The　quantification　of　forest　Gross　Primary　Productivity　(GPP)　has　been　the　focus　of　many　scientific　studies　(e.g.　carbon　cycle,　climate　change,　etc.).　Current　remote　sensing-based　models　(i.e.,　the　MODIS　MOD_17　model),　rely　on　the　accurate　meteorological　data,　specific　vegetation　parameter,　the　applicability　and　explicability　of　remote　sensing　data.　In　this　study,　the　original　MODIS　GPP　products　were　validated　and　showed　significant　underestimation　compared　to　the　eddy　covariance　measurements　of　the　four　forest　sites　over　China.　Thus　the　strategy　of　simple　yet　accurate　and　quantitative　simulation　of　carbon　fluxes　was　improved　by　using　Sims　TG　model　which　was　termed　the　Temperature　and　Greenness　(TG)　model　and　included　the　land　surface　temperature　(LST)　product　and　enhanced　vegetation　index　(EVI)　product　from　MODIS.　The　results　indicated　that　Sims　TG　model　was　poor　adaptive　to　tropical　and　subtropical　evergreen　forest　in　China.　The　model　precision　of　deciduous　forest　was　high　but　the　GPP　of　evergreen　forest　are　underestimated　in　Qianyanzhou　and　Xishuangbanna　station　in　summer.　After　the　parameter　was　optimized　in　Sims　TG　model,　the　estimation　accuracy　of　evergreen　forest　GPP　was　improved　to　a　certain　extent　and　the　model　better　adapt　to　dynamic　change　of　forest　GPP　in　China.