TY - JOUR
T1 - Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity
AU - Kooistra, Lammert
AU - Berger, Katja
AU - Brede, Benjamin
AU - Graf, Lukas Valentin
AU - Aasen, Helge
AU - Roujean, Jean Louis
AU - Machwitz, Miriam
AU - Schlerf, Martin
AU - Atzberger, Clement
AU - Prikaziuk, Egor
AU - Ganeva, Dessislava
AU - Tomelleri, Enrico
AU - Croft, Holly
AU - Reyes Muñoz, Pablo
AU - Garcia Millan, Virginia
AU - Darvishzadeh, Roshanak
AU - Koren, Gerbrand
AU - Herrmann, Ittai
AU - Rozenstein, Offer
AU - Belda, Santiago
AU - Rautiainen, Miina
AU - Rune Karlsen, Stein
AU - Figueira Silva, Cláudio
AU - Cerasoli, Sofia
AU - Pierre, Jon
AU - Tanlr Kaylkçl, Emine
AU - Halabuk, Andrej
AU - Tunc Gormus, Esra
AU - Fluit, Frank
AU - Cai, Zhanzhang
AU - Kycko, Marlena
AU - Udelhoven, Thomas
AU - Verrelst, Jochem
N1 - Funding Information:
The research was mainly supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (Euro10 pean Cooperation in Science and Technology, https://www.cost.eu , last access: 18 October 2023). The research was further funded by the European Research Council (ERC) under the FLEXINEL project (grant no. 101086622). Katja Berger, Pablo Reyes Muñoz, and Jochem Verrelst were funded by the European Union (ERC, 15 FLEXINEL, 101086622).
Funding Information:
We thank the two reviewers for contributing to the discussion of our manuscript and therefore substantially helping to improve the study. Santiago Belda was partially supported by Generalitat Valenciana (SEJIGENT/2021/001) and the European Union – NextGenerationEU (ZAMBRANO 21-04).
Publisher Copyright:
© 2024 Lammert Kooistra et al.
PY - 2024/1/25
Y1 - 2024/1/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85184040204&partnerID=8YFLogxK
U2 - 10.5194/bg-21-473-2024
DO - 10.5194/bg-21-473-2024
M3 - Review Article
AN - SCOPUS:85184040204
SN - 1726-4170
VL - 21
SP - 473
EP - 511
JO - Biogeosciences
JF - Biogeosciences
IS - 2
ER -