## 1 Introduction  
Soil organic carbon (SOC), which is an essential nutrient of crop growth and the main carbon source and sink of greenhouse gases, influences agricultural production and global climate change [1; 2; 3; 4; 5]. The identification of the spatial distribution characteristics of SOC contributes to the investigation of the role of SOC in precision agriculture and the carbon cycle of the ecosystem. Digital soil mapping with the aid of easily obtained soil-forming factors, such as terrain, climatic, and vegetation factors, can continuously map the spatial distribution of SOC [6; 7; 8; 9]. However, the spatial characteristic of natural landscape is similar in plains or flat terrain areas, and most traditional soil-forming factors exhibit small spatial variations that prevent them from contributing to the development of soil-landscape models. Thus, the selection of suitable auxiliary variables to complete soil mapping in plain areas is challenging. The traditional measurement of SOC content is based on the laboratory analysis of field soil sampling [10; 11; 12]. The soil data of sampling points are discrete and incapable of providing continuous and complete information regarding the total study area and require extensive time and labor. The spatial variability of SOC through field soil sampling cannot be obtained. Numerous studies on the digital mapping of SOC have been conducted to resolve this issue on the basis of the spatial autocorrelation of soil [13; 14; 15; 16]. Many studies have proven that soil properties exhibit strong spatial dependence between neighboring regions, and trend surface analysis, inverse distance weighted, and geostatistical models have been successfully used in soil mapping [17; 18; 19]. However, such methods merely rely on the correlation among soil sample points, which is limited by the geographical location of sampling points. In other words, traditional geostatistical methods based on geospatial autocorrelation have two limitations, namely, they are locally limited by sampling density [20; 21] and ignore the role of environmental factors, thereby causing the results to be inconsistent with reality [22; 23]. These methods encounter difficulty in describing the spatial distribution characteristics of SOC in complex terrains. In general, the occurrence, formation, and degradation of soil are influenced by the interaction of the surrounding environmental factors for a long period of time. Thus, numerous soil-forming factors have been used to develop soil-landscape models. [PERSON] et al. [4] estimated the SOC distribution by using nine environmental variables (e.g., precipitation, temperature, land use, and elevation) with boosted regression trees. [PERSON] et al. [24] mapped the SOC content through geographically weighted regression using several environmental predictors (e.g., slope, aspect, elevation, land use, and normalized difference vegetation index [NDVI]) in a case study of Heihe River Basin, China. [PERSON] et al. [25] estimated the SOC spatial distribution using a weighted regression approach based on the correlation of environmental variables (NDVI, annual precipitation and average temperature and moisture index). Thus, these demonstrate that large variation in topography creates large variations in climate and other environmental variables related to SOC, leading to strong statistical relationships. However, environmental variations in areas with small topography, such as plains, are small, making the development of the accurate predictions of SOC difficult [26; 27; 28]. The variation of soil properties is a comprehensive result of the long-term interaction of various environmental factors. Thus, responding to the spatial heterogeneity of soil properties through environmental factors with small differences is difficult, especially in small-scale areas where the variation of environmental variables is obscured.