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

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 fun...

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Vydáno v:	Biogeosciences Ročník 21; číslo 2; s. 473 - 511
Hlavní autoři:	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, Verrelst, Jochem
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Katlenburg-Lindau Copernicus GmbH 25.01.2024 European Geosciences Union Copernicus Publications
Témata:	Analysis Anomalies Atmospheric boundary layer Biomass Carbon Climate and human activity Climate change Climatic changes Data acquisition Data transmission Digital twins Earth and Related Environmental Sciences Ecosystems Environmental Engineering Environmental Sciences Fluorescence Geovetenskap och relaterad miljövetenskap Natural Sciences Naturgeografi Naturvetenskap Net Primary Productivity Photosynthesis Physical Geography Platforms Primary production Productivity Radiation Radiative transfer Radiative transfer models Remote sensing Respiration Satellites Sensors Spectroradiometers Statistical analysis Time series Trend analysis Vegetation Vegetation index Vegetation patterns
ISSN:	1726-4189, 1726-4170, 1726-4189
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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-comparison of different productivity metrics. 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 enhances the accuracy of vegetation productivity monitoring.
AbstractList	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 solarinduced 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-comparison of different productivity metrics. 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 enhances the accuracy of vegetation productivity monitoring. 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-comparison of different productivity metrics. 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 enhances the accuracy of vegetation productivity monitoring. 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-comparison of different productivity metrics. 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 enhances the accuracy of vegetation productivity monitoring.
Audience	Academic
Author	Schlerf, Martin Reyes Muñoz, Pablo Figueira Silva, Cláudio Ganeva, Dessislava Atzberger, Clement Graf, Lukas Valentin Prikaziuk, Egor Cerasoli, Sofia Belda, Santiago Herrmann, Ittai Berger, Katja Machwitz, Miriam Koren, Gerbrand Roujean, Jean-Louis Croft, Holly Rautiainen, Miina Pierre, Jon Aasen, Helge Darvishzadeh, Roshanak Garcia Millan, Virginia Tanır Kayıkçı, Emine Udelhoven, Thomas Kycko, Marlena Rune Karlsen, Stein Halabuk, Andrej Kooistra, Lammert Verrelst, Jochem Rozenstein, Offer Fluit, Frank Brede, Benjamin Tunc Gormus, Esra Tomelleri, Enrico Cai, Zhanzhang
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Snippet	Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical...
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SubjectTerms	Analysis Anomalies Atmospheric boundary layer Biomass Carbon Climate and human activity Climate change Climatic changes Data acquisition Data transmission Digital twins Earth and Related Environmental Sciences Ecosystems Environmental Engineering Environmental Sciences Fluorescence Geovetenskap och relaterad miljövetenskap Natural Sciences Naturgeografi Naturvetenskap Net Primary Productivity Photosynthesis Physical Geography Platforms Primary production Productivity Radiation Radiative transfer Radiative transfer models Remote sensing Respiration Satellites Sensors Spectroradiometers Statistical analysis Time series Trend analysis Vegetation Vegetation index Vegetation patterns
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Title	Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
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