为研究GIS空间插值模拟与土壤类型法估算土壤有机碳（soil organic carbon，SOC）储量的适用性是否一致，该文以山东省3个典型县为例，通过实地取样，采用GIS空间插值模拟和土壤类型法计算0～20 cm SOC储量以及分析土壤有机碳密度（soil organic C density，SCD）空间分布，比较GIS方法与土壤类型法计算县域尺度C储量的差异，验证GIS空间插值模拟的适用性。结果表明：1）依据GIS空间插值和土壤类型法获得的3个典型县0～20 cm土层SOC储量分别为：平邑3.88、3.93 Tg，莱阳3.54、3.57 Tg，禹城2.78、2.86 Tg；估算的平均SCD为：平邑2.2、2.23 kg/m2，莱阳2.08、2.1 kg/m2，禹城2.74、2.82 kg/m2；2）在满足一定采样量的条件下，两种方法在计算县域尺度上C储量时，结果基本一致，但GIS空间插值模拟与土壤类型法相比，更能突显SCD空间分布特征及空间递变规律，更利于分析不同因素对 SCD 空间分布的影响。该文可为缺失土壤类型分类或土地变更频率高区域的C储量计算提供依据。%Soil carbon (C), especially the soil organic carbon (SOC) plays an important role in maintaining food production and reducing greenhouse gas emissions, and thus a better understanding of the spatial variability of SOC stocks is of great significance for the regional ecological environment and development of sustainable agriculture. Previous studies on SOC estimates were more conducted in larger scale, and the results often appeared quite different due to the amount of sampling, calculation methods and the complexity of the regional variation in environmental factors. Simulation with spatial interpolation of geographic information system (GIS) method and soil type method were frequently applied to calculate the SOC stocks, but whether the calculation results of both methods were consistent was inconclusive. In this paper, three typical counties of Shandong Province (Pingyi county, Yucheng county and Laiyang county) were selected as an example. We collected 208 soil profiles from three typical counties, including 71 profile points in Pingyi county, 69 profile points in Laiyang county and 68 profile points inYucheng county, and each soil sampling was collected with 3 duplicates. Through field sampling and analysis, SOC stocks were calculated with spatial interpolation simulation of GIS and soil type method respectively, and spatial distribution of soil organic carbon density (SCD) was analyzed, and further the results of county scale C stocks calculated by the GIS simulation method and soil type calculation method were compared to verify the applicability of GIS spatial interpolation simulation, and then to provide the basis for calculation of the C stocks in soil type classification missing areas or land change high frequency regions. The results indicated that: 1) The calculation results of SOC stocks in 0-20 cm soil layer with the two methods in the three typical counties were 3.88, 3.93, 3.54, 3.57, 2.78, 2.86 Tg respectively, while SCD in 0-20 cm soil layer are 2.2, 2.23; 2.08, 2.1, 2.742, 2.82 kg/m2. Pingyi county ranked first in SOC stocks, and then was Laiyang county, Yucheng county. However, the SCD of Yucheng county was larger than the other two counties. The SCD value in plains was significantly higher than that in hills or plain-hills transition region, caused by differences of terrain and agricultural management. 2) The calculation results of C storage in the county scale with two methods were almost consistent (the maximum relative error did not exceed 3%, and the mean relative error was 1.7%) on condition that the sample met a certain amount but the simulation with spatial interpolation based on GIS method highlighted the SCD spatial distribution characteristics and spatial gradients law, and was more conducive to analyze the impact of different factors on spatial distribution of the SCD. 3) In calculating the county scale distribution of C storage, soil type method ignores a large diversity of soil types and soil details, while GIS spatial interpolation not only considered the spatial variability of soil inside, but also has a advantage of simplicity, easy to operate, and good visibility. The SCD value of the same soil subclasses in the counties makes a great difference, so the weight of main affecting factors should be defined when clustered to a larger area with soil type method.
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