The　global　climate　change　has　led　to　an　increasing　concern　about　the　dynamics　of　the　carbon　storages　of　the　forest　ecosystem.　In　the　past　a　few　decades,　remote　sensing　technology　has　been　frequently　applied　in　measuring　forests＇carbon　storage　on　various　scales.　Nevertheless,　little　work　has　been　done　in　the　estimation　of　the　carbon　storage　of　the　masson＇s　pine,　which　is　a　widespread　pine　species　in　central　and　southern　China.　Therefore,　this　paper　aims　to　develop　a　model　based　on　remote　sensing　technology　to　estimate　the　carbon　storage　of　Pinus　massoniana　forest　using　the　case　of　the　Hetian　Basin　in　County　Changting,　Fujian　province,　southeastern　China.　We　have　carried　out　field　measurements　with　50　sampling　sites　in　November　2010,　in　order　to　acquire　basic　data　of　Pinus　massoniana　forest　in　the　study　area.　Each　sampling　site　has　a　size　of　20×　20m　to　match　the　pixel　size　of　remote　sensing　imagery.　The　filed-acquired　data　were　correlated　with　the　corresponding　vegetation　spectral　information　derived　from　a　near-synchronized　Advanced　Land　Observing　Satellite　(ALOS)　image.　To　examine　whether　the　image　needs　to　be　radiometrically　corrected　before　it　can　be　used　for　the　task,　the　ALOS　image　was　radiometrically　corrected　with　the　ICM　and　IACM　models　respectively.　The　difference　between　the　two　models　lies　in　the　latter　corrects　not　only　solar　illumination　and　terrain　effects　but　also　atmospheric　effects.　Five　vegetation　indices　were　then　derived　from　the　ICM-　and　IACM-corrected　images,　as　well　as　the　original　DN-based　image.　This　is　to　determine　which　index　would　be　most　suitable　for　estimating　the　carbon　storage　of　Pinus　massoniana　forest　in　the　area.　The　five　indices　used　are　the　Normalized　Difference　Vegetation　Index　(NDVI),　the　Difference　Vegetation　Index　(DVI),　the　Perpendicular　Vegetation　Index　(PVI),　the　Soil　Adjusted　Vegetation　Index　(SAVI),　and　the　Soil　Adjusted　Ratio　Vegetation　Index　(SARVI).　By　studying　the　agreement　between　the　field-measured　data　and　the　data　of　the　five　selected　vegetation　indices　derived　from　the　ALOS　image　using　regression　analysis,　the　IACM-corrected　NDVI　data　with　an　exponential　regression　model　appeared　to　have　the　highest　degree　of　agreement　with　the　filed　data　and　thus　was　utilized　to　calculate　the　carbon　storage　of　Pinus　massoniana　forest　in　the　Hetian　Basin　area.　Accuracy　assessment　revealed　that　the　model-estimated　data　were　strongly　correlated　with　field-measured　data,　suggested　by　a　R2　of　0.　979,　a　root　mean　square　error　of　3.　01t/　hm2,　and　a　relative　error　of　-1.　95%.　The　estimated　data　show　a　slight　underestimate　by　2%　when　compared　with　the　measured　data.　This　suggests　that　the　remote-sensing　based　model　can　be　effectively　used　for　estimating　the　carbon　storage　of　the　Pinus　massoniana　forest　in　the　study　area.　Nevertheless,　an　atmosphere　correction　for　the　remote　sensing　image　should　be　carried　out　before　it　can　be　put　in　use,　because　this　study　has　confirmed　that　the　IACM-corrected　data,　which　has　been　radiometrically　corrected　for　atmosphere　effects,　can　significantly　improve　the　precision　of　the　estimated　results.　Based　on　the　retrieved　estimate　model,　the　carbon　storage　of　Pinus　massoniana　forest　in　the　Hetian　Basin　in　2010　was　revealed,　which　was　114.　58×104t　in　total,　with　a　density　of　34.　92t/　hm2.