ANALYZING THE RETRIEVAL ACCURACY OF OPTICALLY ACTIVE WATER COMPONENTS FROM SATELLITE DATA UNDER VARYING IMAGE RESOLUTIONS

Abstract

Water quality monitoring has a key role in maintaining a sustainable ecosystem and environmental health. To ensure consistent monitoring, remote sensing provides regular data acquisition with varying spatial resolutions. However, more accurate, and effective solutions can be achieved by integrating remote sensing data with in-situ measurements. This study investigates the integration of in-situ measurements with satellite data, which have different spectral and spatial resolutions, using linear and exponential regression models for four optically active components in the Gulf of Izmit. In this context, Sentinel-2 (S2) and PlanetScope SuperDove (PS) multispectral images, which were acquired on the same date, were used for the comparative analysis of the accurate mapping of chlorophyll-a (Chl-a), turbidity, Secchi disk depth (SDD) and total suspended matter (TSM) water quality parameters combined with simultaneously collected in-situ measurements. The models were evaluated using validation data, along with visual comparison, to assess their accuracy. The results indicate that, overall, exponential models provide more accurate results than linear models, except for the SDD parameter. Furthermore, models created with S2 data demonstrate better performance in retrieving water quality parameters for Chl-a, turbidity, and TSM, with R2 values of 0.71, 0.84, and 0.91, respectively. The linear model created with PS data stands out in the accurately mapping of SDD parameter. Nevertheless, the spatial distribution of these parameters using both satellite dataset exhibits a similar pattern throughout the gulf, which is under threat from significant terrestrial pollution sources, particularly in the eastern part.