摘要
Spatial distribution of organic carbon in soils is difficult to estimatebecause of inherent spatial variability and insufficient data. A soil-landscape model for a region,based on 151 samples for parent material and topographic factors, was established using a GISspatial analysis technique and a digital elevation model (DEM) to reveal spatial distributioncharacteristics of soil organic carbon (SOC). Correlations between organic carbon and topographicfactors were analyzed and a regression model was established to predict SOC content. Results forsurface soils (0-20 cm) showed that the average SOC content was 12.8 g kg^(-1), with the SOC contentbetween 6 and 12 g kg^(-1) occupying the largest area and SOC over 24 g kg^(-1) the smallest. Also,soils derived from phyllite were the highest in the SOC content and area, while soils developed onpurple shale the lowest. Although parent material, elevation, and slope exposure were allsignificant topographic variables (P < 0.01), slope exposure had the highest correlation to SOCcontent (r = 0.66). Using a multiple regression model (R^2 = 0.611) and DEM (with a 30 m X 30 mgrid), spatial distribution of SOC could be forecasted.
Spatial distribution of organic carbon in soils is difficult to estimate because of inherent spatial variability and insufficient data. A soil-landscape model for a region, based on 151 samples for parent material and topographic factors,was established using a GIS spatial analysis technique and a digital elevation model (DEM) to reveal spatial distribution characteristics ofsoil organic carbon (SOC). Correlations between organic carbon and topographic factors were analyzed and a regression model was established to predict SOC content. Results for surface soils (0-20 cm) showed that the average SOC content was 12.8 g kg-1, with the SOC content between 6 and 12 g kg-1 occupying the largest area and SOC over 24 g kg-1 the smallest. Also, soils derived from phyllite were the highest in the SOC content and area, while soils developed on purple shale the lowest. Although parent material, elevation, and slope exposure were all significant topographic variables (P ＜0.01), slope exposure had the highest correlation to SOC content (r = 0.66). Using a multiple regression model (R2 = 0.611) and DEM (with a 30 m × 30 m grid), spatial distribution of SOC could be forecasted.
基金
Project supported by the National Key Basic Research Support Foundation of China (No. G1999011810)
the Key Innovation Project of Chinese Academy of Sciences (No. KZCX1-SW01-19)
the Frontier Project of the Chinese Academy of Sciences (No. ISSASIP0201
