## 1 Introduction
Satellite images have been used previously to study the water quality and water hyacinth cover in African inland waters [12; 30; 31; 32; 33; 34; 35; 36], and in Lake Tana in particular, during the last few years [26; 27; 37]. To achieve this, various approaches and vegetation indices have been applied to evaluate the cover or biomass of aquatic plants and water hyacinth [12; 34; 35; 38; 39]. Among them, the Normalized Difference Vegetation Index (NDVI), initially developed and applied for terrestrial systems, is often used as a proxy of biomass in aquatic systems as well [12; 40; 41; 42]. NDVI has been demonstrated to provide robust and consistent spatiotemporal time series of vegetation that can be used to monitor change detection in support of accurate seasonal and inter-annual variation, states, and processes [43]. Studies exploring the water hyacinth cover and its expansion rates using remote sensing techniques and data sources have increased throughout the past years worldwide, including Africa. Several works have been conducted in Lake Victoria and other tropical and subtropical water bodies on the African continent [31; 32; 33; 36; 44; 45; 46; 47] (Supplementary Material, Table S1). For Lake Tana, [PERSON] et al. (2020) used Landsat 8 images with 30 m resolution from 2013 to 2017 and applied a supervised classification technique for the detection of water hyacinth expansion in the lake [27]. [PERSON] et al. (2020) used the PlanetScopeimagery to map, also by supervised classification, the distribution of water hyacinth in Lake Tana from 2017 to 2018 [26]. The Moderate-Resolution Imaging Spectroradiometer (MODIS) data product with 250 m spatial resolution monitors the water temperature and turbidity. [PERSON] et al. (2020) combined Sentinel-2 Level-1C Top of Atmosphere (TOA) images at 10 m resolution for classifying water hyacinth and estimating the areal cover from 2015 to 2019 [37]. However, one aspect that is not often considered for inter-comparison between data is the atmospheric correction procedure. Atmospheric correction over inland water environments is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical parameters and evaluation of their spatio-temporal patterns [48]. Robust and consistent atmospheric correction is crucial for the retrieval of aquatic downstream science products from remotely sensed imagery [48]. Most previous studies on water hyacinth monitoring did not report on the atmospheric corrections algorithms applied, if any [34; 35; 49]. For example, the water hyacinth coverage in Lake Tana was processed using Sentinel-2 level 1 (TOA) products by [PERSON] et al. (2020) and using PlanetScope level 1 TOA products by [PERSON] et al. (2020) [26; 37]. Thus, the majority of these studies in Lake Tana used level 1 TOA imagery (no atmospheric correction applied). In this study, we advanced the inspection of the Sentinel-2 imagery by applying the Sen2 Cor and Case 2 Regional Coast Colour processor (C2 RCC) atmospheric corrections to generate level 2 Bottom-of-Atmosphere (BOA) reflectance, as neither of the two processors have been applied yet to this environment. In other water masses, these models have been applied satisfactorily to process from TOA to BOA reflectance, a step required for monitoring emerged intertidal, benthic, or coastal ecosystems [50; 51; 52; 53]. Sen2 Cor was applied to generate the NDVI to detect water hyacinth coverage. In addition, the C2 RCC processor was used to estimate the main water quality parameters, such as TSM and Chl-a [51; 52; 53; 54]. These two parameters are widely used to monitor ecological water quality standards in turbid water bodies such as Lake Tana [55; 56; 57].