A semianalytical model developed to estimate the Secchi disk depth (ZSD) was used in eutrophic-to-hypereutrophic reservoirs (Ibitinga, Ibi, and Barra Bonita, BB) placed in the cascade system of the Tietê River, Brazil. The model was evaluated using the simulated remote sensing reflectance based on the Ocean and Land Color Instrument/Sentinel-3A and the Operational Land Imager/Landsat-8 from both reservoirs. Three quasianalytical algorithm (QAA) versions (QAAv5, QAAM14, and QAAW16) were evaluated to derive the absorption and backscattering coefficients, and then used for ZSD retrieval. For BB, where the chlorophyll-a concentration exceeded 200 mg m − 3, the model based on QAAv5 showed high uncertainties while the QAAW16, which was originally parameterized for BB showed better performance regarding the ZSD retrieval (mean absolute percentage errors—MAPE of 22%). However, QAAW16 did not perform satisfactorily for Ibi, which is dominated by colored dissolved organic matter (CDOM). For Ibi, QAAv5 provided the best result with MAPE of 34.60%, followed by QAAM14 with 34.65%. QAA-based ZSD models tend to perform poorly in waters with high concentration of chlorophyll-a possibly due to phytoplankton package effect, whereas the same models may require additional parameterization in waters dominated by CDOM. Landsat-8 data showed significant potential for ZSD retrieval in inland waters.
Cyanobacterial harmful algal blooms (CHABs) is a major water quality issue in surface water bodies because of its scum
and bad odor forming and toxin producing abilities. Terminations of blooms also cause oxygen depletion leading to
hypoxia and widespread fish kills. Therefore, continuous monitoring of CHABs in recreational water bodies and surface
drinking water sources is highly required for their early detection and subsequent issuance of a health warning and
reducing the economic loss. We present a comparative study between a modified quasi-analytical algorithm (QAA) and a
novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland
waters. An extensive dataset, consisting of radiometric measurements, absorption measurements of phytoplankton,
organic matter, detritus, and pigment concentration, was used to optimize the algorithms. The QAA algorithm isolates
the PC signal from the remote sensing reflectance data using a set of radiative transfer equations and retrieves PC
concentration in the water bodies through bio-optical inversion. Validation of the QAA algorithm, using an independent
dataset, produced a mean relative error (MRE) of 34%. For the PC3 algorithm, we propose a coefficient (ψ) for isolating
the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating chlorophyll-a (chla)
absorption at 620 nm to 665 nm enables PC3 to compensate for the confounding effect of chl-a and considerably
increases the accuracy of the PC prediction algorithm. The MRE of prediction for PC3 was 27%. Moreover, PC3
eliminates the nonlinear sensitivity issue of PC algorithms at high range.