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Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

Junttila, Sofia LU ; Kelly, Julia LU ; Kljun, Natascha LU orcid ; Aurela, Mika ; Klemedtsson, Leif ; Lohila, Annalea ; Nilsson, Mats ; Rinne, Janne LU ; Tuittila, Eeva-stiina and Vestin, Patrik LU orcid , et al. (2021) In Remote Sensing 13(4).
Abstract
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on... (More)
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes (Less)
Abstract (Swedish)
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on... (More)
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Ecosystem respiration (ER), Footprint analysis, Gross primary production (GPP), Net ecosystem exchange (NEE), Peatland, Sentinel-2, Upscaling
in
Remote Sensing
volume
13
issue
4
article number
818
pages
23 pages
publisher
MDPI AG
external identifiers
  • scopus:85102069675
ISSN
2072-4292
DOI
10.3390/rs13040818
project
CarboScale : Scaling carbon flux from towers to the northern landscape
Upscaling carbon fluxes to a landscape
language
English
LU publication?
yes
id
87cf360a-383f-4bca-8161-7bd5c90655ab
date added to LUP
2021-03-11 10:35:00
date last changed
2024-02-17 16:16:45
@article{87cf360a-383f-4bca-8161-7bd5c90655ab,
  abstract     = {{Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes}},
  author       = {{Junttila, Sofia and Kelly, Julia and Kljun, Natascha and Aurela, Mika and Klemedtsson, Leif and Lohila, Annalea and Nilsson, Mats and Rinne, Janne and Tuittila, Eeva-stiina and Vestin, Patrik and Weslien, Per and Eklundh, Lars}},
  issn         = {{2072-4292}},
  keywords     = {{Ecosystem respiration (ER); Footprint analysis; Gross primary production (GPP); Net ecosystem exchange (NEE); Peatland; Sentinel-2; Upscaling}},
  language     = {{eng}},
  month        = {{02}},
  number       = {{4}},
  publisher    = {{MDPI AG}},
  series       = {{Remote Sensing}},
  title        = {{Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data}},
  url          = {{http://dx.doi.org/10.3390/rs13040818}},
  doi          = {{10.3390/rs13040818}},
  volume       = {{13}},
  year         = {{2021}},
}