【Objective】 Determining the optimal grid resolution for fine-scale characterization of cultivated soil organic carbon (SOC) is essential for developing targeted carbon sequestration and emission reduction strategies in topographically heterogeneous regions. This study examines Fujian Province, a subtropical region featuring complex terrain commonly characterized as "80% mountains, 10% water, and 10% farmland."【Method】 We compiled multi-resolution spatial datasets by integrating comprehensive soil sampling data with environmental covariates, then applied Random Forest modeling to predict SOC spatial patterns.【Result】 Our findings reveal that grid resolution governs the hierarchical importance of environmental predictors. Notably, prediction accuracy exhibits differential sensitivity to resolution across landscape types: SOC predictions for paddy fields and hilly-mountainous terrain demonstrate robustness to resolution variation, whereas those for drylands and plain/terrace landscapes show pronounced sensitivity. Optimal resolutions vary systematically by landform and land use: 0.2 km for the entire province and plain/terrace areas, 0.5 km for hilly-mountainous regions, and 0.1 km for valley/basin zones. Land use-specific optima include 0.1 km for paddy fields, 0.2 km for drylands, and 0.5 km for irrigated land.【Conclusion】 These results highlight the imperative of adopting landscape-specific and land use-tailored grid resolutions to ensure robust SOC prediction in topographically complex terrain.