1. Introduction
The Clean Water Act (CWA) authorizes the US Environmental Protection Agency (USEPA) to provide guidance and oversight to states, tribes, and US territories in the development and implementation of water quality standards for the protection of the Nation’s waterways. Under the CWA, the majority of aquatic research by the Agency addresses scientific questions about water quality condition within state waters, estuaries, lakes, and streams. Water quality condition has been traditionally assessed using a suite of indicators which directly relate to the stress of an ecosystem or can serve as indicator of stress. Examples are chlorophyll
a (chl
a) and suspended sediment concentrations, salinity, colored dissolved organic matter (CDOM) and temperature. CDOM serves as a nutrient source and a vector for heavy metals in water (Zhang
et al. [
1]; Heyes
et al. [
2]). CDOM, also known as yellow substance (or gelbstoff), is an important water color parameter for the study of nearshore and estuarine biological processes. Along with chlorophyll
a, the absorption of light by CDOM in the ultraviolet and blue portions of the spectrum makes it an important control on the transfer of solar radiation through the water column which is critical to the structure and function of freshwater and saltwater ecosystems. In the coastal ocean as well as in bays and estuaries, a large portion of CDOM is terrestrial in origin and associated with freshwater (Opsahl and Benner [
3]) which allows its use as an indicator of the input and distribution of terrestrial organic matter in freshwater and brackish water environments.
Several studies have found that detritus and CDOM concentrations are good tracers of salinity (Coble
et al. [
4]; Del Vecchio and Blough [
5]; Vodacek
et al. [
6]). Salinity (along with temperature) determines the density of sea water and controls water column stratification, controls flocculation of particles, and governs the biological, chemical, and physical processes which determine the types and locations of plants and animals in estuarine and nearshore ecosystems. Salinity is also a key factor when monitoring water quality variables (e.g., dissolved oxygen concentration).
On inner continental shelf areas, large salinity variations created during the mixing of higher salinity coastal waters with lower salinity river runoff result in the development of density-driven coastal currents that redistribute biological and chemical substances vertically as well as horizontally across shelf areas (Miller
et al. [
7]). In estuaries, salinity values are used to estimate water residence times and to estimate dissolved organic carbon concentrations (Mannino
et al. [
8]; Del Castillo and Miller [
9]). In all environments, salinity variations strongly influence biogeochemical processes as well as create biological and chemical gradients both horizontally and vertically within the water column. In this paper, salinity (S) is expressed in unitless terms (e.g., S = 35.034) according to the Practical Salinity Scale 1978 (PSS-78). PSS-78 has been considered by the Joint Panel on Oceanographic Tables and Standards as the scale to report salinity data (UNESCO [
10]).
Determining salinities for the global ocean from earth orbiting satellites has been a long-term continuing challenge for the remote sensing community (Lagerloef
et al. [
11]). Attempts to successfully map salinities using remote sensing have ranged from Skylab photography (Lerner and Hollinger [
12]) to microwave radiometer measurements (Blume and Fedors [
13]) and Landsat Thematic Mapper (TM) data (McKeon and Rogers [
14]). The first demonstration of the applicability of using satellite-derived color data to derive estuarine salinity was conducted by Khorram [
15]. This pioneering study found correlations between Landsat TM color bands and surface salinities in the San Francisco Bay Estuary.
Investigators have demonstrated, with various levels of success, the potential of estimating CDOM absorption (
aCDOM) and salinity using remote sensing data (e.g., Miller
et al. [
7]; Siegel
et al. [
16]; Kahru and Mitchell [
17]; Kutser
et al. [
18]; Keith
et al. [
19]; Keith [
20]; Bailey and Werdell [
21]; D’ Sa [
22]; Del Castillo and Miller [
9]; Mannino
et al. [
8]; Shanmugam [
23]; Wang and Xu [
24]; Son
et al. [
25]; Tehrani [
26]; Tehrani
et al. [
27]; Vandermeulen
et al. [
28]) or using neural networks to estimate salinities from relationships between environmental variables (e.g., tides, sea surface temperature, chl
a, stream flow) and ocean color (Urquhart
et al. [
29]; Geiger
et al. [
30]). Most studies have been successful estimating CDOM absorption and salinity in coastal and inner shelf waters (e.g., Mannino
et al. [
8]; D’ Sa [
22]). Successful studies have been conducted to estimate these parameters in lakes (e.g., Kutser
et al. [
18]; Wang and Xu [
24]; Zhu
et al. [
31]). However, only a limited number have been conducted in estuaries (e.g., Keith
et al. [
19]) and there is no proven general or operational salinity algorithm for estuaries and coastal bays (Urquhart
et al. [
30]; Geiger
et al. [
31])
Serious discussions within the remote sensing community have agreed that estimating high-resolution estuarine surface salinities from satellites is of scientific value. These discussions have set the stage for determining salinity on a variety of spatial scales from space-based platforms. For example, there is NASA’s Aquarius mission for determining salinity for the global ocean. On smaller scales, several area-specific algorithms (
i.e., branching algorithms; IOCCG [
32]) have been produced which use CDOM absorptions, derived from the optical properties of local waters, to predict surface salinities of the coastal and estuarine waters of interest. We suggest that the principles, optical characteristics, and geochemical relationships between CDOM absorption and salinity that collectively serve as the basis of these branching algorithms could be integrated into over-arching algorithms, optimized for the range of absorptions and salinities characteristic of estuarine and coastal waters of the US East and Gulf coasts.
In this study, we set out to:
- (1)
Derive a single set of coefficients which can be used in an optical model to estimate the range of CDOM absorptions found in estuarine, inland, and coastal environments along the US East and Gulf Coasts.
- (2)
Create a general salinity model, which uses the derived CDOM absorption coefficients to estimate salinities from oligohaline (salinity < 5) to polyhaline (salinity > 20) waters from New England to the Gulf of Mexico.
Our approaches are to:
- (1)
Use regression analysis to derive a spectrally-based CDOM absorption algorithm based on the relationship between remote sensing reflectances (Rrs) retrieved from in situ optical data and laboratory measured CDOM absorption coefficients.
- (2)
Compare spectrally-derived CDOM absorption coefficients to laboratory measured CDOM absorption coefficients.
- (3)
Use regression analysis to derive a salinity algorithm based on the relationship between laboratory measured CDOM absorption coefficients and in situ salinity values.
- (4)
Compare predicted salinity values to in situ values of salinity.
- (5)
Evaluate these algorithms using in situ radiometric data, laboratory measured CDOM absorption coefficients, and in situ salinity measurements from Narragansett Bay (Rhode Island), New Bedford Harbor (Massachusetts), Neuse River (North Carolina), Pensacola Bay (Florida), Choctawhatchee Bay (Florida), St. Andrews Bay (Florida), St. Joseph Bay (Florida) and Gulf of Mexico inner continental shelf.
- (6)
Using regression analysis, validate the performance of the CDOM and salinity algorithms in Pensacola and Choctawhatchee Bays during summer 2011 using match-ups between Hyperspectral Imager for Coastal Ocean (HICO) spectral data, in situ salinity data, and laboratory measured CDOM absorption coefficients from these estuaries.
- (7)
As an example of the applicability of these algorithms to other satellite sensor data, apply the CDOM absorption and salinity algorithms to a full resolution (300 m) Moderate Resolution Imaging Spectrometer (MERIS) image of the Neuse River estuary to illustrate the spatial distribution of CDOM absorptions and surface salinities within one of the most important commercial seafood nurseries along the US East Coast.
The approach that we used here is based on theoretical work of Bowers
et al. [
33] in which they proposed that a simple band ratio model could be derived to estimate CDOM absorption at 440 nm from the ratio of reflection coefficients (R
1/R
2). Further, salinity could be derived if the relationship between CDOM absorption and salinity is known. Generally, R
1/R
2 is a function of absorption (a) by water (w), particulates (p), and yellow substance or CDOM (y) at two bands. By inversion:
If R
1 is chosen to be in the red end of the spectrum where CDOM is not absorbed, then ay
1 = 0 and
where ay
2 is rewritten as is the product of a specific absorption coefficient (a*
y2) and
g440 is the concentration of CDOM at 440 nm. Solving for
g440:
Bowers
et al. [
33] suggested that at the red wavelengths, with the exception of very turbid waters or plankton blooms, aw
1 > ap
1 and therefore aw
1 + ap
1 ≈ aw
1. If R
2 is chosen in another location in the spectrum where aw is low, then ap
2 > aw
2 and ap
2 + aw
2 ≈ ap
2. With this understanding, Equation (3) simplifies to:
Bowers
et al. [
33] further suggested that Equation (4) would predict a linear relationship between
g440 and the reflectance ratio when one band is red and the other is another color and that ratio will continue to increase as
g440 increased. In theory, the term ap
2/a*
y2 is a constant that is dependent on light absorption by particulates. However, variations in particulate absorption resulted in variations in the intercept of the linear relationship predicted by Equation (4). Bowers
et al. [
33] minimized this unwanted effect by selecting an R
2 band where a*
y2 >> ap
2. This meant choosing a band towards the blue end of the spectrum where CDOM absorption is greatest. In this paper, we use the term
aCDOM440 instead of
g440 to refer to CDOM absorption at 440 nm.
The theory was tested by Binding and Bowers [
34]; Bowers
et al. [
35]; and Bowers
et al. [
36] which showed that the absorption of light by CDOM in marine waters could be derived using a simple band ratio of R
rs and confirmed that the relationship between CDOM absorption and salinity was inversely related over the salinity range of 15–35 found in estuaries and adjoining coastal seas. Additionally, using a Sea-Viewing Wide Field-of View Sensor (SeaWiFS) image over Northern Europe, Binding and Bowers [
35] retrieved CDOM absorption values at 440 nm (
aCDOM440) to calculate and map the distribution of salinities from 16 to 34 along the coast and offshore areas of the Clyde Sea, Scotland. However, with a spatial resolution of 1.1 km, the SeaWiFS image was unable to view regions of lower salinity within adjoining lochs and estuaries. Bowers
et al. [
36] continued to explore the approach of using the linear relationship between
in situ aCDOM440 measurements and spectral band ratios to map surface salinities for the Conwy estuary in North Wales, Great Britain.
The bio-optical models presented here to estimate CDOM absorption and surface salinities were derived from several large datasets acquired by the USEPA during surveys of estuaries, coastal bays, and nearshore waters along the U.S. East and Gulf coasts from 1999 to 2012 (referenced by abbreviations found in
Table 1).
4. Discussion
In order to determine the absorption coefficients needed for developing the CDOM and salinity algorithms, water samples were collected in several estuaries by USEPA field programs and analyzed in laboratory settings according to published, standardized operating procedures. As a part of these protocols, water samples collected from the estuaries in this study were routinely filtered using 0.22 μm Millipore filters or Whatman GF/F 0.7 μm filters. The use of these different pore sizes has raised discussions concerning the comparability of absorption data from the sampled estuaries. A study by Ferrari and Tassan [
51] directly addressed any concerns by comparing the use of Whatman GF/F 0.7 μm and Millipore 0.22 μm filters for chlorophyll, particle, and absorption measurements. Their study found no statistical difference between these filter types and concluded that performance of both filters to be comparable.
We also investigated which wavelength was appropriate, either 412 or 440 nm, for estimating salinity from CDOM absorption in near coastal and estuarine waters along the US East and Gulf coasts. As presented earlier,
aCDOM440 was the wavelength of choice of several remote sensing studies in ocean and nearshore waters. This selection was supported by results obtained by the Ocean Physics Laboratory during the HyCODE 2000 experiment which showed that
aCDOM440 was the only property of the bio-optical and oceanographic parameters measured (e.g., chl
a concentration,
aCDOM440, total particle scattering and total light attenuation) along the New Jersey continental shelf that had a significant relationship with salinity (
R2 = 0.78; Coble
et al. [
4]). However, a search of the scientific literature has also revealed studies that indicated a high correlation existed between
in situ salinities and laboratory measured CDOM absorptions towards UV-blue wavelengths (e.g., 355, 400, 412 nm) in Chesapeake Bay, the Gulf of Mexico, Clyde Sea, and the East China Sea (Haltrin
et al. [
52]; Bowers
et al. [
33]; Ahn
et al. [
53]; D’Sa and DiMarco [
47]; Du
et al. [
54]; Tehrani
et al. [
27]; Bai
et al. [
55]). In this study,
aCDOM412 was chosen as the absorption wavelength for development of the salinity algorithm based on the strong statistical relationship between measured CDOM absorptions at 412 nm and measured salinities in the sampled estuaries. This selection is also appropriate because 412 nm which is one of the more commonly found wavelengths in ocean color satellite sensor data.
The relationship between laboratory measured CDOM absorption values at 412 nm and
in situ salinities showed that, in general, the inverse relationship observed in previous studies was robust across the range of coastal environments in this study. Results showed that salinities exponentially decreased as CDOM absorption increased from salinities ranging from S = 2–33. The relationship also appeared to consist of two trends that may be related to freshwater input and mixing processes characteristic of New England, Mid-Atlantic, and Gulf coast estuaries (
Figure 6a). The first trend consisted of a cluster of data points, between S = 28–3, representative of the low freshwater input into NB from adjoining watersheds. The second trend consisted of an increase in the scatter as salinity decreased and CDOM absorption increased in response to increased freshwater input to coastal and estuarine waters in the mesohaline and oligohaline environments of the NR and northern Gulf Coast estuaries and shelf areas. Using these relationships, we selected spectral data from several overflights of Pensacola Bay (Florida) by the Hyperspectral Imager for the Coastal Ocean (HICO) for algorithm validation purposes. Results showed that the salinity model had a strong fit between measured and predicted values (
R2 = 0.70) and low error in estimation (RSME ± 3.3) relative to the range of surface salinities (~1.0 to 33.0) observed from CTD data. The CDOM model had an excellent fit between measured and predicted values (
R2 = 0.93) and low uncertainty (RMSE ± 0.84 m
−1) relative to the range of absorptions (0.1 to ~7 m
−1) observed in the laboratory data.
To illustrate the coastal dynamics that can be observed from spectral data when applied to the algorithms, we selected a Medium Resolution Imaging Spectrometer (MERIS) of the Neuse River estuary (North Carolina). Generally, the distribution of CDOM and salinities derived from MERIS imagery of the Neuse River estuary followed the inverse relationship characteristic of these properties in coastal waters. Predicted CDOM distributions made it possible to observe the transport of dissolved organic matter from adjoining watersheds into local waters and through coastal inlets into the Atlantic Ocean. Salinities increased as CDOM absorption decreased.
While the HICO and MERIS sensors are no longer operational, these sensors have provided a wealth of legacy ocean color images for examination and study. For almost a decade, the coastal remote sensing community has relied heavily on MERIS and Moderate Imaging Spectroradiometer (MODIS) multispectral sensors to understand coastal dynamics and processes. However, the 1000 m spatial resolution of MODIS color bands is too coarse to observe the spectral character of most estuaries and bays. MERIS (300 m resolution) and HICO (95 m resolution), the first spaceborne imaging spectrometer designed to sample estuaries and the coastal ocean, were ideally suited for environmental monitoring and algorithm development in estuaries and coastal bays.