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  • Kooistra, LammertWageningen University (author)

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

  • Article/chapterEnglish2024

Publisher, publication year, extent ...

  • 2024
  • 39 s.

Numbers

  • LIBRIS-ID:oai:lup.lub.lu.se:3e992127-94a6-4bec-a80a-bfb0980cf4e4
  • https://lup.lub.lu.se/record/3e992127-94a6-4bec-a80a-bfb0980cf4e4URI
  • https://doi.org/10.5194/bg-21-473-2024DOI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:for swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • 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.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Berger, KatjaUniversity of Valencia (author)
  • Brede, BenjaminGFZ German Research Centre for Geosciences (author)
  • Graf, Lukas ValentinETH Zürich,Agroscope (author)
  • Aasen, HelgeETH Zürich,Agroscope (author)
  • Roujean, Jean LouisNational Centre for Space Studies (CNES) (author)
  • Machwitz, MiriamLuxembourg Institute of Science and Technology (LIST) (author)
  • Schlerf, MartinLuxembourg Institute of Science and Technology (LIST) (author)
  • Atzberger, Clement (author)
  • Prikaziuk, EgorFaculty of Geo-Information Science and Earth Observation – ITC (author)
  • Ganeva, DessislavaSpace Research and Technology Institute (author)
  • Tomelleri, EnricoFree University of Bozen-Bolzano (author)
  • Croft, HollyUniversity of Sheffield (author)
  • Reyes Muñoz, PabloUniversity of Valencia (author)
  • Garcia Millan, VirginiaUniversity of Malaga (author)
  • Darvishzadeh, RoshanakFaculty of Geo-Information Science and Earth Observation – ITC(Swepub:lu)gis-rdd (author)
  • Koren, GerbrandCopernicus Institute of Sustainable Development (author)
  • Herrmann, IttaiHebrew University of Jerusalem (author)
  • Rozenstein, OfferAgricultural Research Organization, Volcani Center (author)
  • Belda, SantiagoUniversity of Alicante (author)
  • Rautiainen, MiinaAalto University (author)
  • Rune Karlsen, SteinNORCE Norwegian Research Centre (author)
  • Figueira Silva, Cláudio (author)
  • Cerasoli, Sofia (author)
  • Pierre, Jon (author)
  • Tanlr Kaylkçl, EmineKaradeniz Technical University (author)
  • Halabuk, AndrejSlovak Academy of Sciences (author)
  • Tunc Gormus, EsraKaradeniz Technical University (author)
  • Fluit, FrankWageningen University (author)
  • Cai, ZhanzhangLund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science(Swepub:lu)nate-zgc (author)
  • Kycko, MarlenaUniversity of Warsaw (author)
  • Udelhoven, ThomasUniversity of Trier (author)
  • Verrelst, JochemUniversity of Valencia (author)
  • Wageningen UniversityUniversity of Valencia (creator_code:org_t)

Related titles

  • In:Biogeosciences21:2, s. 473-5111726-4170

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