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.

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 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.

Inland waters are very sensitive ecosystems that are mainly affected by pressures and impacts within their watersheds. One of water’s dominant constituents is the suspended particulate matter that affects the optical properties of water bodies and can be detected from remote sensors. It is important to know their composition since the ecological role they play in water bodies depends on whether they are mostly organic compounds (phytoplankton, decomposition of plant matter, etc.) or inorganic compounds (silt, clay, etc.). Nowadays, the European Space Agency Sentinel-2 mission has outstanding characteristics for measuring inland waters’ biophysical variables. This work developed algorithms that can estimate the total concentration of suspended matter (TSM), differentiating organic from inorganic fractions, through the combined use of Sentinel-2 images with an extensive database obtained from reservoirs, lakes and marshes within eastern zones of the Iberian Peninsula. For this, information from 121 georeferenced samples collected throughout 40 field campaigns over a 4-year period was used. All possible two-band combinations were obtained and correlated with the biophysical variables by fitting linear regression between the field data and bands combination. The results determined that only using bands 705 or 783 lead to the obtaining the amount of total suspended matter and their organic and inorganic fractions, with errors of 10.3%, 14.8% and 12.2%, respectively. Therefore, remote sensing provides information about total suspended matter dynamics and characteristics as well as its spatial and temporal variation, which would help to study its causes.

Mar Menor is a Mediterranean Coastal lagoon with high environmental and social value, but has suffered important episodes of contamination in recent years due to heavy rains, sediment dragging and polluting substances mainly from agriculture as well as the entry of mining waste, causing an increase in eutrophication. Water quality variables such as chlorophyll-a concentration [Chl-a] and turbidity, can be studied through its optical properties by remote sensing techniques. In this work, a methodology is proposed for monitoring [Chl-a] and the turbidity of the Mar Menor using Sentinel-2 images. For this purpose, an extensive database of both variables was used consisting of data taken on different dates between 2016 and 2019 at 12 points of Mar Menor. The images were atmospherically corrected using Case 2 Regional Coast Color Processor (C2RCC) version for turbid waters (C2X) to obtain the water surface reflectance. Then several arithmetic relations between database and reflectance bands used in the bibliography for [Chl-a] and turbidity were analyzed. Comparing the results of each one of these relations with the in situ data, decided that the best index for [Chl-a] estimation is the relation (R560 + R705)/ (R560 + R665) with an RMSE = 2.6 mg/m3 and a NRMSE = 9.1 % and for turbidity R705*R705/R490 with an RMSE = 1.5 NTU and a NRMSE= 10.9 %. Finally, by applying these relationships on different dates, thematic maps of [Chl-a] and turbidity of Mar Menor were obtained. One of these images was some days after September 2019 torrential rains, in which a considerable [Chl-a] and turbidity increase was observed