Soil respiration is a key ecological process during which CO₂ is emitted from the soil and released into the atmosphere. It includes processes such as soil microbial respiration, root respiration, and respiration of heterotrophic animals. The regional soil carbon flux cannot be accurately estimated using traditional random sampling methods, because of its strong spatial heterogeneity. Because multi-point sampling involves massive manpower and equipment costs, it is crucial to determine the appropriate number and the distribution of sampling positions to include in studies estimating regional soil carbon flux. As a complex ecological process, soil respiration is not only affected by environmental factors such as soil temperature and humidity but also by biological factors such as vegetation, microorganisms, and land usage. Because of the correlation between soil respiration and soil moisture, we propose a spatial sampling strategy based on soil moisture distribution characteristic (SMTC) for use in the estimation of fine-scale regional soil carbon flux. Regional soil moisture data are collected using densely deployed sensors nodes, and the monitored area is divided into several sub-regions according to the spatial distribution of the soil moisture data. Then, the optimal number of sampling positions in each sub-region is calculated using the Hammond McCullagh method. As a result, the optimal sampling strategy of the whole monitoring area is determined. We simultaneously applied the SMTC method, random sampling strategy, and uniform sampling strategy to estimate the regional soil carbon flux. In the experiment, we determined that 23 sampling points would be required to measure soil carbon flux in the monitored area, according to the SMTC method. In the same experimental environment, 23 sampling points were selected using a random sampling strategy, and 25 sampling points arranged in a 5 m×5 m grid pattern were selected using a uniform sampling strategy. Regional soil carbon flux is determined via interpolation using the Kriging method based on the measurements taken at all sampling points by using each strategy described above. The experimental results show that SMTC performs better than the other two sampling strategies. The mean squared errors of SMTC, random sampling strategy, and uniform sampling strategy were 8.78%, 13.32%, and 11.56%, respectively. Furthermore, the SMTC method also produced the smallest mean squared error among these three strategies. The SMTC strategy takes the variation of the soil carbon flux among various sub-regions into account, which leads to a better correlation between sampling positions and the distribution of soil carbon flux. Using the SMTC strategy, more sampling points are selected in regions where the soil carbon flux is strongly heterogeneous, allowing the heterogeneity to be captured more fully, and allowing the estimation error to be reduced. In addition, it allows for the use of fewer sampling points in regions of weak heterogeneity. Thus, the SMTC sampling strategy can be used for fine-scale regional soil carbon flux estimation, needing comparatively fewer sampling points because of its strategy of setting each sampling point in a more optimal position than traditional methods.