Improving Colored Dissolved Organic Matter (CDOM) Retrievals by Sentinel2-MSI Data through a Total Suspended Matter (TSM)-Driven Classification: The Case of Pertusillo Lake (Southern Italy)

Abstract

Colored dissolved organic matter (CDOM) is a significant constituent of aquatic systems and biogeochemical cycles. Satellite CDOM retrievals are challenging in inland waters, due to overlapped absorption properties of bio-optical parameters, like Total Suspended Matter (TSM). In this framework, we defined an accurate CDOM model using Sentinel2-MSI (S2-MSI) data in Pertusillo Lake (Southern Italy) adopting a classification scheme based on satellite TSM data. Empirical relationships were established between the CDOM absorption coefficient, aCDOM (440), and reflectance band ratios using ground-based measurements. The Green-to-Red (B3/B4 and B3/B5) and Red-to-Blue (B4/B2 and B5/B2) band ratios showed good relationships (R2 ≥ 0.75), which were further improved according to sub-region division (R2 up to 0.93). The best accuracy of B3/B4 in the match-ups between S2-MSI-derived and in situ band ratios proved the exportability on S2-MSI data of two B3/B4-based aCDOM (440) models, namely the fixed (for the whole PL) and the switching one (according to sub-region division). Although they both exhibited good agreements in aCDOM (440) retrievals (R2 ≥ 0.69), the switching model showed the highest accuracy (RMSE of 0.0155 m−1). Finally, the identification of areas exposed to different TSM patterns can assist with refining the calibration/validation procedures to achieve more accurate aCDOM (440) retrievals.

Keywords

• retrieval models
• S2-MSI data
• inland water reflectance
• CDOM
• unsupervised classification