hyperspectral remote sensing reflectance (Rrs) was measured by a TriOS radiometer system along the East Coast of New Caledonia during the R/V Alis 03-13 October 2011 CALIOPE cruise. The TriOS system consists of radiance and irradiance sensors measuring in the spectral range 320-950 nm, at a spectral resolution of about 10 nm (sampled by every 3.3 nm), and within a 7-degree field-of-view for the radiance sensor. The method developed by Froidefond and Ouillon (2005) was used to determine Rrs, i.e., the radiance sensor was mounted on a small raft to measure upwelling radiance just below the surface, and Rrs was calculated by normalizing water-leaving radiance with downward solar irradiance measured on the ship deck. Inherent Optical Properties (IOPs), i.e., absorption coefficients of phytoplankton and detritus+dissolved substances (a ph and a dg , respectively), and particulate backscattering coefficient (b bp ) were estimated from the hyperspectral Rrs data by applying linear matrix inversion (Hoge and Lyon, 1996). The IOP inversion algorithm was adapted to MODIS data and applied to Level 1b imagery at 500 m resolution to demonstrate the feasibility of regular IOP monitoring from space in the study area. Local characteristics of the IOP spectra were used for the candidate spectra in the algorithm. The estimated MODIS Rrs and IOPs were evaluated using TriOS Rrs and in-situ IOP measurements obtained concomitantly during the cruise.

Published
2012

4. Edible Wheat Gluten Films: Influence of the Main Process Variables on Film Properties using Response Surface Methodology

Author

Stéphane Guilbert, Jean-Louis Cuq, Nathalie Gontard, Université de Montpellier (UM), Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Matériau de conditionnement, Wheat gluten, 02 engineering and technology, chemistry.chemical_compound, 0404 agricultural biotechnology, Q02 - Traitement et conservation des produits alimentaires, blé, Organic chemistry, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Response surface methodology, Solubility, ComputingMilieux_MISCELLANEOUS, Propriété physicochimique, chemistry.chemical_classification, Perméabilité, Ethanol, Aqueous solution, 04 agricultural and veterinary sciences, 021001 nanoscience & nanotechnology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 040401 food science, Gluten, méthode, [CHIM.POLY]Chemical Sciences/Polymers, Q80 - Conditionnement, chemistry, Chemical engineering, Permeability (electromagnetism), Propriété mécanique, Scientific method, Propriété optique, 0210 nano-technology, Food Science
Abstract

An edible wheat gluten film was developed and effects of gluten concentration, ethanol concentration (ET) and pH of the film-forming solution on various film properties were evaluated using Response Surface Methodology. pH and ethanol concentration had strong interactive effects on film opacity, water solubility and water vapor permeability. A simultaneous variation of ethanol concentration and pH between 32.5% ET, pH 4 and 45% ET, pH 2 resulted in homogeneous and transparent film with relatively low water solubility. The lowest water vapor permeability would be expected with 20% ethanol concentration and pH 6. Mechanical properties were mainly affected by gluten concentration and pH. The most resistant film was obtained at high gluten concentration (12.5%) and pH 5.

Published
1992

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