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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00006184naa a2200673 4500
001oai:lup.lub.lu.se:3e992127-94a6-4bec-a80a-bfb0980cf4e4
003SwePub
008240229s2024 | |||||||||||000 ||eng|
0247 a https://lup.lub.lu.se/record/3e992127-94a6-4bec-a80a-bfb0980cf4e42 URI
0247 a https://doi.org/10.5194/bg-21-473-20242 DOI
040 a (SwePub)lu
041 a engb eng
042 9 SwePub
072 7a for2 swepub-publicationtype
072 7a ref2 swepub-contenttype
1001 a Kooistra, Lammertu Wageningen University4 aut
2451 0a Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity
264 1c 2024
300 a 39 s.
520 a 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.
650 7a NATURVETENSKAPx Geovetenskap och miljövetenskapx Naturgeografi0 (SwePub)105072 hsv//swe
650 7a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Physical Geography0 (SwePub)105072 hsv//eng
7001 a Berger, Katjau University of Valencia4 aut
7001 a Brede, Benjaminu GFZ German Research Centre for Geosciences4 aut
7001 a Graf, Lukas Valentinu ETH Zürich,Agroscope4 aut
7001 a Aasen, Helgeu ETH Zürich,Agroscope4 aut
7001 a Roujean, Jean Louisu National Centre for Space Studies (CNES)4 aut
7001 a Machwitz, Miriamu Luxembourg Institute of Science and Technology (LIST)4 aut
7001 a Schlerf, Martinu Luxembourg Institute of Science and Technology (LIST)4 aut
7001 a Atzberger, Clement4 aut
7001 a Prikaziuk, Egoru Faculty of Geo-Information Science and Earth Observation – ITC4 aut
7001 a Ganeva, Dessislavau Space Research and Technology Institute4 aut
7001 a Tomelleri, Enricou Free University of Bozen-Bolzano4 aut
7001 a Croft, Hollyu University of Sheffield4 aut
7001 a Reyes Muñoz, Pablou University of Valencia4 aut
7001 a Garcia Millan, Virginiau University of Malaga4 aut
7001 a Darvishzadeh, Roshanaku Faculty of Geo-Information Science and Earth Observation – ITC4 aut0 (Swepub:lu)gis-rdd
7001 a Koren, Gerbrandu Copernicus Institute of Sustainable Development4 aut
7001 a Herrmann, Ittaiu Hebrew University of Jerusalem4 aut
7001 a Rozenstein, Offeru Agricultural Research Organization, Volcani Center4 aut
7001 a Belda, Santiagou University of Alicante4 aut
7001 a Rautiainen, Miinau Aalto University4 aut
7001 a Rune Karlsen, Steinu NORCE Norwegian Research Centre4 aut
7001 a Figueira Silva, Cláudio4 aut
7001 a Cerasoli, Sofia4 aut
7001 a Pierre, Jon4 aut
7001 a Tanlr Kaylkçl, Emineu Karadeniz Technical University4 aut
7001 a Halabuk, Andreju Slovak Academy of Sciences4 aut
7001 a Tunc Gormus, Esrau Karadeniz Technical University4 aut
7001 a Fluit, Franku Wageningen University4 aut
7001 a Cai, Zhanzhangu Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science4 aut0 (Swepub:lu)nate-zgc
7001 a Kycko, Marlenau University of Warsaw4 aut
7001 a Udelhoven, Thomasu University of Trier4 aut
7001 a Verrelst, Jochemu University of Valencia4 aut
7102 a Wageningen Universityb University of Valencia4 org
7730 t Biogeosciencesg 21:2, s. 473-511q 21:2<473-511x 1726-4170
8564 u http://dx.doi.org/10.5194/bg-21-473-2024x freey FULLTEXT
8564 8u https://lup.lub.lu.se/record/3e992127-94a6-4bec-a80a-bfb0980cf4e4
8564 8u https://doi.org/10.5194/bg-21-473-2024

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