Satellite remote sensing data have been used for water quality mapping, but accurate water reflectance retrieval is dependent on multiple procedures, such as atmospheric and adjacency corrections. For the latter, physical-based methods are used to minimize the adjacency effects caused by neighboring land targets close to water pixels, and implementation requires atmospheric and environmental parameters, such as aerosol optical depth and horizontal range (i.e., distance in meters) of the adjacency effect (${\mathrm{H}}_{\mathrm{Adj}}$). Generally, the ${\mathrm{H}}_{\mathrm{Adj}}$ is empirically defined by users and can lead to substantial errors in water reflectance when incorrectly used. In this research, a physical-based approach with three empirical methods to determine the ${\mathrm{H}}_{\mathrm{Adj}}$ (fixed, SIMilarity Environment Correction—SIMEC, and Adaptative Window by Proportion—AWP-Inland Water) were used to correct and characterize the adjacency effects in Sentinel-2 images over Brazilian inland waters. An interactive inversion method of the deep blue waveband estimated the aerosol loading for the atmospheric correction procedure. The results of atmospheric and adjacency corrections were validated against in-situ reflectance data. The inverted aerosol loading achieved a good agreement with in-situ measurements, especially at visible wavelengths (Mean Absolute Percentage Error—MAPE for eutrophic (~56%), bright (~80%), and dark (~288%) waters). The adjacency correction performance was near similar between the SIMEC and AWP-Inland Water methods in eutrophic and bright waters (MAPE difference < 3%). However, only the AWP-Inland Water method provided a smaller error (MAPE ~53%) for dark waters compared to the fixed (~108%) and SIMEC (~289%) methods, which shows how critical ${\mathrm{H}}_{\mathrm{Adj}}$ parametrization is for low water reflectance values. Simulations of different atmospheric and adjacency effects were performed, and they highlighted the importance of adjacency correction under aerosol loading higher 0.1, which is a typical aerosol loading in a dry climate season, and over extremely dark, low-reflectance waters. This paper contributes to further understanding adjacency effects in medium spatial resolution imagery of inland waters using a physical-based approach including the uncertainties in ${\mathrm{H}}_{\mathrm{Adj}}$ determination.

The aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance ($R_{rs}$), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the $R_{rs}$ spectra provided by PRISMA L2C products—and those derived with ACOLITE, in the case of OLCI—as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m⁻³) and OLCI-derived (MdAE = 3.93 mg.m⁻³) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m⁻³). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m⁻³). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m⁻³) and lower error for chl-a (MdAE = 23.21 mg.m⁻³). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.

Detecting and quantifying cyanobacteria algal bloom occurrence plays an important role in preventing public health risks and understanding aquatic ecosystem dynamics. Satellite remote sensing has been used as an important data source to estimate cyanobacteria biomass based on pigment concentration. Phycocyanin (PC) is a unique pigment of inland water cyanobacteria and has been widely used as a proxy for cyanobacteria algal biomass. Based on the PC absorption feature around 620 nm, scientific efforts have been made to develop bio-optical models for orbital satellite observations, but proposed PC models limit the retrievals at different concentration ranges and depend on empirical models calibrated for specific aquatic environments. This study proposes a hybrid machine learning approach for PC retrieval that efficiently adopts the optimal algorithm for specific PC concentration ranges. An in-situ dataset of 165 samples was collected between November 2020 and December 2021 to support full training and validation of the proposed method. First, a Random Forest algorithm was applied to classify PC-low-concentration waters (0 - ∼14 mg.m^-3) and PC-high-concentration waters (∼14.1 - 300 mg.m^-3). Then, for each defined class, an individual PC estimation algorithm was calibrated. The final PC-hybrid model was applied to atmospherically corrected Sentinel-3/OLCI imagery derived by three approaches (L2-WFR, 6SV, and ACOLITE). The PC hybrid-model performance was evaluated by comparing the estimated PC concentration from satellite and in situ measurements. The hybrid PC model estimates (median symmetric accuracy (ζ) = 25.35%) outperformed the individual PC algorithms calibrated for the entire range of PC concentration, proving the practical applicability for quantifying PC concentration in optically dynamic waters.