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.

Sentinel-2 offers great potential for monitoring water quality in inland and coastal waters. However, atmospheric correction in these waters is challenging, and there is no standardized approach yet, but different methods coexist under constant development. The atmospheric correction Case 2 Regional Coast Colour (C2RCC) processor has been recently updated with the C2X-COMPLEX (C2XC). This study is one of the first attempts at exploring its performance, in comparison with C2RCC and C2X, in inland and coastal waters in the east of the Iberian Peninsula, in retrieving water surface reflectance and estimating chlorophyll-a ([Chl-a]), total suspended matter ([TSM]), and Secchi disk depth (Z_SD). The relationship between in situ Z_SD and Kd_z90max product (i.e., the depth of the water column from which 90% of the water-leaving irradiance is derived) of the C2RCC processors demonstrated the potential of this product for estimating water clarity (r > 0.75). However, [TSM] and [Chl-a] derived from the different processors with default calibration factors were not suitable within the targeted scenarios, requiring recalibration based on optical water types or a shift to dynamic algorithm blending approaches. This would benefit from switching between C2RCC and C2XC, which extends the potential for improving surface reflectance estimates to a wide range of scenarios and suggests a promising future for C2-Nets in operational monitoring of water quality.

The European Water Framework Directive implements the policies to achieve a good ecological status of all European waterbodies. To determine the ecological potential in freshwater environments, abiotic (morphology, physical and chemical variables) and biotics (algae, fishes, etc.) metrics are used. Despite their importance in trophic web, zooplankton was not included as one of the Biological Quality Elements (BQE) to determine the water quality. In the present research, we studied the zooplankton species that can be considered as indicators of trophic status and ecological potential for more than 60 water reservoirs. The data were obtained from more of 300 samples collected during 10 years from reservoirs at Ebro River watershed, which is the largest basin in Spain. According to their physicochemical and biological elements, the trophic status and ecological potential of these reservoirs were established. More than 150 zooplankton species were identified during the study. The results from this research indicate that species that are related with low water quality are: Acanthocyclops americanus, Ceriodaphnia spp., Daphnia cucullata, Daphnia párvula, Diaphanosoma brachyurum, Brachionus angularis, Keratella cochlearis and Phompolyx sulcata. An indicator of moderate quality was Bosmina longirostris, while Daphnia longispina, Ascomorpha ovalis and Ascomorpha saltans were considered as indicators of good water quality. The data obtained suggest that zooplankton species can be used as a valuable tool to determine the water quality status and should be considered, in a near future, as one more of the BQE within the WFD metrics.

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.

The Albufera of Valencia is a coastal lagoon located in the western area of the Mediterranean Sea, in the Iberian Peninsula. It has an area of 23.1 km² and an average depth of only 1 m, with a maximum depth of 1.6 m. This lagoon is the remnants of an original and more extensive wetland of about 220 km² which is now mostly dedicated to rice cultivation. Surface water is supplied through several main and many secondary canals for a total of 64 water entry points and three exit points to the sea. It is difficult to evaluate the residence time due to the lack of reliable measurements of the inflow or outflow, as well as continuous measurements. Between 1988 and 2018, several procedures were used, the results of which are outlined in this document. Overall, a decrease in the inflow during these thirty years was observed and, therefore, it can be concluded that the residence time is increasing. There is a temporal variation during the year due to rainfall and cultivation periods. Likewise, the results found that the natural hydrological zoning of the lagoon causes a spatial heterogeneity with small Northern areas with low residence time of 4.7 days, almost on a weekly basis and large Western extensions with high residence time of 222.9 days. It is impossible to know this information if individual flow measurements are not taken from each of the main watercourses.

The validation of algorithms developed from in situ reflectance to estimate water quality variables has the challenge of atmospheric correction (AC) when applied to satellite images. Estimating water quality variables from satellite images requires an accurate estimation of remote sensing reflectances (Rrs) which vary according to the AC applied. Validation processes for both Rrs and water quality algorithms were carried out, relating the in situ Rrs (convoluted to Sentinel-2-MSI spectral response function) with the satellite Rrs coming from different ACs (C2RCC, C2X, C2XC, and Polymer), and also relating the in situ water quality variable data with estimated water quality variable values, applying the water quality algorithms to the Rrs obtained for each AC. Regarding the Rrs validation results, the best ACs tested in this work were C2XC and Polymer. Regarding the water quality algorithm validation, the best results were also obtained using C2XC and Polymer Rrs. The results demonstrate the usefulness of the water quality algorithms developed from in situ reflectances since they are not specific to an AC and can be used with any processor.