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6. Continental-scale evaluation of three ECOSTRESS land surface temperature products over Europe and Africa: Temperature-based validation and cross-satellite comparison

Author

Hu, Tian, Mallick, Kaniska, Hulley, Glynn C., Planells, Lluís Perez, Göttsche, Frank M., Schlerf, Martin, Hitzelberger, Patrik, Didry, Yoanne, Szantoi, Zoltan, Alonso, Itziar, Sobrino, José A., Skoković, Dražen, Roujean, Jean-Louis, Boulet, Gilles, Gamet, Philippe, and Hook, Simon

Published
2022
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11. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Author

Universidad de Alicante. Departamento de Matemática Aplicada, Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean-Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes-Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Karlsen, Stein Rune, Silva, Cláudio F., Cerasoli, Sofia, Pierre, Jon, Kayıkçı, Emine Tanır, Halabuk, Andrej, Gormus, Esra Tunc, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, Verrelst, Jochem, Universidad de Alicante. Departamento de Matemática Aplicada, Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean-Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes-Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Karlsen, Stein Rune, Silva, Cláudio F., Cerasoli, Sofia, Pierre, Jon, Kayıkçı, Emine Tanır, Halabuk, Andrej, Gormus, Esra Tunc, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, and Verrelst, Jochem

Abstract

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-com

Published
2024

12. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Author

Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Rune Karlsen, Stein, Figueira Silva, Cláudio, Cerasoli, Sofia, Pierre, Jon, Tanlr Kaylkçl, Emine, Halabuk, Andrej, Tunc Gormus, Esra, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, Verrelst, Jochem, Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Rune Karlsen, Stein, Figueira Silva, Cláudio, Cerasoli, Sofia, Pierre, Jon, Tanlr Kaylkçl, Emine, Halabuk, Andrej, Tunc Gormus, Esra, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, and Verrelst, Jochem

Abstract

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter

Published
2024

13. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Author

Global Ecohydrology and Sustainability, Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Rune Karlsen, Stein, Figueira Silva, Cláudio, Cerasoli, Sofia, Pierre, Jon, Tanlr Kaylkçl, Emine, Halabuk, Andrej, Tunc Gormus, Esra, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, Verrelst, Jochem, Global Ecohydrology and Sustainability, Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Rune Karlsen, Stein, Figueira Silva, Cláudio, Cerasoli, Sofia, Pierre, Jon, Tanlr Kaylkçl, Emine, Halabuk, Andrej, Tunc Gormus, Esra, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, and Verrelst, Jochem

Published
2024

15. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Author

Kooistra, Lammert, primary, Berger, Katja, additional, Brede, Benjamin, additional, Graf, Lukas Valentin, additional, Aasen, Helge, additional, Roujean, Jean-Louis, additional, Machwitz, Miriam, additional, Schlerf, Martin, additional, Atzberger, Clement, additional, Prikaziuk, Egor, additional, Ganeva, Dessislava, additional, Tomelleri, Enrico, additional, Croft, Holly, additional, Reyes Muñoz, Pablo, additional, Garcia Millan, Virginia, additional, Darvishzadeh, Roshanak, additional, Koren, Gerbrand, additional, Herrmann, Ittai, additional, Rozenstein, Offer, additional, Belda, Santiago, additional, Rautiainen, Miina, additional, Rune Karlsen, Stein, additional, Figueira Silva, Cláudio, additional, Cerasoli, Sofia, additional, Pierre, Jon, additional, Tanır Kayıkçı, Emine, additional, Halabuk, Andrej, additional, Tunc Gormus, Esra, additional, Fluit, Frank, additional, Cai, Zhanzhang, additional, Kycko, Marlena, additional, Udelhoven, Thomas, additional, and Verrelst, Jochem, additional

Published
2024
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16. Mapping Global Land Surface Phenology with Esa's Proba-V Satellite

Author

Jin, Hongxiao, primary, Cai, Zhanzhang, additional, Wolfs, Davy, additional, De Munck, Dominique, additional, Moroz, Michal, additional, Swinnen, Else, additional, Lacaze, Roselyne, additional, Camacho, Fernando, additional, Sánchez-Zapero, Jorge, additional, Roujean, Jean-Louis, additional, Lewis, Philip, additional, and Eklundh, Lars, additional

Published
2024
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20. Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil‐Atmospheric Aridity and Biomes over Europe

Author

Hu, Tian, primary, Mallick, Kaniska, additional, Hitzelberger, Patrik, additional, Didry, Yoanne, additional, Boulet, Gilles, additional, Szantoi, Zoltan, additional, Koetz, Benjamin, additional, Alonso, Itziar, additional, Pascolini‐Campbell, Madeleine, additional, Halverson, Gregory, additional, Cawse‐Nicholson, Kerry, additional, Hulley, Glynn C., additional, Hook, Simon, additional, Bhattarai, Nishan, additional, Olioso, Albert, additional, Roujean, Jean‐Louis, additional, Gamet, Philippe, additional, and Su, Bob, additional

Published
2023
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21. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Author

Kooistra, Lammert, primary, Berger, Katja, additional, Brede, Benjamin, additional, Graf, Lukas Valentin, additional, Aasen, Helge, additional, Roujean, Jean-Louis, additional, Machwitz, Miriam, additional, Schlerf, Martin, additional, Atzberger, Clement, additional, Prikaziuk, Egor, additional, Ganeva, Dessislava, additional, Tomelleri, Enrico, additional, Croft, Holly, additional, Reyes Muñoz, Pablo, additional, Garcia Millan, Virginia, additional, Darvishzadeh, Roshanak, additional, Koren, Gerbrand, additional, Herrmann, Ittai, additional, Rozenstein, Offer, additional, Belda, Santiago, additional, Rautiainen, Miina, additional, Rune Karlsen, Stein, additional, Figueira Silva, Cláudio, additional, Cerasoli, Sofia, additional, Pierre, Jon, additional, Tanır Kayıkçı, Emine, additional, Halabuk, Andrej, additional, Tunc Gormus, Esra, additional, Fluit, Frank, additional, Cai, Zhanzhang, additional, Kycko, Marlena, additional, Udelhoven, Thomas, additional, and Verrelst, Jochem, additional

Published
2023
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23. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Author

Kooistra, Lammert, Berger, Katja, Brede, Benjamin, Graf, Lukas Valentin, Aasen, Helge, Roujean, Jean-Louis, Machwitz, Miriam, Schlerf, Martin, Atzberger, Clement, Prikaziuk, Egor, Ganeva, Dessislava, Tomelleri, Enrico, Croft, Holly, Reyes Muñoz, Pablo, Garcia Millan, Virginia, Darvishzadeh, Roshanak, Koren, Gerbrand, Herrmann, Ittai, Rozenstein, Offer, Belda, Santiago, Rautiainen, Miina, Rune Karlsen, Stein, Figueira Silva, Cláudio, Cerasoli, Sofia, Pierre, Jon, Tanır Kayıkçı, Emine, Halabuk, Andrej, Tunc Gormus, Esra, Fluit, Frank, Cai, Zhanzhang, Kycko, Marlena, Udelhoven, Thomas, and Verrelst, Jochem

Abstract

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring.

Published
2023

29. Evaluating European ECOSTRESS Hub Evapotranspiration Products Retrieved from Three Structurally Contrasting SEB Models over Europe

Author

Hu, Tian, primary, Mallick, Kaniska, additional, Hitzelberger, Patrik, additional, Didry, Yoanne, additional, Boulet, Gilles, additional, Szantoi, Zoltan, additional, Koetz, Benjamin, additional, Alonso, Itziar, additional, Pascolini-Campbell, Madeleine, additional, Halverson, Gregory H, additional, Cawse-Nicholson, Kerry, additional, Hulley, Glynn, additional, Hook, Simon J., additional, Bhattarai, Nishan, additional, Olioso, Albert, additional, Roujean, Jean-Louis, additional, Gamet, Philippe, additional, and Su, Z. Bob, additional

Published
2022
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32. The Indo-French TRISHNA satellite mission: high resolution and high revisit surface temperature for land and coastal ocean

Author

Gamet, Philippe, Salcedo, Corinne, Marcq, Sébastien, Autret, Emmanuelle, Lifermann, Anne, Maisongrande, Philippe, Roujean, Jean-Louis, and Bhattacharya, Bimal K.

Abstract

Presented at the GHRSST XXIII international science team meeting, 27 June-1 July 2022, online and in-person (Barcelona). #GHRSST23 Short abstract The TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment) is a cooperation between the French (CNES) and Indian (ISRO) space agencies, for a satellite to be launched in 2025 for a 5-year lifetime, to measure approximately twice a week the visible, near infrared and thermal infrared signal of the surface-atmosphere system globally and at 60 m resolution for the continents and coastal ocean, with a resolution of 1000 meters over deep ocean. Level 2 products –free and open data policy- include Sea Surface Temperature, visible and near infrared surface reflectances as well as cloud mask, aerosol optical thickness and data quality flags. Design drivers of the mission: (i) monitoring of ecosystem stress and water use, focusing on agriculture and water content of vegetation, through evapotranspiration; and (ii) coastal and inland waters: characterization of the dynamics of the shallow bathymetry; monitoring of exchanges in estuaries and intertidal zones; sea surface temperatures and winds; sub-mesoscale activity in coastal areas and in the high seas; oil spills, thermal pollutants, effluents and wastewater discharges. Interactions are needed with the experts and future users: definition of monitored coastal areas and polar zones, algorithms for SST and optical surface variables computation, cloud mask, product content: variables, auxiliary and ancilliary data. The synergy (orbits, products, algorithms, CAL/VAL) between TRISHNA and future operational high-resolution thermal infrared missions (Surface Biology and Geology (SBG, 2027) from NASA/JPL, Land Surface Temperature Monitoring (LSTM, 2029) from ESA) is also a key element of the preparation of TRISHNA.

Published
2022
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33. CAMCATT: a multisensor experiment over Toulouse to validate TRISHNA urban products

Author

Briottet, Xavier, Roupioz, Laure, Barda-Chatain, Romain, Rodler, Auline, Guernouti, Sihem, Marjorie, Musy, Nerry, Françoise, Lemonsu, Aude, Michel, Aurélie, Poutier, Laurent, Barillot, Philippe, Deliot, Philippe, Cerbelaud, Arnaud, Albitar, Ahmad, Roujean, Jean-Louis, Sobrino, José, Gadal, Sébastien, Carroll, Eric, Bridier, Sébastien, Cassante, Charlène, Guilhem de Lataillade, Amaury, Doublet, Philippe, ONERA / DOTA, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement (Cerema), Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement - Equipe-projet BPE (Cerema Equipe-projet BPE), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Groupe de Météorologie à Moyenne Échelle (GMME), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), DOTA, ONERA [Salon], ONERA, Centre d'études spatiales de la biosphère (CESBIO), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS), Universitat de València (UV), Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Aix Marseille Université (AMU), Délégation générale de l'armement (DGA), Ministère de la Défense, CNES TOSCA TRISHNA (URBAN CAMCATT), CNES, ISRO, GADAL, Sébastien, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SHS.STAT]Humanities and Social Sciences/Methods and statistics, [SHS.GEO] Humanities and Social Sciences/Geography, VISNIR-SWIR Hyperspectral sensor, Toulouse, Urban remote sensing, [SHS.GEO]Humanities and Social Sciences/Geography, Intercalibration, [SDE.ES]Environmental Sciences/Environmental and Society, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Multi-spectral thermal infrared sensor, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SHS.STAT] Humanities and Social Sciences/Methods and statistics, Multi-sensors, Airborne remote sensing, [SDE.ES] Environmental Sciences/Environmental and Society, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Land surface temperature
Abstract

International audience; An extensive field experiment, CAMCATT-AI4GEO, was conducted for 15 days in June 2021 over Toulouse, to acquire an urban reference dataset combining airborne acquisitions and ground measurements. This presentation aims at introducing the campaign and describing the collected data, which will be used for the validation of tools developed to produce future TRISHNA products, such as algorithms used for atmospheric compensation and temperature emissivity retrieval, microclimatic models. The several workshops of this experiment will be detailed. Joint airborne multispectral thermal infrared and VISNIR-SWIR hyperspectral images were acquired along a 30 km transect centred over Toulouse. A second flight was devoted to calibrating these sensors with on-ground reference targets. During and between the flights, two cars acquired air temperature and relative humidity along a predefined itinerary across the city with sensors mounted on their roof. During the airborne acquisitions, land surface temperature measurements were performed in three locations in Toulouse for calibration purposes. They were completed outside the flight by spectral optical properties in the VISNIR-SWIR and thermal infrared domains over a large range of manmade and natural materials. A specific in lab session was dedicated to the intercalibration of the on-ground thermal sensors. Atmosphere composition data were monitored using radio soundings during the airborne acquisitions and completed with continuous measurements from on-ground stations. Finally, the last session was devoted to the validation of a thermo-radiative model. For this purpose, surface temperature sensors were installed at different locations of buildings walls supplemented by a thermal infrared images acquisition for one of the facades in order to continuously monitor its temperature during the 15 days of the experiment. The air temperature was also measured at different positions in the district (under trees, in flats, etc).

Published
2022

35. Scientific Committee

Author

Fouad, Youssef, primary, Carrere, Véronique, additional, Chabrillat, Sabine, additional, Demarez, Valérie, additional, Roujean, Jean Louis, additional, Lacaze, Roselyne, additional, Pattey, Elizabeth, additional, Kayitakire, Francois, additional, Ceschia, Eric, additional, Brunet, Yves, additional, Sheeren, David, additional, Vaudour, Emmanuelle, additional, Alleaume, Samuel, additional, Bappel, Eric, additional, Corgne, Samuel, additional, Arvor, Damien, additional, Catry, Thibault, additional, Rudant, Jean-Paul, additional, Gaetano, Raffaele, additional, Mougin, Eric, additional, Weiss, Marie, additional, Michez, Adrien, additional, Bauwens, Sébastien, additional, Bonnet, Stéphanie, additional, Lejeune, Philippe, additional, Belouard, Thierry, additional, Barbier, Nicolas, additional, Rejou, Maxime, additional, Vincent, Grégoire, additional, Baghdadi, Nicolas, additional, and Zribi, Mehrez, additional

Published
2016
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39. Variation of Clumping Index With Zenith Angle for Forest Canopies

Author

Geng, Jun, primary, Tu, Lili, additional, Chen, Jing M., additional, Roujean, Jean-Louis, additional, Yuan, Gang, additional, Hu, Ronghai, additional, Huang, Jianwei, additional, Zhang, Chunju, additional, Ye, Zhourun, additional, Qu, Xiaochuan, additional, Yu, Min, additional, Zhu, Yongchao, additional, and Tian, Qingjiu, additional

Published
2022
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45. Land surface albedo derived on a daily basis from Meteosat second generation observations

Author

Geiger, Bernhard, Carrer, Dominique, Franchisteguy, Laurent, Roujean, Jean-Louis, and Meurey, Catherine

Subjects
Albedo -- Research, Meteorological satellites -- Usage, Solar radiation -- Research, Business, Earth sciences, Electronics and electrical industries
Abstract

Land surface albedo determines the repartition of downwelling solar radiation into components that are either reflected back to the atmosphere or absorbed by the surface. As more sophisticated soil--vegetation--atmosphere transfer schemes are being implemented in numerical meteorological models, it will become increasingly important to accurately characterize the spatial and temporal albedo variations. Within the scope of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis, we have developed a daily albedo product, which is derived in near real time from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the geostationary satellites of the Meteosat Second Generation series. The basic algorithm concept comprises an atmospheric correction scheme, the inversion of a linear semiempirical model of the bidirectional reflectance distribution function, the angular integration of the bidirectional reflectance distribution function to obtain spectral albedo, and the application of suitable conversion relations to derive broadband albedo estimates. The reflectance model inversion is performed each day based on the available set of clear-sky observations. In addition, constraints on the model parameters are taken into account in the inversion process. By specifying these constraints according to the previous model output in a recursive manner, a complete spatial coverage of the resulting albedo maps is achieved while, at the same time, preserving a high temporal resolution. This paper primarily concentrates on the description of the methodology. In addition, examples for the obtained albedo maps and time series, as well as the first validation results, are presented. Index Terms--Geostationary satellites, land surface modeling, near real time, operational processing, surface albedo.

Published
2008

46. Centre d'expertise scientifique (CES) température de surface et emissivité

Author

Michel, Aurélie, Briottet, Xavier, Irvine, Mark, Lemonsu, Aude, Musy, Marjorie, Nerry, Françoise, Olioso, Albert, Ottle, Catherine, Rivalland, Vincent, Rodler, Auline, Roujean, Jean-Louis, ONERA / DOTA, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement (Cerema)

Subjects
[SDE.MCG]Environmental Sciences/Global Changes
Abstract

International audience; Le Centre d’Expertise Scientifique (CES) Température de surface et émissivité (LST/LSE) réunit des équipes de l’Onera, d’Icube, du LSCE, du Cesbio, de l’INRAE, du Cerema, du CNRM, du CNES et de l’OFB travaillant sur l’amélioration de l’estimation de la température de surface (LST), de l’émissivité de surface (LSE) ainsi que leur cartographie et leur usage. La communauté utilisatrice visée est autant scientifique qu’institutionnelle. L’objectif du CES est de répondre aux enjeux suivants :- Améliorer les algorithmes d’estimation pour les capteurs (actuels et futurs) à haute résolution spatiale dans l’infrarouge thermique- Établir des méthodes d’évaluation afin de fournir des produits avec une incertitude associée- Améliorer la résolution spatiale et/ou temporelle des estimations en fusionnant des données spatiales ou exogènes.- Développer des séries temporelles de produits thermiques- Valoriser et mettre à disposition des produits thermiques pour répondre aux différents besoins,- Fédérer la communauté scientifique et fluidifier les échanges avec les instituts

Published
2020

47. Spectral normalization and fusion of optical sensors for the retrieval of BRDF and albedo: application to VEGETATION, MODIS, and MERIS data sets

Author

Samain, Olivier, Geiger, Bernhard, and Roujean, Jean-Louis

Subjects
Spectroradiometer -- Usage, Albedo -- Research, Reflectance -- Analysis, Business, Earth sciences, Electronics and electrical industries
Abstract

This paper aims at demonstrating the possibility of merging data from various medium-resolution spaceborne sensors to produce a consistent time series of surface bidirectional reflectance distribution function (BRDF) and albedo products. The spectral, directional, temporal, and spatial aspects of the multisensor fusion are presented. Emphasis is then given on the spectral normalization for the fusion of Medium Resolution Imaging Spectrometer Instrument (MERIS) data with Moderate Resolution Imaging Spectroradiometer (MODIS) and VEGETATION (VGT) data. Two methods are evaluated: a simple statistical method, which relies on a linear regression using all the available spectral bands, and a more innovative method called the spectral mode method, which is based on the restitution of the surface spectral signature by a combination of universal spectral functions. Analysis with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data and satellite products reveals that the spectral mode method is more efficient. This approach is used to merge top-of-canopy bidirectional reflectances from MERIS and VGT for the restitution of BRDF and albedo over a subset of West Africa. Compared to the products obtained with MERIS alone, the fusion with VGT demonstrates an improvement of the spatial coverage and a reduction of product uncertainty by about a third. Index Terms--Albedo, bidirectional reflectance distribution function (BRDF), fusion, Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), spectral normalization, VEGETATION sensor.

Published
2006

48. Effect of small-scale snow surface roughness on snow albedo and reflectance

Author

Manninen, Terhikki, primary, Anttila, Kati, additional, Jääskeläinen, Emmihenna, additional, Riihelä, Aku, additional, Peltoniemi, Jouni, additional, Räisänen, Petri, additional, Lahtinen, Panu, additional, Siljamo, Niilo, additional, Thölix, Laura, additional, Meinander, Outi, additional, Kontu, Anna, additional, Suokanerva, Hanne, additional, Pirazzini, Roberta, additional, Suomalainen, Juha, additional, Hakala, Teemu, additional, Kaasalainen, Sanna, additional, Kaartinen, Harri, additional, Kukko, Antero, additional, Hautecoeur, Olivier, additional, and Roujean, Jean-Louis, additional

Published
2021
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