Raster resolution has a significant impact on quantifying farmland soil organic carbon (SOC) stock. However, it remains poorly understood to set an appropriate raster resolution when converting vector data to raster data, especially for complex topography and its various landforms. To address this issue, Fujian Province of China was selected as the study area, a large region of complex topography that included three landforms: hill-mountain, valley-basin and plain-platform. Based on 235,309 sampling sites and interpolation of simple kriging combining with terrain information (SK_T), we developed a detailed vector dataset at a scale of 1: 50,000, and then it was converted into a series of raster datasets with different resolutions. Furthermore, we evaluated the accuracy of the conversion and quantified the effect of raster resolution on the estimation of farmland SOC stock in the complex topography and its various landforms by calculating variations in an index value (VIV) for farmland area (AREA), SOC density (SOCD) and SOC storage (SOCS). Results showed that VIV decreased with finer resolution on the whole. However, coarser raster resolutions did not always lead to lower accuracy. Specifically, conversions at 0.1 km resolution for both AREA and SOCS in Fujian's complex topography were acceptable (5% ˂ VIV ≤ 10%), but others were deemed unacceptable at other resolutions (VIV ˃ 10%). For SOCD, it was considered error-free (VIV ≤ 1%) and satisfactory (1% ˂ VIV ≤ 5%) at 0.1 and 0.2 km resolution conversions, acceptable at 0.5, 2.0, 2.5 km but unacceptable at 0.8, 1.0, 1.5 km resolutions. As viewed from its various landforms, hill-mountain was the landform that had the greatest accuracy loss in the conversion for AREA and SOCS, followed by valley-basin and then plain-platform, while the opposite is true for SOCD. The major factors contributing to these differences among landforms were patch density and spatial variation. In addition, resolution setting during conversion and the generalization of maps contributed to the uncertainties of SOC stock estimation based on raster datasets. Therefore, our work provides a guideline for researchers to identify a landform-specific appropriate raster resolution in converting vector to raster for farmland SOC stock estimation in areas like subtropical China or other places with similar complex topographies.