As　an　important　indicator　to　measure　the　health　of　terrestrial　ecosystems,　the　Gross　Primary　Productivity　(GPP)　of　vegetation　can　directly　reflect　the　improvement　of　regional　environment.　Therefore,　accurate　estimation　of　vegetation　GPP　changes　is　of　great　significance　to　regional　sustainable　development.　In　this　paper,　a　GPP　estimation　model　using　the　CatBoost　algorithm　integrating　topographic　data　was　developed.　Using　the　vorticity　flux　observation　data　from　China　and　Japan,　this　model　was　applied　to　simulate　the　long　term　GPP　of　Fujian　Province　where　the　topographic　effect　is　significant.　The　results　show　that:　(1)　Terrain　features　are　important　parameters　for　the　estimation　of　GPP　using　machine　learning　methods.　The　accuracy　of　GPP　simulation　results　with　terrain　features　included　is　significantly　improved,　and　the　Root　Mean　Square　Error　(RMSE)　is　decreased　by　16%;　(2)　The　GPP　estimation　model　based　on　CatBoost　has　higher　accuracy　and　stronger　robustness　and　effectively　reduces　the　overestimation　and　underestimation　phenomena　existing　in　traditional　GPP　estimation　models　and　commonly　used　machine　learning　models　(e.g.,　random　forest　and　support　vector　machine).　The　coefficient　of　determination　(R2)　is　0.888,　the　RMSE　is　1.164　gC·　m-2·　day-1,　and　the　Mean　Absolute　Error　(MAE)　is　0.773　gC·　m-2·　day-1;　(3)　The　multi-year　GPP　changes　in　Fujian　Province　simulated　by　the　CatBoost　GPP　estimation　model　are　highly　consistent　with　the　GOSIF　GPP　estimation　results,　indicating　a　more　accurate　GPP　spatial　distribution　in　Fujian　Province.　It　is　found　that　the　mean　GPP　of　Fujian　Province　from　2002　to　2020　was　1　697　gC　·　m-　2　·　a-　1.　The　overall　spatial　distribution　is　characterized　by　＂decreasing　from　southeast　to　northwest＂,　and　the　multi-year　GPP　variation　shows　a　trend　of　＂non-significant　fluctuation　increase＂.　This　study　provides　a　new　method　and　useful　data　for　regional　GPP　estimation　and　ecological　environment　management.　©　2023　Journal　of　Geo-Information　Science.　All　rights　reserved.