Accurately quantifying soil organic carbon (SOC) stocks in soils is considered necessary and important for studying the soil quality and productivity, modeling the global carbon cycle, and assessing the global climate change. The objectives of this chapter are (1) to evaluate the effects of sampling density and interpolation methods on spatial distribution of SOC density (SOCD) and (2) to estimate the SOC stocks in 0–30, 0–50, and 0–100 cm layer of Tainan rural soils (2192 km2), Taiwan. Ordinary kriging (OK), empirical Bayesian kriging (EBK), and inverse distance weighting (IDW) methods and four sampling densities (n = 7388, 1168, 370, or 77) were used for spatial interpolation. The results indicated that different sampling densities had significant effects on predicting the spatial patterns of SOCD, but no significant difference was found among three interpolation methods. Spatial pattern of SOCD obtained from the highest sampling density appeared to be the most detailed distribution, and the prediction accuracy showed a reducing trend with decreasing sampling density. At least 1 sample per 2 km × 2 km area was suggested. The estimates of SOC stocks in different layers of Tainan soils ranged from 8.03 to 8.08 million tons in 0–30 cm, 11.92 to 12.04 million tons in 0–50 cm, and 20.38 to 20.65 million tons in 0–100 cm.
Part of the book: Geospatial Technology