## 1 Introduction  
Soil organic matter (SOM) is one of the important components of soil [1]. It has the functions of providing crop nutrients, improving soil cultivability and promoting microbial activities, and it plays an important role in the quality of soil fertility. Due to the long-term influence of natural factors such as parent material, topography, climate and biology, as well as the intervention of human factors such as irrigation, fertilization and farming, the distribution of SOM within a certain area will show corresponding spatial differences [2, 3]. For spatial-variation analysis of regional soil properties, spatial prediction and evaluation are often carried out through soil survey sampling and indoor laboratory assays. The accuracy of spatial prediction is related to the layout and density of sampling points. Theoretically, under the same spatial-distribution mode, the more sampling numbers and the greater the density, the better the spatial-prediction results and the higher the accuracies. Many studies have also confirmed this claim that the spatial-prediction accuracy of SOM exhibits a tendency to increase with the number of sample points [4; 5; 6]. Nevertheless, in practice, too high a sampling density can cause a waste of manpower, material resources and financial resources [7; 8]. Moreover, some studies believe that blindly increasing the number of samples may not always improve the accuracy of spatial prediction [9]. Therefore, setting a reasonable number of sampling points within the region is of great significance for saving sampling costs, improving sampling efficiency and spatial-prediction accuracy and achieving efficient and sustainable utilization of soil resources. In view of the above problems, some scientists have conducted a series of studies on the impact of soil-sampling number on the spatial variability of soil properties in different regions. When performing the spatial prediction of SOM in Guangdong Province, China, [PERSON] et al. found that regression Kriging combined with 800 soil-sampling points could economically and accurately provide spatial-distribution information of SOM, equivalent to collecting 5 samples per 1000 km\({}^{2}\)[10]. [PERSON] et al. showed that during a spatial-variation study against micronutrients and aluminum in the state of Sao Paulo, Brazil, based on standardized variograms, the minimum number of sampling points available for this region is 400 (equivalent to 2 sample points collected per 1000 km\({}^{2}\)) [11]. In addition, they believed that the minimum sampling spacing varied across topographic units. [PERSON] et al. took Fujian Province with an area of 124,000 km\({}^{2}\) in Southeast China as the study area [6]. They found that a reasonable number of samples per 1000 square kilometer were 11,000, 10,000 and 9000, respectively, for SOM sampling studies in different terrain areas such as valley-basin, hill-mountain, and plain-platform. Moreover, this study also showed that the spatial interpolation accuracy of the SOM was more sensitive to the sampling density under relatively simple topographic conditions. [PERSON] et al. in typical coal-mining areas at the Loess Plateau showed that 40 soil-sampling points could guarantee the accuracy of SOM and total nitrogen spatial expression in the region (equivalent to 90,900 sample points collected per 1000 km\({}^{2}\)[5]. Furthermore, in the other two studies, [PERSON] et al. and [PERSON] et al. analyzed the effect of sampling density changes on spatial-interpolation accuracy, using soil copper and SOM, respectively [12; 13]. The results of both suggested that increasing sampling density and selection of reasonable interpolation methods facilitated accurate estimation of spatial variation in soil properties. These studies indicate that the number of reasonable sampling points required in different regional geographical environment conditions were different.