Multi-Parameter Algorithms of Remote Sensing Reflectance, Absorption and Backscattering for Coastal Waters of the Southern Baltic Sea Applied to Pomeranian Lakes

Abstract

The Pomeranian lakes in Northern Poland and the nearby coastal waters of the Baltic Sea belong to optically complex water bodies characterised by high eutrophication levels. These water types require a local approach when developing bio-optical algorithms that combine the inherent and the apparent properties of seawater. Well-established local algorithms are of great value for understanding and addressing rapid changes in water quality related mostly to human activities in coastal and near-shore zones, as well as in optically similar lakes. Our research analyses the possibility of using the multi-parameter algorithms of absorption a(λ), backscattering bb(λ) and remote sensing reflectance Rrs(λ), originally developed for the coastal waters of the Southern Baltic Sea, for three selected Pomeranian lakes. Our multi-parameter algorithms are based on the input concentrations of the biogeochemical components measured in the lake waters, i.e., chlorophyll a (Chl a), suspended particulate matter (SPM), inorganic suspended particulate matter (SPMinorg), the sum of the surface concentrations of accessory pigments (ΣC) and coloured dissolved organic matter with a wavelength of 400 nm (aCDOM(400)). Rrs(λ) and a(λ) output values were compared with independent measurements of these parameters conducted in the lake waters at 20 sampling stations. Our algorithm output values of bb(λ) were compared to the values obtained based on the algorithm provided by Ficek, previously developed and validated for Pomeranian lakes, at the same stations. The statistical analyses conducted afterwards showed that the multi-parameter algorithms of Rrs(λ) and a(λ) for the Southern Baltic Sea are sufficient to be used for the stations investigated in the aforementioned three lakes. Specifically, the correlations between the bb(λ) values obtained based on the Ficek algorithm and the bb(λ) values obtained using our multi-parameter algorithm reveal a statistical error rate of less than 20%.