Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

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Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data. / Junttila, Sofia; Kelly, Julia; Kljun, Natascha; Aurela, Mika; Klemedtsson, Leif; Lohila, Annalea; Nilsson, Mats; Rinne, Janne; Tuittila, Eeva-stiina; Vestin, Patrik; Weslien, Per; Eklundh, Lars.

In: Remote Sensing, Vol. 13, No. 4, 818, 23.02.2021.

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Junttila, Sofia ; Kelly, Julia ; Kljun, Natascha ; Aurela, Mika ; Klemedtsson, Leif ; Lohila, Annalea ; Nilsson, Mats ; Rinne, Janne ; Tuittila, Eeva-stiina ; Vestin, Patrik ; Weslien, Per ; Eklundh, Lars. / Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data. In: Remote Sensing. 2021 ; Vol. 13, No. 4.

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TY - JOUR

T1 - Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

AU - Junttila, Sofia

AU - Kelly, Julia

AU - Kljun, Natascha

AU - Aurela, Mika

AU - Klemedtsson, Leif

AU - Lohila, Annalea

AU - Nilsson, Mats

AU - Rinne, Janne

AU - Tuittila, Eeva-stiina

AU - Vestin, Patrik

AU - Weslien, Per

AU - Eklundh, Lars

PY - 2021/2/23

Y1 - 2021/2/23

N2 - 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

AB - 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

KW - Ecosystem respiration (ER)

KW - Footprint analysis

KW - Gross primary production (GPP)

KW - Net ecosystem exchange (NEE)

KW - Peatland

KW - Sentinel-2

KW - Upscaling

U2 - 10.3390/rs13040818

DO - 10.3390/rs13040818

M3 - Article

VL - 13

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 4

M1 - 818

ER -