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Predicting the spatial variation of the soil organic carbon pool at a regional scale

Umakant Mishra, Rattan Lal, Desheng Liu and Marc Van Meirvenne UGent (2010) SOIL SCIENCE SOCIETY OF AMERICA JOURNAL. 74(3). p.906-914
abstract
Estimates of soil organic C (SOC) storage and their variability at various spatial scales are essential to better understand the global C cycle, estimate C sink capacity, identify effective C sequestration strategies, and quantify the amount of SOC sequestered during a specific period of time. This study used a geographically weighted regression (GWR) approach to predict the SOC pool at a regional scale. The GWR considers varying relationships between the SOC pool and environmental variables across the study area. The range of the variogram of SOC observations was used to define a search radius in the GWR. Terrain attributes, climate data, land use data, bedrock geology, and normalized difference vegetation index data were used to predict the SOC pool for seven states in the midwestern United States. The prediction accuracy of this SOC pool map was compared with the multiple linear regression (MLR) and regression kriging (RK) approaches. Higher contrast and wider variability (1.73-39.3 kg m(-2)) of the SOC pool were predicted with lower global prediction errors (mean estimation error = -0.11 kg m(-2), RMSE = 6.40 kg m(-2)) in GWR compared with the other approaches. A relative improvement of 22% over MLR and 2% over RK was observed in SOC prediction. The total SOC pool to the 0.5-m depth was estimated to be 6.22 Pg. The results suggest that the GWR approach is a promising tool for regional-scale SOC prediction.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
CLIMATE-CHANGE, REGRESSION, TERRAIN ATTRIBUTES, LANDSCAPE, VARIABILITY, EMISSIONS, ELEVATION, BULK, OHIO
journal title
SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
Soil Sci. Soc. Am. J.
volume
74
issue
3
pages
906 - 914
Web of Science type
Article
Web of Science id
000277500600022
JCR category
SOIL SCIENCE
JCR impact factor
1.866 (2010)
JCR rank
13/32 (2010)
JCR quartile
2 (2010)
ISSN
0361-5995
DOI
10.2136/sssaj2009.0158
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1146506
handle
http://hdl.handle.net/1854/LU-1146506
date created
2011-02-11 09:50:39
date last changed
2011-02-18 10:47:41
@article{1146506,
  abstract     = {Estimates of soil organic C (SOC) storage and their variability at various spatial scales are essential to better understand the global C cycle, estimate C sink capacity, identify effective C sequestration strategies, and quantify the amount of SOC sequestered during a specific period of time. This study used a geographically weighted regression (GWR) approach to predict the SOC pool at a regional scale. The GWR considers varying relationships between the SOC pool and environmental variables across the study area. The range of the variogram of SOC observations was used to define a search radius in the GWR. Terrain attributes, climate data, land use data, bedrock geology, and normalized difference vegetation index data were used to predict the SOC pool for seven states in the midwestern United States. The prediction accuracy of this SOC pool map was compared with the multiple linear regression (MLR) and regression kriging (RK) approaches. Higher contrast and wider variability (1.73-39.3 kg m(-2)) of the SOC pool were predicted with lower global prediction errors (mean estimation error = -0.11 kg m(-2), RMSE = 6.40 kg m(-2)) in GWR compared with the other approaches. A relative improvement of 22\% over MLR and 2\% over RK was observed in SOC prediction. The total SOC pool to the 0.5-m depth was estimated to be 6.22 Pg. The results suggest that the GWR approach is a promising tool for regional-scale SOC prediction.},
  author       = {Mishra, Umakant and Lal, Rattan and Liu, Desheng and Van Meirvenne, Marc},
  issn         = {0361-5995},
  journal      = {SOIL SCIENCE SOCIETY OF AMERICA JOURNAL},
  keyword      = {CLIMATE-CHANGE,REGRESSION,TERRAIN ATTRIBUTES,LANDSCAPE,VARIABILITY,EMISSIONS,ELEVATION,BULK,OHIO},
  language     = {eng},
  number       = {3},
  pages        = {906--914},
  title        = {Predicting the spatial variation of the soil organic carbon pool at a regional scale},
  url          = {http://dx.doi.org/10.2136/sssaj2009.0158},
  volume       = {74},
  year         = {2010},
}

Chicago
Mishra, Umakant, Rattan Lal, Desheng Liu, and Marc Van Meirvenne. 2010. “Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale.” Soil Science Society of America Journal 74 (3): 906–914.
APA
Mishra, U., Lal, R., Liu, D., & Van Meirvenne, M. (2010). Predicting the spatial variation of the soil organic carbon pool at a regional scale. SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 74(3), 906–914.
Vancouver
1.
Mishra U, Lal R, Liu D, Van Meirvenne M. Predicting the spatial variation of the soil organic carbon pool at a regional scale. SOIL SCIENCE SOCIETY OF AMERICA JOURNAL. 2010;74(3):906–14.
MLA
Mishra, Umakant, Rattan Lal, Desheng Liu, et al. “Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale.” SOIL SCIENCE SOCIETY OF AMERICA JOURNAL 74.3 (2010): 906–914. Print.