The　development　of　remote　sensing　algorithms　has　traditionally　relied　on　satellite　spectra　or　simulated　equivalents　derived　from　in-situ　spectra　to　monitor　inland　water　quality.　However,　such　equivalent　spectra　often　result　in　significant　errors　when　retrieving　chlorophyll-a　(Chl-a)　concentrations　due　to　discrepancies　between　in-situ　and　satellite-derived　spectra.　In　this　research,　the　authors　innovatively　adjusted　the　red-light　component　of　in-　situ　spectra　for　application　in　two　inland　waters,　Dongzhang　Reservoir　and　Jie　Zhukou　Reservoir.　Sentinel-2　multispectral　images　(MSI),　standard　equivalent　spectra　(ES),　and　modified　equivalent　spectra　(MES)　were　utilized　as　input　data　to　assess　models＇　effectiveness　in　terms　of　accuracy,　robustness,　and　generalizability.　The　research　applied　Chl-a　retrieval　models　including　deep　neural　networks　(DNN),　extreme　gradient　boosting　(XGB),　and　conventional　statistical　approaches　with　various　spectral　indices,　such　as　the　red-NIR　method,　the　three-band　method,　and　the　normalized　difference　chlorophyll　index　(NDCI).　The　results　revealed　that　the　MES-based　model　achieved　best　results　in　Chl-a　retrieval　(RMSE　=　2.04　mg/m3)　comparable　to　MSI-based　model　(RMSE　=　2.07　mg/m3)　and　ES-based　model　(RMSE　=　7.71　mg/m3).　Moreover,　MES-based　model　behaved　robustness　and　precision　within　selected　water　bodies　and　temporal　periods.　Notably,　the　integration　of　the　red-NIR　method　with　DNN　was　particularly　effective　in　retrieving　Chl-a　with　higher　accuracy,　robustness,　and　generalizability.　Enhancement　method　to　the　equivalent　spectra　methodology　provided　by　the　research　have　reduced　retrieval　errors　in　retrieving　Chl-a,　and　providing　a　valuable　reference　for　future　model　development　in　this　domain.