An estuarine-tuned quasi-analytical algorithm (QAA-V): assessment and application to satellite estimates of SPM in Galveston Bay following Hurricane Harvey

• Published in: Biogeosciences Vol. 15; no. 13; pp. 4065 - 4086
• Main Authors: Joshi, Ishan D., D'Sa, Eurico J.
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 04.07.2018; Copernicus Publications
• Subjects: Absorption; Absorption coefficient; Absorptivity; Algorithms; Backscatter; Backscattering; Brackishwater environment; Case studies; Coastal environments; Coastal water quality; Coastal waters; Color; Color imagery; Computer simulation; Data processing; Dynamics; Empirical analysis; Empirical models; Estuaries; Estuarine environments; Estuarine water; Flooding; Floods; Floodwater; Hurricanes; Image processing; Image quality; Imagery; In situ measurement; Mathematical models; Methods; Ocean color; Ocean colour; Oceans; Particulate matter; Reflectance; Remote sensing; Rivers; Satellite imagery; Satellite observation; Satellites; Sensors; Spaceborne remote sensing; Suspended particulate matter; Water absorption; Water quality; Water quality assessments; Galveston Bay
• ISSN: 1726-4189; 1726-4170; 1726-4189
• DOI: 10.5194/bg-15-4065-2018

The standard quasi-analytical algorithm (Lee et al., 2002) was tuned as QAA-V using a suite of synthetic data and in situ measurements to improve its performance in optically complex and shallow estuarine waters. Two modifications were applied to the standard QAA: (1) the semi-analytical relationship for obtaining remote sensing reflectance just below the water surface as a function of absorption and backscattering coefficients was updated using Hydrolight® simulations, and (2) an empirical model of the total non-water absorption coefficient was proposed using a ratio of green to red bands of an ocean color sensor, which is known to work well in various inland and estuarine environments. The QAA-V-derived total absorption and backscattering coefficients, which were evaluated in a variety of waters ranging from highly absorbing and turbid to relatively clear shelf waters, showed satisfactory performance on a Hydrolight-simulated synthetic dataset (R2 > 0.87, MRE < 17 %), an in situ estuarine and nearshore dataset (R2 > 0.70, MRE < 35 %), and the NOMAD (R2 > 0.90, MRE < 30 %). When compared to the standard QAA (QAA-v6), the QAA-V showed an obvious improvement with ∼ 30–40 % reduction in absolute mean relative error for the Hydrolight-simulated synthetic and in situ estuarine and nearshore datasets, respectively. The methodology of tuning QAA was applied to the VIIRS ocean color sensor and validation results suggest that the proposed methodology can also be applied to other ocean color and land-observing sensors. The QAA-V was also assessed on VIIRS imagery using a regional relationship between suspended particulate matter (SPM) and particulate backscattering coefficient at 532 nm (bbtnw532; R2 = 0.89, N = 33). As a case study, the QAA-V processing chain and VIIRS imagery were used to generate a sequence of SPM maps of Galveston Bay, Texas following the unprecedented flooding of Houston and the surrounding regions due to Hurricane Harvey in August 2017. The record discharge of floodwaters through two major rivers into the bay resulted in very high SPM concentrations over several days throughout the bay, with wind forcing additionally influencing its distribution into the coastal waters of the northern Gulf of Mexico. The promising results of this study suggest that the application of QAA-V to various ocean color and land-observing satellite imagery could be used to assess the bio-optical state and water quality dynamics in a variety of coastal systems around the world.