Estimating the Optical Properties of Inorganic Matter-Dominated Oligo-to-Mesotrophic Inland Waters

Abstract

Many studies over the years have focused on bio-optical modeling of inland waters to monitor water quality. However, those studies have been conducted mainly in eutrophic and hyper-eutrophic environments dominated by phytoplankton. With the launch of the Ocean and Land Colour Instrument (OLCI)/Sentinel-3A in 2016, a range of bands became available including the 709 nm band recommended for scaling up these bio-optical models for productive inland waters. It was found that one category of existing bio-optical models, the quasi-analytical algorithms (QAAs), when applied to colored dissolved organic matter (CDOM) and detritus-dominated waters, produce large errors. Even after shifting the reference wavelength to 709 nm, the recently re-parameterized QAA versions could not accurately retrieve the inherent optical properties (IOPs) in waterbodies dominated by inorganic matter. In this study, three existing versions of QAA were implemented and proved inefficient for the study site. Therefore, several changes were incorporated into the QAA, starting with the re-parameterization of the empirical steps related to the total absorption coefficient retrieval. The re-parameterized QAA, QAA OMW showed a significant improvement in the mean absolute percentage error (MAPE). MAPE decreased from 58.05% for existing open ocean QAA (QAA Lv5 ) to 16.35% for QAA OMW . Considerable improvement was also observed in the estimation of the absorption coefficient of CDOM and detritus from a MAPE of 91.05% for QAAL v5 to 18.87% for QAA OMW . The retrieval of the absorption coefficient of phytoplankton (a φ ) using the native form of QAA proved to be inaccurate for the oligo-to-mesotrophic waterbody due to the low a φ returning negative predictions. Therefore, a novel approach based on the normalized a φ was adopted to maintain the spectral shape and retrieve positive values, resulting in an improvement of 119% in QAA OMW . Further tuning and scale-up of QAA OMW to OLCI bands will aid in monitoring water resources and associated watershed processes.

Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme

“…The TRCS is located in the southeast of Brazil and contains six reservoirs-Barra Bonita (BB, 22 • Figure 1). Among them, BB, BAR, IBI and NAV reservoirs produce more than 90% of hydroelectric energy from the entire cascade (resulting in 763 MW [30]). BAR, IBI and NAV are run-of-river reservoirs, whereas BB is an accumulation reservoir [32].…”

“…The TRCS is located in the southeast of Brazil and contains six reservoirs-Barra Bonita (BB, 22°31'S, 48°32'W), Bariri (BAR, 22° 9'S,48°44'W), Ibitinga (IBI, 21°45'S, 48°59'), Promissão (21°48' S, 49°23'W), Nova Avanhandava (NAV, 21°71'S, 50°12'W) and Três Irmãos (21°38'S, 51°32') ( Figure 1). Among them, BB, BAR, IBI and NAV reservoirs produce more than 90% of hydroelectric energy from the entire cascade (resulting in 763 MW [30]). BAR, IBI and NAV are run-of-river reservoirs, whereas BB is an accumulation reservoir [32].…”

“…The coefficients to retrieve a t in (λ 0 ) can be recalibrated using in situ datasets in step 2 [20,30,51]. We used optimization processing to define the best band combination for computing the χ factor [20,30].…”

“…For this, K d is estimated based on the methods by Lee et al [28], derived from the IOPs (a and b b ) estimated using a re-parameterized version of a quasi-analytical algorithm (QAA [29]). The QAA was tested for the TRCS [20,30] and the results suggested that further re-parameterizations of the QAA are required for more accurate results. The K d model from Lee et al (2013 [28], 2015 [31]) was also evaluated for other inland waters [22,30], which provided the lowest error when compared with other published algorithms to retrieved K d .…”

“…The QAA was tested for the TRCS [20,30] and the results suggested that further re-parameterizations of the QAA are required for more accurate results. The K d model from Lee et al (2013 [28], 2015 [31]) was also evaluated for other inland waters [22,30], which provided the lowest error when compared with other published algorithms to retrieved K d . However, the suitability of Lee's model was never tested for the entire TRCS where the optical composition of surface water varies over reservoirs.…”