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Spatiotemporal averaging resolution of high importance within Earth-observation-based light use efficiency models of gross primary production

Tagesson, Torbern LU ; Senty, Paul ; Diatta, Ousmane ; Cai, Zhanzhang LU orcid ; Wieckowski, Aleksander LU orcid ; Ndiaye, Ousmane and Ardö, Jonas LU orcid (2025) In Science of Remote Sensing 12.
Abstract

Gross primary production (GPP) of the vegetation is the largest carbon exchange process of the global carbon cycle. Currently, within satellite-based remote sensing, GPP is generally modelled using linear light use efficiency models where GPP is related to photosynthetically active radiation absorbed by the green vegetation (APAR). These models work well on moderate to low spatiotemporal averaging resolutions. However, the relationship has been shown to rather follow an asymptotic curve at high spatiotemporal resolutions. The main aim of this study was to investigate at which spatial and temporal scale the GPP-APAR relationship converts from being asymptotic to linear. We used field data and satellite observations from the Dahra field... (More)

Gross primary production (GPP) of the vegetation is the largest carbon exchange process of the global carbon cycle. Currently, within satellite-based remote sensing, GPP is generally modelled using linear light use efficiency models where GPP is related to photosynthetically active radiation absorbed by the green vegetation (APAR). These models work well on moderate to low spatiotemporal averaging resolutions. However, the relationship has been shown to rather follow an asymptotic curve at high spatiotemporal resolutions. The main aim of this study was to investigate at which spatial and temporal scale the GPP-APAR relationship converts from being asymptotic to linear. We used field data and satellite observations from the Dahra field site, a semi-arid savanna grassland in West Africa. At half-hourly to daily temporal resolution an asymptotic relationship gives the better fit, whereas for monthly and weekly data a linear relationship is preferred. A linear relationship was best when working with low spatial resolutions (>two and four Ha for daily and sub-daily GPP estimates, respectively), whereas if working with smaller pixel sizes, the asymptotic relationship was preferred. Hence, if studying GPP variability with satellite sensors such as AVHRR, MODIS, and Sentinel-3, a linear light use efficiency approach works well, whereas if using sensors such as Landsat and Sentinel-2, an asymptotic relationship is recommended. If we aim to improve our understanding of the GPP variability and its role within the carbon cycle, increasing the spatial and temporal resolution of Earth observation-based products is vital. This study provides an initial step toward the impact this may have, and future research across diverse ecosystems and over longer timescales is essential to expand upon these findings.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Earth observation, FAPAR, Gross primary productivity, Light response function, Light use efficiency, Vegetation productivity
in
Science of Remote Sensing
volume
12
article number
100324
publisher
Elsevier
external identifiers
  • scopus:105021094776
ISSN
2666-0172
DOI
10.1016/j.srs.2025.100324
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 The Authors.
id
b00fd311-423c-4222-bdee-5d1bf0db2775
date added to LUP
2025-12-05 10:30:31
date last changed
2025-12-05 10:40:06
@article{b00fd311-423c-4222-bdee-5d1bf0db2775,
  abstract     = {{<p>Gross primary production (GPP) of the vegetation is the largest carbon exchange process of the global carbon cycle. Currently, within satellite-based remote sensing, GPP is generally modelled using linear light use efficiency models where GPP is related to photosynthetically active radiation absorbed by the green vegetation (APAR). These models work well on moderate to low spatiotemporal averaging resolutions. However, the relationship has been shown to rather follow an asymptotic curve at high spatiotemporal resolutions. The main aim of this study was to investigate at which spatial and temporal scale the GPP-APAR relationship converts from being asymptotic to linear. We used field data and satellite observations from the Dahra field site, a semi-arid savanna grassland in West Africa. At half-hourly to daily temporal resolution an asymptotic relationship gives the better fit, whereas for monthly and weekly data a linear relationship is preferred. A linear relationship was best when working with low spatial resolutions (&gt;two and four Ha for daily and sub-daily GPP estimates, respectively), whereas if working with smaller pixel sizes, the asymptotic relationship was preferred. Hence, if studying GPP variability with satellite sensors such as AVHRR, MODIS, and Sentinel-3, a linear light use efficiency approach works well, whereas if using sensors such as Landsat and Sentinel-2, an asymptotic relationship is recommended. If we aim to improve our understanding of the GPP variability and its role within the carbon cycle, increasing the spatial and temporal resolution of Earth observation-based products is vital. This study provides an initial step toward the impact this may have, and future research across diverse ecosystems and over longer timescales is essential to expand upon these findings.</p>}},
  author       = {{Tagesson, Torbern and Senty, Paul and Diatta, Ousmane and Cai, Zhanzhang and Wieckowski, Aleksander and Ndiaye, Ousmane and Ardö, Jonas}},
  issn         = {{2666-0172}},
  keywords     = {{Earth observation; FAPAR; Gross primary productivity; Light response function; Light use efficiency; Vegetation productivity}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Science of Remote Sensing}},
  title        = {{Spatiotemporal averaging resolution of high importance within Earth-observation-based light use efficiency models of gross primary production}},
  url          = {{http://dx.doi.org/10.1016/j.srs.2025.100324}},
  doi          = {{10.1016/j.srs.2025.100324}},
  volume       = {{12}},
  year         = {{2025}},
}