Abstract
Research on the regional variability of soil organic carbon (SOC) has focused mostly on the influence of the number of soil sampling points and interpolation methods. Little attention has typically been paid to the influence of sampling point discretization. Based on dense soil sampling points in the red soil area of Southern China, we obtained four sample discretization levels by a resampling operation. Then, regional SOC distributions were obtained at four levels by two interpolation methods: ordinary Kriging (OK) and Kriging combined with land use information (LuK). To evaluate the influence of sample discretization on revealing SOC variability, we compared the interpolation accuracies at four discretization levels with uniformly distributed validation points. The results demonstrated that the spatial distribution patterns of SOC were roughly similar, but the contour details in some local areas were different at the various discretization levels. Moreover, the predicted mean absolute errors (MAE) and root mean square errors (RMSE) of the two Kriging methods all rose with an increase in discretization. From the lowest to the largest discretization level, the MAEs of OK and LuK rose from 4.47 and 3.02 g kg−1 to 5.46 and 3.54 g kg−1, and the RMSEs rose from 5.13 and 3.95 g kg−1 to 5.76 and 4.76 g kg−1, respectively. Though the trend of prediction errors varied with discretization levels, the interpolation accuracies of the two Kriging methods were both influenced by the sample discretization level. Furthermore, the spatial interpolation uncertainty of OK was more sensitive to the discretization level than that of the LuK method. Therefore, when the spatial distribution of SOC is predicted using Kriging methods based on the same sample quantity, the more uniformly distributed sampling points are, the more accurate the spatial prediction accuracy of SOC will be, and vice versa. The results of this study can act as a useful reference for evaluating the uncertainty of SOC spatial interpolation and making a soil sampling scheme in the red soil region of China.
Highlights
Soil organic carbon (SOC), as an important soil property, directly affects soil fertility and crop growth, and plays an important role in global carbon cycling [1,2,3]
The primary objectives of this study were to (1) reveal the influence of the sample discretization level on the interpolation uncertainty in revealing SOC variability, and to find out what kind of discretization is beneficial to spatial interpolation with the Kriging method and (2) to find out the differences in responses to the sample discretization level for the different Kriging methods used for SOC interpolation
We comparatively analyzed the influence of sampling point discretization on the SOC variability using two Kriging methods at four discretization levels in the red soil region, China
Summary
Soil organic carbon (SOC), as an important soil property, directly affects soil fertility and crop growth, and plays an important role in global carbon cycling [1,2,3]. Type-based stratified sampling design has been used in regional SOC surveys, such as the soil type-based stratified sampling design, the land use based stratified sampling design, and so on In these type-based stratified designs, the study area is divided into several different subregions with relatively uniform properties, and soil samples are allocated respectively in each subregion according to respective variation characteristics of SOC content. Systematic sampling with a squared grid has been widely applied in soil surveys owing to the advances in geostatistics and geographic information system (GIS) technology [16,17] In this design, a well-designed net is superimposed upon the study area on the computer, and sampling points are placed at the central position of each grid. The soil sampling points allocated by this design are all regularly and uniformly distributed
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