Soil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for comprehensive understanding of its distribution and controls. This study explores the influence of various covariates on SOC spatial distribution at both local and continental scales using a deep learning approach. Our findings highlight the significant role of terrain attributes in shaping SOC spatial distribution, with terrain contributing approximately one third of the overall prediction importance at the local scale (1.25 km). At the continental scale, climate is only 1.2 times more important than terrain for predicting SOC concentration, whereas at the local scale, the structural pattern of terrain is 14 and 2 times more important than climate and vegetation indices, respectively. We underscore that terrain attributes, while being integral to SOC distribution at all scales, primarily operate within limited spatial ranges and often in a spatially asymmetric manner. Our analysis presents a nuanced perspective about SOC spatial distribution, which suggests disparate predictors of SOC at local and continental scales. The above insights gained from this study have implications for improved SOC mapping, decision support tools, and land management strategies, aiding in the development of effective carbon sequestration initiatives and enhancing climate mitigation efforts.