# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 1 Introduction  
The chlorophyll content in surface waters is related to nutrients, such as nitrogen and phosphorus, and serves as one of many indicators of eutrophication. High amounts of these elements in water contribute to the excessive growth of algae, resulting in poor water quality. This topic is especially urgent in surface water intakes used for municipal or industrial consumers. The chlorophyll content in water can be determined by the traditional laboratory method, based on acetone extraction and absorbance measurement [1, 2]. However, to track down changes in the chlorophyll content in rivers or lakes, and to develop concentration maps, a large number of analyses would have to be done; it would be a time-consuming and ineffective approach.  
Satellite images of water surfaces allow for faster and more cost-effective estimation of chlorophyll content in water [3]. Another advantage of remote sensing (teledetection) is a spatial analysis of chlorophyll concentrations from in situ data are collected at particular points [4]. There are, however, a number of problems regarding the remote sensing of chlorophyll faces in surface waters. Water regime [5], as well as the reservoir's depth [6], are associated with the chemical composition and content of biological elements in water. Radiometric and atmospheric corrections also play an important role in the case of satellite data. The correction models, such as ATCOR [7], Second Simulation of a Satellite Signal in the Solar Spectrum (6 SV), Acolite, or Sen2 cor are available and used in these studies. Allthese concerns have prompted the use of empirical [8; 9] or semi-analytical approaches in the development of teledetection methods for monitoring chlorophyll content in inland waters. These methods are based on the physics of the interactions of radiation with water and its compounds [10; 11]. In recent years, neural networks and machine learning methods have been employed in the research on chlorophyll detection [8; 12].  
Empirical methods, though well suited to local conditions [13], require quite a lot of data. On the other hand, semi-analytical methods are more universal, but provide less accurate results. Deep learning methods are still in the early stage of development, and they require a large amount of heterogeneous teaching data; their complexity delays their implementation in small subjects, such as water reservoirs.  
The research focused on the automation of the estimation of turbidity (in nephelometric units, NTU) and chlorophyll content in water taken from shallow dam reservoirs used for drinking purposes. In such reservoirs, water is usually classified as case 2, where optical properties are a function of at least three water components, i.e., phytoplankton, suspended sediments, and colored dissolved organic matter [14]. Such objects, with their firm positions, usually exhibit multiple time observation series of basic water quality indicators (turbidity, chlorophyll). The described remote water quality research favors the empirical method, which, however, must include an optical interaction between the main pollution components, i.e., mineral suspension and phytoplankton, and the shallow tank. Therefore, the authors offer a combination of statistical models that include these elements. The models have been verified using Sentinel 2 data in Lake Dobczyce; the lake serves as the drinking water reservoir for Krakow, Poland.