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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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 |
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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 |
_version_ |
1766072235371528192 |