Comparison of sampling schemes for spatial predictionof soil organic carbon in Northern China

Determining an optimal sample size is a key step in designing field surveys,and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon(SOC).Based on 550 soil sampling points in the nearsurface layer(0 to 20 cm)in a representative region of northern China's agro-pastoral ecotone,we studied effects of four interpolation methods such as ordinary kriging(OK),universal kriging(UK),inverse distance weighting(IDW)and radial basis function(RBF)and random subsampling(50,100,200,300,400,and 500)on the prediction accuracy of SOC estimation.When the Shannon's Diversity Index(SHDI)and Shannon's Evenness Index(SHEI)was 2.01 and 0.67,the OK method appeared to be a superior method,which had the smallest root mean square error(RMSE)and the mean error(ME)nearest to zero.On the contrary,the UK method performed poorly for the interpolation of SOC in the present study.The sample size of 200 had the most accurate prediction;50 sampling points produced the worst prediction accuracy.Thus,we used 200 samples to estimate the study area's soil organic carbon density(SOCD)by the OK method.The total SOC storage to a depth of 20 cm in the study area was 117.94 Mt,and its mean SOCD was 2.40 kg/m2.The SOCD kg/(C⋅m2)of different land use types were in the following order:woodland(3.29)>grassland(2.35)>cropland(2.19)>sandy land(1.55).