## 2 Materials and Methods
### Satellite Data  
In order to examine the dynamics of the water hyacinth, we focused our study on the northeastern part of the lake where this invasive species is currently observed, although additional suitable areas for its growth have been suggested previously in the lake [25]. Sentinel-2A/B cloud-free images (Level-1C and Level-2A) were selected and downloaded from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)), accessed on 28 October 2021) for the study period (2020), one per month (Table 1). A one-month time interval was sufficient to capture general patterns of water hyacinth cover and biomass changes. The spatial resolution is 10-20-60 m depending on the band and spreads over the visible, near-infrared (NIR), and short-wave infrared regions of the electromagnetic spectrum [60]. The images used for the estimation of Chl-a and TSM concentrations were initially resampled using the bilinear resampling method to 10 m spatial resolution within the open-access Sentinel Application Platform (SNAP) software that is suited to Sentinel data products [50]. Level-1C data were converted to Level-2 data products using the Case 2 Regional Coast Colour processor (C2 RCC) as recommended by the European Space Agency and other studies [51, 52, 53, 50, 54, 55, 56, 62]. Figure 1: Map of the Horn of Africa indicating the location of Lake Tana (Ethiopia), indicating its basin (marked with a red line) and its major tributaries (blue lines) within the region of interest. The location of the meteorological stations is also indicated. Figure 2: Monthly average temperature (\({}^{\circ}\)C) (dash-dotted line), and monthly mean precipitation (mm) (solid line) in Lake Tana during 2020. The C2 RCC is developed for optically complex waters. The processor relies on an extensive database of simulated water-leaving reflectance and TOA radiances and it is based on neural network technology trained in extreme ranges of scattering and absorption properties [61]. The inversion of the water signal and satellite signal is performed by neural networks, which determine the water-leaving radiance from the TOA and retrieve inherent optical properties (IOPs) of the water body. A characterization of optically complex waters through its IOPs is used along with the coastal atmospheres to parameterize radiative transfer models for the atmosphere over the water body. The C2 RCC has been improved to cover extreme ranges of scattering and absorption, now using a 5-component bio-optical model. The C2 RCC outputs are surface reflectance, IOPs, and water quality, both chl-a and TSM, and provides the possibility to add additional background information such as salinity, elevation, ozone, temperature, and air pressure. At the time of the research, C2 RCC included two versions: a version called the normal net (C2 RCC), with typical ranges of IOPS, and an extreme net version (CX), for extreme ranges of absorption and scattering. The images were processed here according to the default processing parameters of C2 RCC. According to [PERSON] et al. (2019), who evaluated several atmospheric correction algorithms for inland waters, C2 RCC performed better for optically complex water bodies (i.e., mesotrophic to hypertrophic), similar to Lake Tana [51]. The average concentrations of Chl-a and TSM were calculated using the Statistics Analysis tool in SNAP. All the pixels within the study area identified as a water surface were considered in estimating the average concentration of Chl-a and TSM. Reflectance values of Sentinel-2 data products before (Level-1C TOA) and after atmospheric correction (Level-2A BOA), the latter from both C2 RCC and Sen2 Cor processors, were compared across the visible and NIR bands to evaluate the atmospheric effect on the spectrum's signature.