Chlorophyll-a　(Chla)　and　total　suspended　solid　(TSS)　concentrations　are　important　parameters　for　water　quality　assessment,　and　in　recent　years,　machine　learning　has　been　shown　to　have　great　potential　in　this　field.　However,　current　water　quality　parameter　inversion　models　lack　interpretability　and　rarely　consider　the　morphological　characteristics　of　the　spectrum.　To　address　this　limitation,　we　used　Sentinel-3　OLCI　data　to　construct　an　interpretable　CatBoost　model　guided　by　spectral　morphological　characteristics　for　remote　sensing　monitoring　of　Chla　and　TSS　along　the　coast　of　Fujian.　The　results　show　that　the　coastal　waters　of　Fujian　Province　can　be　divided　into　five　clusters,　and　the　areas　of　different　clusters　will　change　with　the　alternation　of　seasons.　Clusters　2　and　4　are　the　main　types　of　coastal　waters.　The　CatBoost　model　combined　with　spectral　feature　engineering　has　a　high　accuracy　in　predicting　Chla　and　TSS,　among　which　Chla　is　slightly　better　than　TSS　(R2　=　0.88,　MSE　=　8.21,　MAPE　=　1.10　for　Chla　predictions;　R2　=　0.77,　MSE　=　380.49,　MAPE　=　2.48　for　TSS　predictions).　We　further　conducted　an　interpretability　analysis　on　the　model　output　and　found　that　the　combination　of　BRI　and　TBI　indexes　composed　of　bands　such　as　b8,　b9,　and　b10　and　the　fluctuation　of　spectral　curves　will　have　a　significant　impact　on　the　prediction　of　model　output.　The　interpretable　CatBoost　model　based　on　spectral　morphological　features　proposed　in　this　study　can　provide　an　effective　technical　means　of　estimating　the　chlorophyll-a　and　total　suspended　particulate　matter　concentrations　in　the　coastal　areas　of　Fujian.