Chlorophyll-a　(chl-a)　is　an　important　parameter　of　water　quality　and　its　concentration　can　be　directly　retrieved　from　satellite　observations.　The　Ocean　and　Land　Color　Instrument　(OLCI),　a　new-generation　water-color　sensor　onboard　Sentinel-3A　and　Sentinel-3B,　is　an　excellent　tool　for　marine　environmental　monitoring.　In　this　study,　we　introduce　a　new　machine　learning　model,　Light　Gradient　Boosting　Machine　(LightGBM),　for　estimating　time-series　chl-a　concentration　in　Fujian＇s　coastal　waters　using　multitemporal　OLCI　data　and　in　situ　data.　We　applied　the　Case　2　Regional　CoastColour　(C2RCC)　processor　to　obtain　OLCI　band　reflectance　and　constructed　four　spectral　indices　based　on　OLCI　feature　bands　as　supplementary　input　features.　We　also　used　root-mean-square　error　(RMSE),　mean　absolute　error　(MAE),　median　absolute　percentage　error　(MAPE),　and　R-2　as　performance　indicators.　The　results　indicate　that　the　addition　of　spectral　indices　can　easily　improve　the　prediction　accuracy　of　the　model,　and　normalized　fluorescence　height　index　(NFHI)　has　the　best　performance,　with　an　RMSE　of　0.38　mu　g/L,　MAE　of　0.22　mu　g/L,　MAPE　of　28.33%,　and　R-2　of　0.785.　Moreover,　we　used　the　well-known　band　ratio　and　three-band　methods　for　chl-a　estimation　validation,　and　another　two　OLCI　chl-a　products　were　adopted　for　comparison　(OC4Me　chl-a　and　Inverse　Modelling　Technique　(IMT)　Neural　Net　chl-a).　The　results　confirmed　that　the　LightGBM　model　outperforms　the　traditional　methods　and　OLCI　chl-a　products.　This　study　provides　an　effective　remote　sensing　technique　for　coastal　chl-a　concentration　estimation　and　promotes　the　advantage　of　OLCI　data　in　ocean　color　remote　sensing.