Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity
(2024) In Biogeosciences 21(2). p.473-511- 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 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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2024-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Biogeosciences
- volume
- 21
- issue
- 2
- pages
- 39 pages
- publisher
- Copernicus GmbH
- external identifiers
-
- scopus:85184040204
- ISSN
- 1726-4170
- DOI
- 10.5194/bg-21-473-2024
- language
- English
- LU publication?
- yes
- id
- 3e992127-94a6-4bec-a80a-bfb0980cf4e4
- date added to LUP
- 2024-02-29 14:21:21
- date last changed
- 2024-02-29 14:23:17
@article{3e992127-94a6-4bec-a80a-bfb0980cf4e4,
abstract = {{<p>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.</p>}},
author = {{Kooistra, Lammert and Berger, Katja and Brede, Benjamin and Graf, Lukas Valentin and Aasen, Helge and Roujean, Jean Louis and Machwitz, Miriam and Schlerf, Martin and Atzberger, Clement and Prikaziuk, Egor and Ganeva, Dessislava and Tomelleri, Enrico and Croft, Holly and Reyes Muñoz, Pablo and Garcia Millan, Virginia and Darvishzadeh, Roshanak and Koren, Gerbrand and Herrmann, Ittai and Rozenstein, Offer and Belda, Santiago and Rautiainen, Miina and Rune Karlsen, Stein and Figueira Silva, Cláudio and Cerasoli, Sofia and Pierre, Jon and Tanlr Kaylkçl, Emine and Halabuk, Andrej and Tunc Gormus, Esra and Fluit, Frank and Cai, Zhanzhang and Kycko, Marlena and Udelhoven, Thomas and Verrelst, Jochem}},
issn = {{1726-4170}},
language = {{eng}},
number = {{2}},
pages = {{473--511}},
publisher = {{Copernicus GmbH}},
series = {{Biogeosciences}},
title = {{Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity}},
url = {{http://dx.doi.org/10.5194/bg-21-473-2024}},
doi = {{10.5194/bg-21-473-2024}},
volume = {{21}},
year = {{2024}},
}