The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

Freshwater quality maintenance is essential for human use and ecological functions. To ensure this objective, governments establish programs for a continuous monitoring of the inland waters state. This could be possible with Sentinel-2 (S2) and Sentinel-3 (S3), two remote sensing satellites of the European Space Agency, equipped with spectral optical sensors. To determine optimal water quality algorithms applicable to their spectral bands, 36 algorithms were tested for different key variables (chlorophyll a (Chl_a), colored dissolved organic matter (CDOM), colored dissolved organic matter (TSS), phycocyanin (PC) and Secchi disk depth (SDD)). A database of 296 water-leaving reflectance spectra were used, as well as concomitant water quality measurements of Mediterranean reservoirs and lakes of Spain. Two equal data sets were used for calibration and validation. The best algorithms were recalculated using all database and used the following band relations: SDD, R560/R700; CDOM, R665/R490; PC, R705/R665 for S2 and R620, R665, R709 and R779 for S3, using a semi-analytical algorithm; R700 for TSS < 20 mg/L and R783/R492 (S2) or R779/R510 (S3) for TSS > 20 mg/L; and for Chl_a, the maximum (R443; R492)/R560 for Chl_a < 5 mg/m³ and R700/R665 for Chl_a > 5 mg/m³. A preliminary test with a satellite image in a well-known reservoir showed results consistent with the expected ranges and spatial patterns of the variables.

Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in the quantification of the water quality parameters, such as the suspended mineral sediments, dissolved organic matter, and chlorophyll-a concentration. Selecting the most suitable algorithm for each type of water is not a simple matter. One way to make selecting the most suitable water quality algorithm easier on each occasion is by knowing ahead of time the type of water being handled. This approach is used, for instance, in the Lake Water Quality production chain of the Copernicus Global Land Service. The objective of this work is to determine which supervised classification approach might give the most accurate results. We use a dataset of manually labeled pixels on lakes and reservoirs in Eastern Spain. High-resolution images from the Multispectral Instrument sensor on board the ESA Sentinel-2 satellite, atmospherically corrected with the Case 2 Regional Coast Colour algorithm, are used as the basis for extracting the pixels for the dataset. Three families of different supervised classifiers have been implemented and compared: the K-nearest neighbor, decision trees, and support vector machine. Based on the results, the most appropriate for our study area is the random forest classifier, which was selected and applied on a series of images to derive the temporal series of the optical water types per lake. An evaluation of the results is presented, and an analysis is made using expert knowledge.