## 4 Discussion  
### Monitoring Lake Tana with the Sentinel-2 Satellites  
We used high-resolution Sentinel-2 images to estimate both the cover and biomass of water hyacinth in Lake Tana by calculating NDVI, to assess seasonal dynamics during 2020, and to establish their relation with proxies of biogeochemical water quality parameters, namely Chl-a and TSM. The water hyacinth is an aquatic invasive alien species that has expanded rapidly across the planet in fresh water systems, resulting in serious problems for the affected ecosystems [9; 10]. Therefore, monitoring the temporal and spatial dynamics of the water hyacinth is essential for managers to be able to adequately respond and implement appropriate measures. Remote sensing tools are very useful for water quality management, for implementing near-real-time monitoring strategies, and for mapping invasive species especially in remote environments, areas with limited resources or in very dynamic systems, as the one investigated in this study. Our multi-sensor approach might advance previous studies that aimed to characterize water hyacinth and biogeochemical water quality over inland water masses using coarser-spatial-resolution imagery [26; 27; 37; 44; 63] (Table S1), as the complex variability in the inland water environment was revealed in all the synoptic satellite-derived maps presented in this study (Figures 4-7). In addition, compensation for atmospheric scattering and absorption and for surface reflection at the air-water interface (sunglint) from the signal measured at the TOA is mandatory over coastal and inland water masses [48]. Recognizing the differences in spatial and spectral sampling under various atmospheric, sunlight, and aquatic conditions to create a data record for coastal and inland water quality monitoring is a critical task [48; 64]. This information is vital to ensure a precise near-real-time monitoring with Sentinel-2 satel  
Figure 8: Temporal evolution of **(a)** Chlorophyll-a (Chl-a, mg m\({}^{-3}\)), total suspended matter (TSM, g m\({}^{-3}\)), and water hyacinth cover (ha); **(b)** average water hyacinth biomass (NDVI) and water hyacinth biomass scaled to total cover (NDVI* cover) in Lake Tana during 2020. X-axis represents the days during the year on a linear scale. Thus, major tick marks correspond to the 1 st of each month and data points are shown based on the actual date of sampling, whereas bars are set at the midpoint between two consecutive samplings. lites. Land and water surface reflectance values over Lake Tana are affected by atmospheric conditions; thus, they need to be corrected using appropriate pre-designed algorithms [65]. However, only few studies have used satellite images properly corrected for atmospheric issues when estimating water hyacinth cover or biomass (e.g., [PERSON] et al. (2019) using Sen2 Cor [66]). In this study, we applied two pre-processing processors, the C2 RCC and Sen2 Cor atmospheric correction algorithms, to generate Level-2C products and compared them with Level-1C reflectance values on water and water hyacinth pixels (Figure 3c,d). Over inland waters, TOA products might lead to large uncertainties in satellite data products, thus limiting the detection of subtle variabilities in aquatic ecosystems [48; 64]. The C2 RCC processor includes a sunlight correction in addition to the atmospheric correction [50; 61], a significant advancement when mapping inland waters [48; 64] compared to Sen2 Cor, which is a land-focused processor. Therefore, C2 RCC is optimal for water quality retrieval over complex water masses. Sen2 Cor, aimed at performing over land cover, allowed us to identify and map water hyacinth and characterize its expansion. In order to achieve higher-quality downstream products in freshwater or marine ecosystems, atmospheric correction is mandatory, as already demonstrated in several studies [48; 50; 51; 52; 53; 62; 64]. Few studies in Lake Tana used a similar spatial resolution as the one used here, 10 m, and none with a known atmospheric correction. [PERSON] et al.