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Agricultural runoff and municipal sewage generate excessive nutrient input in estuaries, which disturbs the ecosystem's natural balance. Most monitoring programs require in situ measurements, which are expensive, time-consuming, and lack spatial and temporal resolution. Extensive research focuses on mitigating these costs by minimizing the indicators or using remote sensing tools. One of the currently investigated options is the application of unmanned aerial vehicles (UAVs) data since it can narrow the multi-resolution gap between the in situ and satellite data. As an initial step of such a multi-scaling approach, we focused on testing the applicability of existing algorithms developed for the Sentinel-2 multispectral data (MS) on our hyperspectral (HS) data obtained using UAV. We applied the available algorithms to estimate three water quality (WQ) parameters: Chlorophyll a (Chl a), Colored Dissolved Organic Matter (CDOM), and turbidity (TUR), for the in situ data acquired at the estuary of the River Jadro near the city of Split (Croatia). The higher spectral resolution obtained by HS imaging enabled us to use the specific wavelengths corresponding to the satellite bands for which the initial algorithms were developed. Moreover, we made one synthetic dataset of MS data, obtained by spectral resampling of HS data using spectral response functions for Sentinel 2 sensors given by ESA. By using these corresponding bandwidths, the initial study found medium and poor correlations with the WQ parameters: Chl a (R2=0.48), turbidity (R2=0.07), and CDOM (R2=0.22). Furthermore, all algorithms revealed higher correlations when using HS data compared to synthesized MS data. However, to fortify these results, we need to test more algorithms and compare the results with satellite reflectance data. Moreover, the future goals of this study are to develop new algorithms which could serve as surrogate data for satellite predictions.
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