A Machine Learning‐Based High‐Resolution Inventory of Soil Inorganic Carbon Across the Contiguous United States

Abstract Soil inorganic carbon (SIC) is critical for carbon sequestration, infiltration, and climate modeling, yet quantifying its precise spatial distribution at continental scales remains challengings. We introduce a high‐resolution (30 m) CONUS SIC map using machine learning (ML) models trained on the ISRIC World Soil Information Service (WoSIS) database. Integrating point‐based soil data with high resolution climate and land surface variables enhances predictions and reduces uncertainty in unsampled areas over previous polygon‐based inventories. Our SIC Random Forest Regression (RFR) yielded an Root Mean Square Error (RMSE) of 15 kg/m 2 , and the multi‐class classifier had an accuracy of 0.56. Our model estimates that CONUS soils store 77 ± 1.8 Pg of SIC in the top 1 m, a significant increase in SIC storage over prior inventories. We distinguish two SIC storage types: lithogenic carbonates persisting in humid regions and pedogenic carbonates, which characteristically form in arid soils.