东北黑土区是我国最重要的商品粮基地之一。然而，由于长期高强度农业生产，近年来当地土壤侵蚀加剧、土壤有机碳含量（Soil Organic Carbon Content，SOC）持续减少，导致黑土质量下降，粮食产量降低。本研究选取东北黑土区的典型坡耕地，分析SOC的垂直和水平分布特征，并在此基础上构建SOC的空间分布模拟模型，以期为当地的土壤资源保护和农业生产管理提供科学依据。得到的主要结论如下。

（1）随着土壤深度的增大，坡面各样点SOC呈指数递减趋势。根据指数衰减模型拟合结果，坡上各样点的表层SOC显著高于坡下，但2个坡位的SOC剖面变化速率无显著差异。

（2）在水平方向上，0-10 cm和10-20 cm深度SOC无显著差异，空间变异均较弱；尽管2个深度的SOC半方差最优拟合模型不同，但相关参数十分接近，空间相关程度中等。

（3）状态—空间模型对2个深度SOC空间分布的模拟效果明显优于多元线性回归方程，0-10 cm深度最优模拟模型的自变量为孔隙度、黏粒和砂粒含量，10-20 cm深度模型的自变量为容重、黏粒和砂粒含量。

The black soil region of Northeast China is one of the most important commercial grain bases in the country. Owing to long-term intensive agricultural production, however, soil erosion has intensified during recent years, and soil organic carbon content (SOC) has continuously decreased, resulting in the decline of black soil quality and grain yield. In the current study, a typical slope farmland was selected in the black soil region of Northeast China, and the vertical and horizontal distributions of SOC were analyzed, based on which a simulation model for the spatial distribution of SOC was constructed, aiming to provide a scientific basis for the protection of local soil resources as well as the management of agricultural production there. The main conclusions are presented as below.

（1）With the increase of soil depth, the SOC of each slope point showed an exponential decreasing trend. According to the fitting results of the exponential decay model, the surface SOC of each slope point was significantly higher than that of the slope, but there was no significant difference in the change rate of SOC profile between the two slope positions.

（2） In the horizontal direction, there was no significant difference in SOC at the depth of 0-10 cm and 10-20 cm, and the spatial variability was weak. Although the optimal fitting models of SOC semi variance at the two depths were different, the correlation parameters were very close, and the spatial correlation degree was moderate.

（3）The simulation effect of state-space model on the spatial distribution of SOC at the two depths was obviously better than that of multiple linear regression equation. The independent variables of the optimal simulation model at the depth of 0-10 cm were porosity, clay and sand content, and the independent variables of the 10-20 cm depth model were bulk density, clay and sand content.