Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem

Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exch...

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Published in:Remote Sensing
Main Authors: Azevedo, Olivia, Parker, Thomas C, Siewert, Matthias B, Subke, Jens-Arne
Other Authors: NERC Natural Environment Research Council, Biological and Environmental Sciences, Umea University, orcid:0000-0002-6220-8865, orcid:0000-0002-3648-5316, orcid:0000-0001-9244-639X
Format: Article in Journal/Newspaper
Language:English
Published: MDPI 2021
Subjects:
LAI
SOC
Online Access:http://hdl.handle.net/1893/32839
https://doi.org/10.3390/rs13132571
http://dspace.stir.ac.uk/bitstream/1893/32839/1/remotesensing-13-02571.pdf
id ftunivstirling:oai:dspace.stir.ac.uk:1893/32839
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spelling ftunivstirling:oai:dspace.stir.ac.uk:1893/32839 2023-05-15T12:59:39+02:00 Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem Azevedo, Olivia Parker, Thomas C Siewert, Matthias B Subke, Jens-Arne NERC Natural Environment Research Council Biological and Environmental Sciences Umea University orcid:0000-0002-6220-8865 orcid:0000-0002-3648-5316 orcid:0000-0001-9244-639X 2021-07 application/pdf http://hdl.handle.net/1893/32839 https://doi.org/10.3390/rs13132571 http://dspace.stir.ac.uk/bitstream/1893/32839/1/remotesensing-13-02571.pdf en eng MDPI Azevedo O, Parker TC, Siewert MB & Subke J (2021) Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem. Remote Sensing, 13 (13), Art. No.: 2571. https://doi.org/10.3390/rs13132571 http://hdl.handle.net/11667/180 Will more productive Arctic ecosystems sequester less soil carbon? A key role for priming in the rhizosphere ('PRIME-TIME') Will more productive Arctic ecosystems sequester less soil carbon? A key role for priming in the rhizosphere ("PRIME-TIME") NE/P002722/1 NE/P002722/2 2571 http://hdl.handle.net/1893/32839 doi:10.3390/rs13132571 WOS:000671075500001 2-s2.0-85109825895 1739481 http://dspace.stir.ac.uk/bitstream/1893/32839/1/remotesensing-13-02571.pdf © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). http://creativecommons.org/licenses/by/4.0/ CC-BY Abisko CO2 flux LAI modelling plant functional type SOC vegetation index Journal Article VoR - Version of Record 2021 ftunivstirling https://doi.org/10.3390/rs13132571 2022-06-13T18:42:16Z Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e 3.508 x , p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis. Article in Journal/Newspaper Abisko Arctic Arctic Tundra University of Stirling: Stirling Digital Research Repository Abisko ENVELOPE(18.829,18.829,68.349,68.349) Arctic Remote Sensing 13 13 2571
institution Open Polar
collection University of Stirling: Stirling Digital Research Repository
op_collection_id ftunivstirling
language English
topic Abisko
CO2 flux
LAI
modelling
plant functional type
SOC
vegetation index
spellingShingle Abisko
CO2 flux
LAI
modelling
plant functional type
SOC
vegetation index
Azevedo, Olivia
Parker, Thomas C
Siewert, Matthias B
Subke, Jens-Arne
Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
topic_facet Abisko
CO2 flux
LAI
modelling
plant functional type
SOC
vegetation index
description Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e 3.508 x , p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis.
author2 NERC Natural Environment Research Council
Biological and Environmental Sciences
Umea University
orcid:0000-0002-6220-8865
orcid:0000-0002-3648-5316
orcid:0000-0001-9244-639X
format Article in Journal/Newspaper
author Azevedo, Olivia
Parker, Thomas C
Siewert, Matthias B
Subke, Jens-Arne
author_facet Azevedo, Olivia
Parker, Thomas C
Siewert, Matthias B
Subke, Jens-Arne
author_sort Azevedo, Olivia
title Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
title_short Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
title_full Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
title_fullStr Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
title_full_unstemmed Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem
title_sort predicting soil respiration from plant productivity (ndvi) in a sub-arctic tundra ecosystem
publisher MDPI
publishDate 2021
url http://hdl.handle.net/1893/32839
https://doi.org/10.3390/rs13132571
http://dspace.stir.ac.uk/bitstream/1893/32839/1/remotesensing-13-02571.pdf
long_lat ENVELOPE(18.829,18.829,68.349,68.349)
geographic Abisko
Arctic
geographic_facet Abisko
Arctic
genre Abisko
Arctic
Arctic
Tundra
genre_facet Abisko
Arctic
Arctic
Tundra
op_relation Azevedo O, Parker TC, Siewert MB & Subke J (2021) Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem. Remote Sensing, 13 (13), Art. No.: 2571. https://doi.org/10.3390/rs13132571
http://hdl.handle.net/11667/180
Will more productive Arctic ecosystems sequester less soil carbon? A key role for priming in the rhizosphere ('PRIME-TIME')
Will more productive Arctic ecosystems sequester less soil carbon? A key role for priming in the rhizosphere ("PRIME-TIME")
NE/P002722/1
NE/P002722/2
2571
http://hdl.handle.net/1893/32839
doi:10.3390/rs13132571
WOS:000671075500001
2-s2.0-85109825895
1739481
http://dspace.stir.ac.uk/bitstream/1893/32839/1/remotesensing-13-02571.pdf
op_rights © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
http://creativecommons.org/licenses/by/4.0/
op_rightsnorm CC-BY
op_doi https://doi.org/10.3390/rs13132571
container_title Remote Sensing
container_volume 13
container_issue 13
container_start_page 2571
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