Sökning: WFRF:(Karlsen Rune) > Reviews and synthes...
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000 | 06184naa a2200673 4500 | |
001 | oai:lup.lub.lu.se:3e992127-94a6-4bec-a80a-bfb0980cf4e4 | |
003 | SwePub | |
008 | 240229s2024 | |||||||||||000 ||eng| | |
024 | 7 | a https://lup.lub.lu.se/record/3e992127-94a6-4bec-a80a-bfb0980cf4e42 URI |
024 | 7 | a https://doi.org/10.5194/bg-21-473-20242 DOI |
040 | a (SwePub)lu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a for2 swepub-publicationtype |
072 | 7 | a ref2 swepub-contenttype |
100 | 1 | a Kooistra, Lammertu Wageningen University4 aut |
245 | 1 0 | a Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity |
264 | 1 | c 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 | 7 | a NATURVETENSKAPx Geovetenskap och miljövetenskapx Naturgeografi0 (SwePub)105072 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Physical Geography0 (SwePub)105072 hsv//eng |
700 | 1 | a Berger, Katjau University of Valencia4 aut |
700 | 1 | a Brede, Benjaminu GFZ German Research Centre for Geosciences4 aut |
700 | 1 | a Graf, Lukas Valentinu ETH Zürich,Agroscope4 aut |
700 | 1 | a Aasen, Helgeu ETH Zürich,Agroscope4 aut |
700 | 1 | a Roujean, Jean Louisu National Centre for Space Studies (CNES)4 aut |
700 | 1 | a Machwitz, Miriamu Luxembourg Institute of Science and Technology (LIST)4 aut |
700 | 1 | a Schlerf, Martinu Luxembourg Institute of Science and Technology (LIST)4 aut |
700 | 1 | a Atzberger, Clement4 aut |
700 | 1 | a Prikaziuk, Egoru Faculty of Geo-Information Science and Earth Observation – ITC4 aut |
700 | 1 | a Ganeva, Dessislavau Space Research and Technology Institute4 aut |
700 | 1 | a Tomelleri, Enricou Free University of Bozen-Bolzano4 aut |
700 | 1 | a Croft, Hollyu University of Sheffield4 aut |
700 | 1 | a Reyes Muñoz, Pablou University of Valencia4 aut |
700 | 1 | a Garcia Millan, Virginiau University of Malaga4 aut |
700 | 1 | a Darvishzadeh, Roshanaku Faculty of Geo-Information Science and Earth Observation – ITC4 aut0 (Swepub:lu)gis-rdd |
700 | 1 | a Koren, Gerbrandu Copernicus Institute of Sustainable Development4 aut |
700 | 1 | a Herrmann, Ittaiu Hebrew University of Jerusalem4 aut |
700 | 1 | a Rozenstein, Offeru Agricultural Research Organization, Volcani Center4 aut |
700 | 1 | a Belda, Santiagou University of Alicante4 aut |
700 | 1 | a Rautiainen, Miinau Aalto University4 aut |
700 | 1 | a Rune Karlsen, Steinu NORCE Norwegian Research Centre4 aut |
700 | 1 | a Figueira Silva, Cláudio4 aut |
700 | 1 | a Cerasoli, Sofia4 aut |
700 | 1 | a Pierre, Jon4 aut |
700 | 1 | a Tanlr Kaylkçl, Emineu Karadeniz Technical University4 aut |
700 | 1 | a Halabuk, Andreju Slovak Academy of Sciences4 aut |
700 | 1 | a Tunc Gormus, Esrau Karadeniz Technical University4 aut |
700 | 1 | a Fluit, Franku Wageningen University4 aut |
700 | 1 | 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 |
700 | 1 | a Kycko, Marlenau University of Warsaw4 aut |
700 | 1 | a Udelhoven, Thomasu University of Trier4 aut |
700 | 1 | a Verrelst, Jochemu University of Valencia4 aut |
710 | 2 | a Wageningen Universityb University of Valencia4 org |
773 | 0 | t Biogeosciencesg 21:2, s. 473-511q 21:2<473-511x 1726-4170 |
856 | 4 | u http://dx.doi.org/10.5194/bg-21-473-2024x freey FULLTEXT |
856 | 4 8 | u https://lup.lub.lu.se/record/3e992127-94a6-4bec-a80a-bfb0980cf4e4 |
856 | 4 8 | u https://doi.org/10.5194/bg-21-473-2024 |
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