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Issue 11, 2011
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Mapping the organic carbon stocks of surface soils using local spatial interpolator

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Abstract

The largest uncertainties are associated with estimating the soil organic carbon (SOC) stock because of natural soil variability and data scarcity. Thus, a local spatial geostatistical hybrid approach, the geographically weighted regression kriging (GWRK), was used in the present study to overcome some of these uncertainties. This study was designed to estimate the SOC stock (kg C m−2) for the surface 0 to 15 cm depth using the state of Pennsylvania as the study region. A total of 920 soil profiles were extracted from the National Soil Survey Center database and were divided into calibration (80%) and validation (20%) periods. Some soil parameters that include clay content, bulk density (ρb), total nitrogen (TN) content, pH, Ca2+, Na+, extractable acidity (EXACID), and cation exchange capacity (CEC) were used as covariates for estimating the SOC stock. These covariates exhibited spatial autocorrelation (Moran's Index, I = 0.62 to 0.89). Further, residuals of geographically weighted regression were spatially autocorrelated, and hence support the use of the GWRK approach. Validation results concluded that the performance of the GWRK approach was the best with the lowest values of root mean square error, mean estimation error and mean absolute estimation error. The estimated SOC stock for the surface 0 to 15 cm depth ranged from 1.41 to 3.94 kg m−2. Results from this study show that the GWRK captures spatial dependent relationships, and addresses spatial non-stationarity issues, hence this approach improves the estimations of SOC stock.

Graphical abstract: Mapping the organic carbon stocks of surface soils using local spatial interpolator

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Publication details

The article was received on 29 Jun 2011, accepted on 19 Sep 2011 and first published on 19 Oct 2011


Article type: Paper
DOI: 10.1039/C1EM10520E
Citation: J. Environ. Monit., 2011,13, 3128-3135
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    Mapping the organic carbon stocks of surface soils using local spatial interpolator

    S. Kumar and R. Lal, J. Environ. Monit., 2011, 13, 3128
    DOI: 10.1039/C1EM10520E

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