Journal of Geographical Sciences ›› 2013, Vol. 23 ›› Issue (2): 280-296.doi: 10.1007/s11442-013-1010-1

• Research Articles • Previous Articles     Next Articles

Estimating the spatial distribution of organic carbon density for the soils of Ohio,USA

Sandeep KUMAR1, Rattan LAL2, Desheng LIU3, Rashid RAFIQ4   

  1. 1. Department of Plant Science,Room 248C NPB,BOX 2140C,1110 Rotunda Lane North South Dakota State University,Brookings SD 57007,USA;
    2. Carbon Management and Sequestration Center,School of Environment and Natural Resources,The Ohio State University,2021 Coffey Road,414A Kottman Hall,Columbus OH 43210-1085,USA;
    3. Department of Geography and Department of Statistics,The Ohio State University,Columbus OH43210, USA;
    4. Department of Microbiology&Plant Biology,University of Oklahoma,Norman,OK 73019,USA
  • Received:2012-01-30 Revised:2012-11-30 Online:2013-02-20 Published:2013-02-20


Historical database of National Soil Survey Center containing 1424 geo-referenced soil profiles was used in this study for estimating the organic carbon(SOC)for the soils of Ohio,USA.Specific objective of the study was to estimate the spatial distribution of SOC density(C stock per unit area)to 1.0-m depth for soils of Ohio using geographically weighted regression(GWR),and compare the results with that obtained from multiple linear regression (MLR).About 80%of the analytical data were used for calibration and 20%for validation.A total of 20 variables including terrain attributes,climate data,bedrock geology,and land use data were used for mapping the SOC density.Results showed that the GWR provided better estimations with the lowest(3.81 kg m-2)root mean square error(RMSE)than MLR approach. Total estimated SOC pool for soils in Ohio ranged from 727 to 742 Tg.This study demonstrates that,the local spatial statistical technique,the GWR can perform better in capturing the spatial distribution of SOC across the study region as compared to other global spatial statistical techniques such as MLR.Thus,GWR enhances the accuracy for mapping SOC density.

Key words: geographically weighted regression, multiple linear regression, major land resource areas, root mean square error, soil organic carbon