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1. A Physical Method for Optical Characterization of Pollution in Industrial Wastewater Ponds Using Imaging Spectroscopy

Author

Louis Zaugg, Rodolphe Marion, Malik Chami, Xavier Briottet, and Laure Roupioz

Subjects
Imaging spectroscopy, inversion method, optical characterization, water pollution, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Investigating the application of remote sensing to water pollution in industrial ponds is of great interest for rapid and cost-effective pollution monitoring. This article presents a method to detect pollutants and map their spatial distribution in industrial ponds using the water inherent optical properties (IOPs), namely the absorption and backscattering coefficients, derived from imaging spectroscopy data. The IOPs of industrial water pollutants remain poorly known. Current remote sensing methods are site-specific and require in situ measurements to calibrate empirically based models. Here, a generic approach is proposed based on the semianalytical radiative transfer model adapted to take into account both the absorption and backscattering coefficients of pollutant particles. The model is then inverted using an alternating multipixel method, named industrial wastewater optical characterization (IWOC), to map the spatial distribution of the pollutants. The performances of IWOC are evaluated using noise-free and noisy simulated datasets for an absorption-dominated water case and a backscattering-dominated water case. The water reflectance spectra (Rrs) for noise-free synthetic datasets are satisfactorily retrieved by the IWOC method. The optical properties of the pollutants are also well retrieved, with maximum root mean square error (RMSE) values of 2.43 × 10−3 m−1 for the absorption-dominated case and fairly zero for the backscattering-dominated case. A sensitivity study shows that the impact of noise is the highest on the estimates of the spectral slope exponent of the backscattering coefficient. The performances of the IWOC method are also examined through hyperspectral airborne images acquired over relevant study areas. The reflectance Rrs is well retrieved with RMSE values ranging from 7.5 × 10−5 sr−1 to 5.82 × 10−4 sr−1. The a priori knowledge of the properties of the study areas is consistent with the spatial distribution of the effluents within the ponds as derived from the remote sensing observations. The approach conducted in this article is a first step toward a generic inversion method for the optical characterization of pollution sources in water, which could further lead to an operational method.

Published
2024
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2. WaRM: A Roof Material Spectral Library for Wallonia, Belgium

Author

Coraline Wyard, Rodolphe Marion, and Eric Hallot

Subjects
roof materials, urban materials, spectral library, spectrometry, hyperspectral data, remote sensing, Bibliography. Library science. Information resources
Abstract

The exploitation of urban-material spectral properties is of increasing importance for a broad range of applications, such as urban climate-change modeling and mitigation or specific/dangerous roof-material detection and inventory. A new spectral library dedicated to the detection of roof material was created to reflect the regional diversity of materials employed in Wallonia, Belgium. The Walloon Roof Material (WaRM) spectral library accounts for 26 roof material spectra in the spectral range 350–2500 nm. Spectra were acquired using an ASD FieldSpec3 Hi-Res spectrometer in laboratory conditions, using a spectral sampling interval of 1 nm. The analysis of the spectra shows that spectral signatures are strongly influenced by the color of the roof materials, at least in the VIS spectral range. The SWIR spectral range is in general more relevant to distinguishing the different types of material. Exceptions are the similar properties and very close spectra of several black materials, meaning that their spectral signatures are not sufficiently different to distinguish them from each other. Although building materials can vary regionally due to different available construction materials, the WaRM spectral library can certainly be used for wider applications; Wallonia has always been strongly connected to the surrounding regions and has always encountered climatic conditions similar to all of Northwest Europe.

Published
2023
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3. Improved Deconvolution of Mineral Reflectance Spectra

Author

Ronan Rialland, Charles Soussen, Rodolphe Marion, and Veronique Carrere

Subjects
AGM procedure, EGO model, HySpex, hyperspectral images, mineral reflectance spectra, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

Reflectance spectroscopy is a widely used technique for mineral identification and characterization. Since modern airborne and satellite-borne sensors yield an increasing number of hyperspectral data, it is crucial to develop unsupervised methods to retrieve relevant spectral features from reflectance spectra. Spectral deconvolution aims to decompose a reflectance spectrum as a sum of a continuum modeling its overall shape and some absorption features. We present a flexible and automatic method able to deal with various minerals. The approach is based on a physical model and allows us to include noise statistics. It consists of three successive steps: first, continuum pre-estimation based on nonlinear least-squares; second, pre-estimation of absorption features using a greedy algorithm; third, refinement of the continuum and absorption estimates. The procedure is first validated on synthetic spectra, including a sensitivity study to instrumental noise and a comparison to other approaches. Then, it is tested on various laboratory spectra. In most cases, absorption positions are recovered with an accuracy lower than 5 nm, enabling mineral identification. Finally, the proposed method is assessed using hyperspectral images of quarries acquired during a dedicated airborne campaign. Minerals such as calcite and gypsum are accurately identified based on their diagnostic absorption features, including when they are in a mixture. Small changes in the shape of the kaolinite doublet are also detected and could be related to crystallinity or mixtures with other minerals such as gibbsite. The potential of the method to produce mineral maps is also demonstrated.

Published
2021
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4. Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site

Author

Nicolas Nesme, Rodolphe Marion, Olivier Lezeaux, Stéphanie Doz, Claude Camy-Peyret, and Pierre-Yves Foucher

Subjects
methane, industrial plume, PRISMA satellite, concentration, flow rate, Science
Abstract

Methane (CH4) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open up daily temporal monitoring of industrial methane sources at a spatial resolution of 30 m. Here, we developed the Characterization of Effluents Leakages in Industrial Environment (CELINE) code to inverse images of the Korpezhe industrial site. In this code, the in-Scene Background Radiance (ISBR) method was combined with a standard Optimal Estimation (OE) approach. The ISBR-OE method avoids the use of a complete and time-consuming radiative transfer model. The ISBR-OEM developed here overcomes the underestimation issues of the linear method (LM) used in the literature for high concentration plumes and controls a posteriori uncertainty. For the Korpezhe site, using the ISBR-OEM instead of the LM -retrieved CH4 concentration map led to a bias correction on CH4 mass from 4 to 16% depending on the source strength. The most important CH4 source has an estimated flow rate ranging from 0.36 ± 0.3 kg·s−1 to 4 ± 1.76 kg·s−1 on nine dates. These local and variable sources contribute to the CH4 budget and can better constrain climate change models.

Published
2021
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5. Mineral Mapping Using the Automatized Gaussian Model (AGM)—Application to Two Industrial French Sites at Gardanne and Thann

Author

Rodolphe Marion and Véronique Carrère

Subjects
mineral mapping, spectral deconvolution, AGM, industrial sites, Science
Abstract

The identification and mapping of the mineral composition of by-products and residues on industrial sites is a topic of growing interest because it may provide information on plant-processing activities and their impact on the surrounding environment. Imaging spectroscopy can provide such information based on the spectral signatures of soil mineral markers. In this study, we use the automatized Gaussian model (AGM), an automated, physically based method relying on spectral deconvolution. Originally developed for the short-wavelength infrared (SWIR) range, it has been extended to include information from the visible and near-infrared (VNIR) range to take iron oxides/hydroxides into account. We present the results of its application to two French industrial sites: (i) the Altéo Environnement site in Gardanne, southern France, dedicated to the extraction of alumina from bauxite; and (ii) the Millennium Inorganic Chemicals site in Thann, eastern France, which produces titanium dioxide from ilmenite and rutile, and its associated Séché Éco Services site used to neutralize the resulting effluents, producing gypsum. HySpex hyperspectral images were acquired over Gardanne in September 2013 and an APEX image was acquired over Thann in June 2013. In both cases, reflectance spectra were measured and samples were collected in the field and analyzed for mineralogical and chemical composition. When applying the AGM to the images, both in the VNIR and SWIR ranges, we successfully identified and mapped minerals of interest characteristic of each site: bauxite, Bauxaline® and alumina for Gardanne; and red and white gypsum and calcite for Thann. Identifications and maps were consistent with in situ measurements.

Published
2018
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6. Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site

Author

Foucher, Nicolas Nesme, Rodolphe Marion, Olivier Lezeaux, Stéphanie Doz, Claude Camy-Peyret, and Pierre-Yves

Subjects
methane, industrial plume, PRISMA satellite, concentration, flow rate
Abstract

Methane (CH4) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open up daily temporal monitoring of industrial methane sources at a spatial resolution of 30 m. Here, we developed the Characterization of Effluents Leakages in Industrial Environment (CELINE) code to inverse images of the Korpezhe industrial site. In this code, the in-Scene Background Radiance (ISBR) method was combined with a standard Optimal Estimation (OE) approach. The ISBR-OE method avoids the use of a complete and time-consuming radiative transfer model. The ISBR-OEM developed here overcomes the underestimation issues of the linear method (LM) used in the literature for high concentration plumes and controls a posteriori uncertainty. For the Korpezhe site, using the ISBR-OEM instead of the LM -retrieved CH4 concentration map led to a bias correction on CH4 mass from 4 to 16% depending on the source strength. The most important CH4 source has an estimated flow rate ranging from 0.36 ± 0.3 kg·s−1 to 4 ± 1.76 kg·s−1 on nine dates. These local and variable sources contribute to the CH4 budget and can better constrain climate change models.

Published
2021
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7. Improved deconvolution of mineral reflectance spectra

Author

Rodolphe Marion, Ronan Rialland, Charles Soussen, Veronique Carrere, Centre d'Études de Limeil-Valenton (CEA-DAM), Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Planétologie et Géodynamique [UMR 6112] (LPG), Université d'Angers (UA)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), and Université d'Angers (UA)-Université de Nantes - Faculté des Sciences et des Techniques

Subjects
Atmospheric Science, Materials science, 010504 meteorology & atmospheric sciences, Continuum (design consultancy), 0211 other engineering and technologies, Hyperspectral images, Hyperspectral imaging, Mineralogy, AGM procedure, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], 02 engineering and technology, 01 natural sciences, Spectral line, HySpex, Characterization (materials science), Mineral reflectance spectra, EGO model, Deconvolution, Sensitivity (control systems), Computers in Earth Sciences, Absorption (electromagnetic radiation), Continuum Modeling, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

International audience; Reflectance spectroscopy is a widely used technique for mineral identification and characterization. Since modern airborne and satellite-borne sensors yield an increasing number of hyperspectral data, it is crucial to develop unsupervised methods to retrieve relevant spectral features from reflectance spectra. Spectral deconvolution aims to decompose a reflectance spectrum as a sum of a continuum modeling its overall shape and some absorption features. We present a flexible and automatic method able to deal with various minerals. The approach is based on a physical model and allows us to include noise statistics. It consists of three successive steps: (i) continuum pre-estimation based on non-linear least-squares; (ii) pre-estimation of absorption features using a greedy algorithm; (iii) refinement of the continuum and absorption estimates. The procedure is first validated on synthetic spectra, including a sensitivity study to instrumental noise and a comparison to other approaches. Then, it is tested on various laboratory spectra. In most cases, absorption positions are recovered with an accuracy lower than 5 nm, enabling mineral identification. Finally, the proposed method is assessed using hyperspectral images of quarries acquired during a dedicated airborne campaign. Minerals such as calcite and gypsum are accurately identified based on their diagnostic absorption features, including when they are in a mixture. Small changes in the shape of the kaolinite doublet are also detected and could be related to crystallinity or mixtures with other minerals such as gibbsite. The potential of the method to produce mineral maps is also demonstrated.

Published
2021

8. OMP-based algorithm for mineral reflectance spectra deconvolution from hyperspectral images

Author

Rodolphe Marion, Ronan Rialland, Charles Soussen, Veronique Carrere, CEA (CEA/DAM), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Planétologie et Géodynamique [UMR 6112] (LPG), Université d'Angers (UA)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Deconvolution procedure, 010504 meteorology & atmospheric sciences, Computer science, 0211 other engineering and technologies, Hyperspectral imaging, 02 engineering and technology, Sparse approximation, Overfitting, 01 natural sciences, Reflectivity, Spectral line, Hyperspectral image, Mineral reflectance spectra, Orthogonal Matching Pursuit, EGO model, Noise (video), Deconvolution, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, [SDU.STU.MI]Sciences of the Universe [physics]/Earth Sciences/Mineralogy
Abstract

International audience; The absorption positions and shapes are key information to identify and characterize a mineral from its reflectance spectrum. With the development of new airborne and satellite-borne hyperspectral sensors, automatic methods have to be developed to extract and analyze this useful information. A flexible deconvolution procedure, able to deal with various sensor characteristics and a wide variety of minerals of interest, is proposed. The approach is based on the sparse representation of the spectrum and the use of a greedy algorithm, the Non-Negative Orthogonal Matching Pursuit algorithm. First, NNOMP is adapted to deal with a parameteric physical model of mineral reflectance spectra. Then, noise statistical information is taken into account to improve the detection of small absorptions while minimizing overfitting effects. The procedure is tested on real data from two quarries in France. Results show the potential of our procedure for the estimation of a consistent number of absorptions whose parameters can be used to analyze the mineralogy.

Published
2020

9. Applications in remote sensing—anthropogenic activities

Author

Mauro Dalla Mura, Christiane Weber, Veronique Carrere, Pierre-Yves Foucher, Kuniaki Uto, Xavier Briottet, Sophie Fabre, Rodolphe Marion, Josselin Aval, ONERA / DOTA, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, Laboratoire de Planétologie et Géodynamique - Angers (LPG-ANGERS), Laboratoire de Planétologie et Géodynamique [UMR 6112] (LPG), Université d'Angers (UA)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Détection et de Géophysique (CEA) (LDG), DAM Île-de-France (DAM/DIF), Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Tokyo Institute of Technology [Tokyo] (TITECH), 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), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
010504 meteorology & atmospheric sciences, Agricultural ecosystems, Hyperspectral imaging, DSM data, 04 agricultural and veterinary sciences, 01 natural sciences, Natural resource, VNIR, SWIR, Soil, Thematic map, Remote sensing (archaeology), Hyperspectral imagery, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Bauxaline, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 0105 earth and related environmental sciences, Remote sensing
Abstract

International audience; This chapter aims at presenting thematic applications of hyperspectral imagery related to human activities. This chapter is a companion of Chapter 3.1 in which four applications related to monitoring of natural resources or inspection of scenes were presented.

Published
2020

10. Anthropogenic aerosol emissions mapping and characterization by imaging spectroscopy – application to a metallurgical industry and a petrochemical complex

Author

Rodolphe Marion, Xavier Briottet, Pierre-Yves Foucher, Yannick Philippets, Laboratoire de Détection et de Géophysique (CEA) (LDG), DAM Île-de-France (DAM/DIF), Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), ONERA / DOTA, Université de Toulouse [Toulouse], and ONERA-PRES Université de Toulouse

Subjects
[PHYS]Physics [physics], 010504 meteorology & atmospheric sciences, Metallurgy, 0211 other engineering and technologies, AEROSOL, 02 engineering and technology, Metallurgical industry, 01 natural sciences, Characterization (materials science), Aerosol, Imaging spectroscopy, [SPI]Engineering Sciences [physics], Lead (geology), Petrochemical, 13. Climate action, HYPERSPECTRAL, General Earth and Planetary Sciences, Environmental science, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

International audience; This paper is focused on the retrieval of industrial aerosol optical thickness (AOT) and microphysical properties by means of airborne imaging spectroscopy. Industrial emissions generally lead to optically thin plumes requiring an adapted detection method taking into account the weak proportion of particles sought in the atmosphere. To this end, a semi-analytical model combined with the Cluster-Tuned Matched Filter (CTMF) algorithm is presented to characterize those plumes, requiring the knowledge of the soil under the plume. The model allows the direct computation of the at-sensor radiance when a plume is included in the radiative transfer. When applied to industrial aerosol classes as defined in this paper, simulated spectral radiances can be compared to ‘real’ MODTRAN (Moderate Resolution Atmospheric Transmission) radiances using the Spectral Angle Mapper (SAM). On the range from 0.4 to 0.7 µm, for three grounds (water, vegetation, and bright one), SAM scores are lower than 0.043 in the worst case (a both absorbing and scattering particle over a bright ground), and usually lower than 0.025. The darker the ground reflectance is, the more accurate the results are (typically for reflectance lower than 0.3). Concerning AOT retrieval capabilities, with a pre-calculated model for a reference optical thickness of 0.25, we are able to retrieve plume AOT at 550 nm in the range 0.0 to 0.4 with an error usually ranging between 9% and 13%. The first test case is a CASI (Compact Airborne Spectrographic Imager) image acquired over the metallurgical industry of Fos-sur-Mer (France). First results of the use of the model coupled with CTMF algorithm reveal a scattering aerosol plume with particle sizes increasing with the distance from the stack (from detection score of 54% near the stack for particles with a diameter of 0.1 µm, to 69% away from it for 1.0 µm particles). A refinement is made then to estimate more precisely aerosol plume properties, using a multimodal distribution based on the previous results. It leads to find a mixture of sulfate and brown carbon particles with a plume AOT ranging between 0.2 and 0.5. The second test case is an AHS (Airborne Hyperspectral Scanner) image acquired over the petrochemical site of Antwerp (Belgium). The first CTMF application results in detecting a brown carbon aerosol of 0.1 µm mode (detection score is 51%). Refined results show the evolution of the AOT decreasing from 0.15 to 0.05 along the plume for a mixture of brown carbon fine mode and 0.3 µm radius of sulfate aerosol.

Published
2018

11. Preface

Author

Rodolphe MARION

Published
2018

12. European hyperspectral explorer : Hypex-2. Monitoring anthropogenic influences in critical zones

Author

Xavier Briottet, Asner, Gregory P., Touria Bajjouk, Véronique Carrère, Sabine Chabrillat, Malik Chami, Jocelyn Chanussot, Andrew Dekker, Christophe Delacourt, Jean-Baptiste Féret, Pierre-Yves Foucher, Santos, J. M., Patrick Gambade, Peter Gege, Cecile Gomez, Stéphane Jacquemoud, Marie-José Lefèvre, Camille Lelong, López Sebastián, A., Rodolphe Marion, David Mckee, Audrey Minghelli-Roman, Duccio Rocchini, Maria Joao de Los Santos, Schaepman, Michael E., Thomas Schmid, David Sheeren, Michal Shimoni, Andrew Skidmore, Ben Somers, Bo Stenberg, Emmanuelle Vaudour, Christiane Weber, ONERA - The French Aerospace Lab [Toulouse], ONERA, Department of Plant Biology [Carnegie] (DPB), Carnegie Institution for Science, Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Université de Nantes (UN), German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), Université Pierre et Marie Curie - Paris 6 (UPMC), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), University of Queensland [Brisbane], Université de Bretagne Occidentale, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Utrecht University [Utrecht], Università degli Studi di Pavia = University of Pavia (UNIPV), Dienstleistungszentren Landlicher Raum Rheinland-Pfalz (DLR), Institut de Recherche pour le Développement (IRD), Université Paris Diderot - Paris 7 (UPD7), Centre National d’Etudes Spatiales, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Universidad de las Palmas de Gran Canaria (ULPGC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), University of Strathclyde [Glasgow], Université de Toulon (UTLN), Signal et Image (SIIM), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Trento (UNITN), Universität Zürich [Zürich] = University of Zurich (UZH), Centro de Investigaciones Energéticas Medioambientales y Tecnológicas [Madrid] (CIEMAT), Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Groupe de recherche en économie mathématique et quantitative (GREMAQ), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), International Institute for Geo-Information Science and Earth Observation, Université Catholique de Louvain = Catholic University of Louvain (UCL), Swedish University of Agricultural Sciences (SLU), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Carnegie Institution for Science [Washington], Université Grenoble Alpes (COMUE) (UGA), University of Pavia, University of Trento, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-École des hautes études en sciences sociales (EHESS)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse 1 Capitole (UT1), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract

Événement(s) lié(s) : - 5. International Symposium on Recent Advances in Quantitative Remote Sensing (RAQS),; Valence (ESP) - (2017-09-05); International audience

Published
2017

13. The French EO high spatial resolution hyperspectral dual mission - an update

Author

Véronique Carrère, Xavier Briottet, Stéphane Jacquemoud, Rodolphe Marion, Anne Bourguignon, Malik Chami, Jocelyn Chanussot, Stéphane Chevrel, Philippe Déliot, Marie Dumont, Pierre-Yves Foucher, Audrey Minghelli-Roman, David Sheeren, Christiane Weber, Marie-José Lefèvre-Fonollosa, Mioara Mandea, Faculté des Sciences et Techniques, ONERA - The French Aerospace Lab [Toulouse], ONERA, Laboratoire GéoSciences Réunion (LGSR), Université de La Réunion (UR)-Institut de Physique du Globe de Paris, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Grenoble Alpes (COMUE) (UGA), Météo France, Université du Sud Toulon Var, Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Centre National d’Etudes Spatiales, Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Commissariat à l'Energie Atomique et aux énergies alternatives - CEA (FRANCE), Centre National d'Études Spatiales - CNES (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Institut de Physique du Globe de Paris - IPGP (FRANCE), Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE), Sorbonne Université (FRANCE), Université Grenoble Alpes - UGA (FRANCE), Université de Strasbourg - UNISTRA (FRANCE), Université de Nantes (FRANCE), Université du Sud Toulon-Var - USTV (FRANCE), Université de La Réunion (FRANCE), Bureau de Recherches Géologiques et Minières - BRGM (FRANCE), Institut national des sciences de l'Univers - INSU (FRANCE), Météo France (FRANCE), Université Nantes Angers Le Mans - UNAM (FRANCE), Université de La Rochelle (FRANCE), Institut de Physique du Globe de Paris - IPGP (Paris, France), Laboratoire d'Océanographie de Villefranche - LOV (Villefranche-sur-Mer, France), Centre National de Recherches Météorologiques - CNRM (Toulouse, France), Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Instrumentation et méthodes pour l'astrophysique, Spaceborne sensors, [SDV]Life Sciences [q-bio], Imaging spectrometry, Traitement du signal et de l'image, Airborne imaging spectroscopy, [SHS]Humanities and Social Sciences
Abstract

International audience; More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion [1] or HICO [2] have clearly demonstrated the ability of such a remote sensing technique to produce value added information regarding surface composition and physical properties for a large variety of applications [3]. Scheduled missions such as EnMAP [4], HISUI [5] or PRISMA [6] prove the increased interest of the scientific community for such a type of remote sensing data. In France, after gathering a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyperspectral, GSH [7]) to establish an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution, CNES (French Space Agency) decided a pre-phase A study for an hyperspectral mission concept called HYPXIM (HYPerspectral-X IMagery), the main fields of applications of which were to be vegetation, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. During this pre-phase A, the feasibility of such a platform was evaluated, based on specific studies supported by Defence and a more accurate definition of reference radiances and instrument characteristics. Results also pointed to applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. For example, in the case of ecosystem studies, it is generally agreed that many model variables and processes are not accurately represented and that upcoming sensors with improved spatial and spectral capabilities, such as higher resolution imaging spectrometers, are needed to further improve the quality and accuracy of model variables [8, 9]. The growing interest for urban environment related applications also emphasized the need for an increased spatial resolution [10, 11]. Finally, short revisit time is an issue for security and Defense as well as crisis monitoring. Table 1 summarizes the Science and Defence mission requirements at the end of pre-phase A. Two instrument designs were proposed by the industry (EADS-Astrium and Thales Alenia Space) based on these new requirements [12]: HYPXIM-Challenging, on a micro-satellite platform, with a 15 m pixel and HYPXIM-Performance, on a mini-satellite platform, with a 8 m pixel, and possible TIR hyperspectral capabilities. Both scenarios included a PAN camera with a 1.85 m pixel. Platform agility would allow for “on-event mode” with a 3-day revisit time. CNES decided to select HYPXIM-Performance, the system providing a higher spatial resolution (pixel ≤ 8 m, [13, 14]), but without TIR capabilities, for a phase A study [15]. This phase A was to start at the beginning of 2013 but is currently stopped due to budget constraints. An important part of the activities has been focusing on getting the French community more involved through various surveys and workshops in preparation for the CNES prospective meeting, an important step for the future of the mission. During this prospective meeting, which took place last March, decision was taken to keep HYPXIM alive as a mid-term (2020-2025) mission. The attendance at the recent workshop organized by the SFPT-GH (Société Française de Photogrammétrie et Télédétection, Groupe Hyperspectral) which gathered more than 90 participants from various field of application, including the industry (see http://www.sfpt.fr/hyperspectral for more details), demonstrates the interest and support of the French scientific community for a high spatial resolution imaging spectrometry mission.

Published
2014

14. The French proposal for a high spatial resolution hyperspectral mission

Author

Véronique Carrère, Xavier Briottet, Stéphane Jacquemoud, Rodolphe Marion, Anne Bourguignon, Malik Chami, Jocelyn Chanussot, Stéphane Chevrel, Philippe Déliot, Marie Dumont, Pierre-Yves Foucher, Cécile Gomez, Audrey Roman-Minghelli, David Sheeren, Marie-José Lefèvre, Mioara Mandea, Laboratoire de Planétologie et Géodynamique [UMR 6112] (LPG), Université d'Angers (UA)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), ONERA - The French Aerospace Lab [Toulouse], ONERA, Laboratoire GéoSciences Réunion (LGSR), Université de La Réunion (UR)-Institut de Physique du Globe de Paris, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Grenoble Alpes (COMUE) (UGA), Météo France, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), 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), 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), Université de Toulon (UTLN), Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Centre National d’Etudes Spatiales, Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)-Université d'Angers (UA)

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, [SHS]Humanities and Social Sciences
Abstract

International audience; More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion or HICO have clearly demonstrated the ability of such a remote sensing technique to produce value added information regard- ing surface composition and physical properties for a large variety of applications. Scheduled missions such as EnMAP and PRISMA prove the increased interest of the scientific community for such a type of remote sensing data. In France, a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyper-spectral, GSH) established an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution. From these conclusions, CNES (French Space Agency) decided a phase 0 study for an hyperspectral mission concept, named at this time HYPXIM (HYPerspectral-X IMagery), the main fields of applications are vegetation biodiversity, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. Results pointed out applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. The phase A started at the beginning of 2013 based on the following HYPXIM characteristics: a hyperspectral camera covering the [0.4 – 2.5 μ m] spectral range with a 8 m ground sampling distance (GSD) and a PAN camera with a 1.85 m GSD, onboard a mini-satellite platform. This phase A is currently stopped due to budget constraints. Nevertheless, the Science team is currently focusing on the preparation for the next CNES prospective meeting (March, 2014), an important step for the future of the mission. This paper will provide an update of the status of this mission and of new results obtained by the Science team.

Published
2014

15. Modeling directional-hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 µm to 5.7 µm with the PROSPECT-VISIR model

Author

Stéphane Jacquemoud, Rodolphe Marion, B Ribeiro da Luz, Albert Olioso, F. Gerber, S Fabre, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Physique du Globe de Paris (IPGP), Centre National de la Recherche Scientifique (CNRS)-Université de La Réunion (UR)-Université Paris Diderot - Paris 7 (UPD7)-IPG PARIS-Institut national des sciences de l'Univers (INSU - CNRS), ONERA - The French Aerospace Lab [Toulouse], ONERA, French national remote sensing program (INSU Programme National de Teledetection Spatiale) as part of the SVETIR (Soil-Vegetation Emissivity in the Thermal Infra-Red) project, and 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)

Subjects
Absorption of water, Materials science, 010504 meteorology & atmospheric sciences, Infrared, LEAF OPTICAL PROPERTIES, 0211 other engineering and technologies, Soil Science, Hyperspectral imaging, INFRARED, Geology, 02 engineering and technology, Fresnel equations, 01 natural sciences, WATER CONTENT, REMOTE SENSING, Wavelength, [SDU]Sciences of the Universe [physics], Transmittance, RADIATIVE TRANSFER MODELS, Computers in Earth Sciences, Spectral resolution, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

International audience; Modeling directional–hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 μm to 5.7 μm with the PROSPECT-VISIR model Vegetation water content retrieval using passive remote sensing techniques in the 0.4–2.5 μm region (reflection of solar radiation) and the 8–14 μm region (emission of thermal radiation) has given rise to an abundant literature. The wavelength range in between, where the main water absorption bands are located, has surprisingly received very little attention because of the complexity of the radiometric signal that mixes both reflected and emitted fluxes. Nevertheless, it is now covered by the latest generation of passive optical sensors (e.g. SEBASS, AHS). This work aims at modeling leaf spectral reflectance and transmittance in the infrared, particularly between 3 μm and 5 μm, to improve the retrieval of vegetation water content using hyperspectral data. Two unique datasets containing 32 leaf samples each were acquired in 2008 at the USGS National Center, Reston (VA, USA) and the ONERA Research Center, Toulouse (France). Reflectance and transmittance were recorded using laboratory spectrometers in the spectral region from 0.4 μm to 14 μm, and the leaf water and dry matter contents were determined. It turns out that these spectra are strongly linked to water content up to 5.7 μm. This dependence is much weaker further into the infrared, where spectral features seem to be mainly associated with the biochemical composition of the leaf surface. The measurements show that leaves transmit light in this wavelength domain and that the transmittance of dry samples can reach 0.35 of incoming light around 5 μm, and 0.05 around 11 μm. This work extends the PROSPECT leaf optical properties model by taking into account the high absorption levels of leaf constituents (by the insertion of the complex Fresnel coefficients) and surface phenomena (by the addition of a top layer). The new model, PROSPECT-VISIR (VISible to InfraRed), simulates leaf reflectance and transmittance between 0.4 μm and 5.7 μm (at 1 nm spectral resolution) with a root mean square error (RMSE) of 0.017 and 0.018, respectively. Model inversion also allows the prediction of water (RMSE = 0.0011 g/cm²) and dry matter (RMSE = 0.0013 g/cm²) contents.

Published
2011

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