Please use this identifier to cite or link to this item:
http://hdl.handle.net/1893/32839
Appears in Collections: | Biological and Environmental Sciences Journal Articles |
Peer Review Status: | Refereed |
Title: | Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem |
Author(s): | Azevedo, Olivia Parker, Thomas C Siewert, Matthias B Subke, Jens-Arne |
Keywords: | Abisko CO2 flux LAI modelling plant functional type SOC vegetation index |
Issue Date: | Jul-2021 |
Date Deposited: | 5-Jul-2021 |
Citation: | 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 |
Abstract: | 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. |
DOI Link: | 10.3390/rs13132571 |
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/). |
Licence URL(s): | http://creativecommons.org/licenses/by/4.0/ |
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remotesensing-13-02571.pdf | Fulltext - Published Version | 1.52 MB | Adobe PDF | View/Open |
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