## 1 Introduction  
Over the last two decades, Morocco, as a Mediterranean country affected by climate change, has pursued an economic and social policy characterized by numerous development programs such as the policy of dam construction [1]. These hydraulic infrastructuresprovide a variety of services to both humans and the environment by organizing agricultural practice [2,3], as well as ensuring an efficient mobilization of water resources and improving the living conditions and environment of citizens [3]. These dams also provide habitat for fauna and flora and play a very important role in the global carbon cycle and climate change [4,5]. However, they are facing the interannual variability of precipitation and the succession of droughts and floods [6, 7, 8, 9]. In order to monitor the water quality and observe the biophysical and biochemical conditions of the Hassan Addakhil dam and to prevent serious damage from occurring to the ecological system, the Guir-Ziz-Rheris Hydraulic Basin Agency (HBAGZR), in charge of water resources management in the Errachidia region, conducts in situ measurement surveys. The implemented system of in situ measurement (Figure 1) and monitoring is not practical due to its limitations in time and space [10]. It is expensive and has deficiencies that prevent accurate and complete results. Therefore, it is essential to have a complete, accurate, fast and inexpensive monitoring system to follow the water quality of the dam in order to avoid any degradation by applying prompt treatments. Recently, geospatial tools have been widely used for the spatiotemporal monitoring of environmental phenomena [6, 11, 12], especially the monitoring of lake water quality parameters [3, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]. Such application is mainly enabled by the high spatial resolution data [21, 24, 26] as well as the temporal resolution. However, this aspect has always encountered problems due to the lack of appropriate sensors [31, 32]. Moreover, moderate resolution sensors that are characterized by frequent revisit time and high radiometric resolution have been used [31], but the spatial resolution of these sensors does not allow for small lakes [31]. Several works have been carried out using Landsat TM and ETM+ data, but these satellites are limited in terms of revisit time [21, 31] for very frequent monitoring. However, with the availability of new satellites with higher spatial, spectral and temporal resolution, such as Landsat OLI and Sentinel-2, retrieval and mapping of water quality from the satellite orbit has become more accessible. In 2008, [PERSON] et al. [31] conducted a study with the main purpose of monitoring turbidity and colored dissolved organic matter (CDOM) through ETM+ images in lakes in two river basins in southern Finland. The results showed that despite limitations in spectral and radiometric resolution, these images can be an effective and useful tool for water quality monitoring of small lakes (<1 km\({}^{2}\)). [PERSON] and his collaborators [18] conducted a study in Estonia in which they evaluated Sentinel-2 Multispectral Imager (MSI) data in the mapping of different lake water quality parameters  
Figure 1: In situ measurement and monitoring of the Hassan Addakhil dam with (**a**) fieldwork data collection equipment and (**b**) laboratory data analysis. such as chlorophyll a (Chl-a), water color, CDOM and dissolved organic carbon (DOC). Therefore, field data of different parameters were compared to the Sentinel-2 derived band ratio algorithms. The obtained results showed a strong correlation between the Sentinel-2 MSI ratio bands and the different lake water quality parameters such as Chl-a (R\({}^{2}\) = 0.83). In the Czech Republic, a study was carried out by [PERSON] and his collaborators [33]. It aimed at developing a semiempirical model for predicting water quality parameters such as Chl-a and total suspended solids (TSS) by combining Sentinel-2A data and machine learning methods.