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Article Dans Une Revue (Article De Synthèse) Biogeosciences Discussions Année : 2023

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

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

Résumé

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.

Dates et versions

hal-04244106 , version 1 (16-10-2023)

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Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, et al.. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity. Biogeosciences Discussions, 2023, ⟨10.5194/bg-2023-88⟩. ⟨hal-04244106⟩
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