Abstract: [Purposes] COD_Mn is an important indicator for assessing the level of organic pollution in water bodies,and its accurate retrieval is crucial for quality monitoring. [Methods] Based on water radiative transfer theory,a multi-component physical inversion model was developed by considering the interactions among optically active substances,including COD_Mn,chlorophyll-a,and suspended solids.Sentinel-2 imagery was used to retrieve COD_Mn concentrations on a pixel-by-pixel basis for surface waters in Guangzhou,and geographic weighted regression (GWR)was applied to analyze the influencing factors. [Findings] The retrieved COD_Mn values showed good agreement with in situ measurements,with a coefficient of determination (R²) of 0.82,a root mean square error (RMSE)of 0.71,and a mean absolute percentage error (MAPE)of 27.30%, indicating satisfactory accuracy.COD_Mn concentrations in Guangzhou were mainly concentrated in the range of 0～2 mg/L,corresponding to Class I surface water quality,suggesting overall good water conditions.Industrial wastewater significantly affected water quality in Nansha,Panyu,and Zengcheng,agricultural activities played a prominent role in Nansha and Zengcheng,and tourism had a stronger impact in Panyu. [Conclusions] The radiative transfer-based multi-component physical inversion approach can accurately characterize the spatial distribution of COD_Mn,providing technical support for refined water quality.

Key words: organic pollution, COD _Mn, physical model, geographically weighted regression, Sentinel-2 imagery