Correlation of Remotely Sensed Surface Reflectance With Forcing Variables in Six Different Estuaries

Abstract

This study examines the links between estuarine dynamics and longitudinal distributions of remotely sensed reflectance in estuaries. Reflectance at 655 nm from Landsat-8 correlates with in situ measurements of surface turbidity. Images collected from 2013 to 2018 are used to investigate the spatial and temporal characteristics of reflectance distribution in six selected estuaries with different dynamics. The results show that the maximum magnitude (C max ) and location (X max ) of the reflectance are functions of two major forcing variables, the freshwater discharge and tidal stage. Salt wedge estuaries (i.e., the Fraser River, Connecticut River, and Columbia River estuary) are affected strongly by river discharge and are relatively less affected by tidal forcing. In salt wedge estuaries, the C max values along the river channel are correlated with river discharge, while the X max values generally are not. In partially mixed to strain-induced periodic stratification (SIPS) estuaries (i.e., Delaware River) and SIPS-to-well-mixed estuaries (i.e., Gironde estuary), the X max values are correlated with river discharge, but the C max values are not. SIPS-to-well-mixed estuaries (i.e., Gironde estuary) and highly time-dependent salt wedge to well-mixed estuaries (i.e., Merrimack River estuary) are affected by tidal forcing. In these estuaries, the C max values are correlated with tidal stage. The C max values tend to be lower around high tide than low tide. Overall, the results demonstrate that remote sensing observations of ocean color can be utilized to infer subsurface estuarine processes. Satellite ocean color is a potential tool to monitor river discharge and to classify estuaries. Plain Language SummaryEstuaries are dynamic environments where river freshwater mixes with seawater. The relative strength of river flow relative to flow caused by ocean tides determines how and where freshwater and seawater mix in estuaries, which in turn affects various processes important to humans, like biological productivity and diversity, harbor siltation, and transport of pollutants. It is a challenge to classify estuaries on a global scale due to limited in situ observations. However, satellite remote sensing observations are able to provide repeated, snapshot views of surface water color over large spatial scale. In this study, we examine the links between estuarine dynamics and remote sensing observations in six different estuaries using satellite imagery. The observations demonstrate that satellite-derived maximum magnitude and position in surface reflectance, which is related to particle concentration, are correlated with river flow and tidal forcing, which can be used to infer different types of estuaries. Remotely sensed observations can also be useful for inferring the subsurface water mixing and sediment resuspension in estuaries. In addition, the responses of maximum in surface particle concentration and its location to river discharge can be used for satellite-based estimation of river discharge.

Seasonal bias in global ocean color observations

“…Since the launch of the coastal zone color scanner in 1978, satellite observations of R rs (λ) have been used to derive biogeochemical variables (e.g., chlorophyll ([chl], mg m −3 ), spectral particulate backscattering (b bp (λ), m −1 ), spectral particulate absorption (a p (λ) m −1 ), spectral phytoplankton absorption (a ph (λ), m −1 ), and spectral dissolved organic matter absorption [a dg (λ)m −1 )]. These products have subsequently been used to quantify global net primary production [1,2], global carbon export and associated pathways for sinking (e.g., [3]), particulate organic carbon stocks [4,5], suspended particle sizes [6,7], metrics of phytoplankton community composition [8][9][10][11][12], harmful algal blooms [13][14][15], phytoplankton carbon and physiology [16,17], nitrogen fixation [18], river plumes and suspended sediment concentrations [19][20][21], dissolved organic matter concentrations [22,23], metrics of general ecological dynamics [24], and references therein, and metrics associated with climate change [25][26][27].…”

An Algorithm to Estimate Suspended Particulate Matter Concentrations and Associated Uncertainties from Remote Sensing Reflectance in Coastal Environments

“…Remote sensing techniques, such as satellite ocean color remote sensing, provide spatial distributions of surface SPM concentration in natural waters not possible with in-situ tools, with spatial resolution as high as 10 m (e.g., as with the spatial resolution of the Sentinel 2a and 2b satellites) and temporal resolution as high as one hour (e.g., Geostationary Ocean Color Imager, GOCI). Both approaches, however, are most useful when done in synergy, as remote sensing estimates of SPM require ground-truth data to insure they are unbiased, and their estimates are confined to surface water (though sub-surface dynamic may be inferred [1]).…”

Long‐Term Trends in Chesapeake Bay Remote Sensing Reflectance: Implications for Water Clarity

“…While most past studies focused on derived variables, uncertainties were high, as the use of derived variables may mask important particulate, planktonic, and dissolved contributions to long-term trends (Zheng & DiGiacomo, 2017). Rrs values themselves have been used in other estuaries to study general patterns (Tao & Hill, 2019), and ratios are the underlying metric of a major amount of ocean color algorithms (Dierssen, 2010). Thus, the Rrs approach is used in the present study for Chesapeake Bay to further discern change over time.…”