Abstract

Abstract. 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.

Highlights

  • Urbanization and the associated anthropogenic stressors are of major concern for the ecosystem health and water quality of estuarine environments, cumulatively affecting the coastal and marine ecosystems through estuarine–shelf exchange processes (Haynes et al, 2007; Bricker et al, 2008; Jutterström et al, 2014)

  • A multiband quasi-analytical algorithm tuned for the Visible and Infrared Imaging Radiometer Suite (VIIRS) ocean color sensor (QAA-V) and for estuarine and nearshore waters was proposed

  • Two major changes were applied to the standard QAA (Lee et al, 2002): (1) the coefficients g0 and g1 of a semi-analytical quadratic relationship were updated to obtain u from the Rrs (Eq 1), and (2) a threshold-based empirical model was proposed using the green to red band ratio (GRBR) to estimate the total absorption coefficient at a reference wavelength

Read more

Summary

Introduction

Urbanization and the associated anthropogenic stressors are of major concern for the ecosystem health and water quality of estuarine environments, cumulatively affecting the coastal and marine ecosystems through estuarine–shelf exchange processes (Haynes et al, 2007; Bricker et al, 2008; Jutterström et al, 2014). The effects of water turbidity caused by dissolved and particulate components on physical and behavioral changes in aquatic species have been well reported in the literature (Wang et al, 2008; Kjelland et al, 2015). These water constituents attenuate incoming light, while a fraction of it is backscattered out of water by the water itself and particles. Deciphering Lw (or remote sensing reflectance; Rrs) to separate the individual contributions of optically active components may provide crucial information about the bio-optical state of a water body

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call