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

[摘要] 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 R² > 0.70, MRE R² > 0.90, MRE b_btnw532; 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.

[学科分类] 地球化学与岩石