摘要
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 demon strates 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.
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 demon strates 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.
