Accurate　prediction　of　soil　organic　carbon　(SOC)　at　fine　scales　is　frequently　limited　by　data　availability　but　fine-scale　maps　of　soil　properties　are　often　of　great　importance　for　local　agricultural　land　management　and　watershed　processes　assessment.　Terrain　attributes　derived　from　elevation　have　been　commonly　used　predictor　variables　in　SOC　mapping.　The　aim　of　this　study　is　to　map　SOC　density　(0–30　cm)　using　hybrid　methods　that　combine　regional　topographic　models　and　a　residual　kriging　procedure　using　local　samples　from　an　area　of　interest　and　evaluate　the　effects　of　different　sampling　designs　for　residual　kriging　on　final　map　quality.　The　regional　topographic　model　was　developed　based　on　light　detection　and　ranging　(lidar)-derived　topographic　covariates　using　random　forest　to　map　SOC　density　on　a　cropland　site　intensively　sampled　for　SOC　(n　=　224).　To　test　ability　to　generate　a　local　calibration　of　this　regional　model,　a　second　intensively　sampled　cropland　site　(n　=　225)　was　used　to　generate　a　reference　SOC　density　map　where　residual　kriging　was　implemented　involving　different　numbers　of　local　samples　(n　=　10–255)　and　different　sample　selection　schemes:　stratified　random　sampling　(SRS)　and　spatial　coverage　sampling　with　close-pairs　inclusions　(SCS+).　To　generate　a　population　of　local　sample　sets,　500　repeats　were　conducted　for　each　scheme　from　the　reference　map.　By　analyzing　the　distribution　of　relative　mean　error　(RME)　of　the　overall　map　for　the　second　site　with　repeated　selection　of　local　samples,　we　found　a　smaller　variation　in　RME　for　SCS+　than　that　for　SRS　for　a　given　sample　size.　However,　the　E(RME)　estimates　for　SCS+　slightly　varied　with　number　of　local　samples　used　while　the　E(RME)　for　SRS　was　stabilized　at　zero　over　that　range　in　local　sampling　intensity.　There　was　negligible　difference　between　the　distributions　of　RME　for　the　two　sampling　schemes　at　individual　points,　except　a　finding　of　somewhat　lower　RME　for　SCS+　based　point　estimates.　These　results　indicate　that　SCS+　can　generate　smaller　variation　in　overall　map　quality　while　SRS　can　provide　unbiased　mean　estimate　for　an　area　of　interest.　This　study　demonstrates　the　utility　of　extrapolating　a　regional　topographic　model　by　use　of　a　limited　number　of　new　samples　for　local　calibration　and　provides　insight　for　practitioners　who　are　interested　in　mapping　soil　carbon　distribution　in　sparsely　sampled　areas　of　interest.　©　2021