## 2 Materials and Methods
### Satellite Images and Atmospheric Correction  
The ESA Sentinel-2 mission (S2) consists of a two-satellite constellation flying in tandem: S2A (launch date: 23 June 2015) and S2B (launch date: 7 March 2017). Each satellite is equipped with the MSI sensor (Multispectral Instrument), which measures Earth's reflected radiance for 13 spectral bands, from visible to near infrared (NIR) and short-wave infrared (SWIR), with spatial resolutions of 10, 20 and 60 m [46]. Table 1 summarizes the principal features of S2-MSI.  
The field campaigns were planned on cloud-free days in which the S2 satellites acquired images over the selected areas [5; 47]. The time gap between the satellite image capture and the field collection campaigns was extended for at least three days, one before and one after the day of image capture, following the methodology of [PERSON] [48]. Images used in this study were downloaded from Copenicus web (European Union) and USGS (United States Geological Survey) archives. The dataset consists of S2-Level 1C imagery (the level 1C (top of atmosphere; TOA) images are georeferenced without atmospheric correction) for both the Ebro and Jucar reservoirs and S2-Level 2A (bottom of atmosphere; BOA) imagery. The level 2A images are georeferenced with atmospheric correction that gives reflectance below the atmosphere for the water bodies and marshes, according to [6]. The images from the Sentinel satellites have the possibility of being processed with a program that was specially developed by ESA for them, the SNAP (Sentinel Application Platform, Brockman Consulting), which is free to download and use.  
The retrieval of water constituents, or its optical properties, is achieved from the water leaving reflectance spectrum, measured at the top of atmosphere and thus requiring a correction for atmospheric effects. This multivariate problem is extremely challenging in optically complex water (or Case-2 waters). The C2 RCC (Case-2 Regional CoastColour), a processor built-in SNAP, is composed of a set of additional neural networks that are trained to perform the inversion of spectrum for the atmospheric correction. Those neural networks are also capable of performing the retrieval of inherent optical properties of the water body [49]. A version developed for turbid water, called C2 RCC for Complex Waters (known as C2X), is also available at SNAP. Sen2 Cor is a Level 2A (L2A) processor whose main purpose is to correct single-date Sentinel-2 Level-1C products from the effects of the atmosphere in order to deliver a Level-2A surface reflectance product [50]. Sen2 Cor processor was designed for vegetation and land, but also provides good results for eutrophic or hypertrophic waters, such as Albufera of Valencia and its surrounding marshes [6; 51]. All images were resampled at 20 m, those corresponding to the Jucar and Ebro reservoirs were corrected using the C2 RCC processors according to their trophic status, while those corresponding to the Albufera's marshes were discharged with the atmospheric correction given by the Sen2 Cor processor. If the water bodies studied vary in their trophic status between oligotrophic and hypereutrophic, then the most appropriate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band Number** & **Central Wavelength (nm)** & **Bandwidth (nm)** & **Spatial Resolution (m)** \\ \hline
1 & 443 & 20 & 60 \\
2 & 490 & 65 & 10 \\
3 & 560 & 35 & 10 \\
4 & 665 & 30 & 10 \\
5 & 705 & 15 & 20 \\
6 & 740 & 15 & 20 \\
7 & 783 & 20 & 20 \\
8 & 842 & 115 & 10 \\
8a & 865  
atmospheric corrector must be chosen. To obtain reflectance data, nine pixels' parameter values were extracted around the coordinates of each measurement point; subsequently, the mean of these 9 values is calculated and the outliers are eliminated using the standard deviation and the mean of the remaining values' results as the spectrum of the measured parameter [52]. With these spectrums, and together with the field data, the database for the spectral indices calibration was built.