New models for retrieving and partitioning the colored dissolved organic matter in the global ocean: Implications for remote sensing

Remote Sensing of Environment 115 (2011) 1501–1521

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Remote Sensing of Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e

New models for retrieving and partitioning the colored dissolved organic matter in the global ocean: Implications for remote sensing Palanisamy Shanmugam ⁎ Ocean Optics and Imaging Group, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai — 600036, India

a r t i c l e

i n f o

Article history: Received 29 July 2010 Received in revised form 6 February 2011 Accepted 12 February 2011 Available online 12 March 2011 Keywords: Ocean optics New CDOM models SeaWiFS Biogeochemical processes Global ocean

a b s t r a c t Despite the importance of CDOM to upper ocean biogeochemical processes and optics, our current understanding of its spatial and temporal distributions and the factors controlling these distributions is very limited. This eventually prevents an understanding of its relationship to the pool of dissolved organic carbon in coastal and open oceans. This work aims to present a new approach for accurate modeling of absorption spectra of CDOM (acdom) and deriving information on its composition in global ocean waters. The modeling approach uses measurements (in situ) of the remote sensing reflectances at two wavelengths (denoted 555 443Rrs) to estimate acdom(350) and acdom(412), applies them to determine two spectral slopes of an exponential curve fit (S) and a hyperbolic curve fit (γ), derives an appropriate parameter (γo) for grading the CDOM compositional changes from acdom (350) and γ, and finally employs acdom(350), S, and γo in a modified exponential model to describe acdom(λ) as a function of wavelength. The robustness of this model was rigorously tested on three independent datasets, such as NOMAD in situ data, NOMAD SeaWiFS match-ups data and IOCCG simulated data (all of them contain acdom(λ) and Rrs(λ)), which represent a variety of waters within coastal and offshore regions around the world. Accuracy of the retrievals found with the new models was generally excellent, with MRE (mean relative error) and RMSE (root mean square error) of − 5.64–3.55% and 0.203–0.318 for the NOMAD in situ datasets, and − 5.63 to −0.98% and 0.136–0.241 for the NOMAD satellite datasets respectively (for λ412 to λ670). When used with SeaWiFS images collected over the regional and global waters, the new model showed the highest surface abundances of CDOM within the subpolar gyres and continental shelves dominated by terrestrial inputs (and perhaps local production) of colored dissolved materials, and the lowest surface abundances of CDOM in the central subtropical gyres and the open oceans presumably regulated by photobleaching phenomenon, bacterial activity and local processes. Significant interseasonal and interannual seasonal changes in the terrestrially-derived CDOM distributions were noticed from these new products that closely corresponded with the global mean runoff/river discharge induced by climate change/warming scenarios. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Colored or chromophoric dissolved organic matter (CDOM) — historically referred to gelbstoff, gilvin or yellow substance — forms a significant fraction of the total DOM pool absorbing light strongly in the ultraviolet and blue domains and thereby plays a number of essential roles in the light-induced biogeochemical and carbon cycling and other processes in the ocean. This optically active component of marine water manifestly modulates the penetration of biologically damaging UVB radiation in the water column and thus protects phytoplankton and biota (Blough & Green, 1995; Vodacek et al., 1997). Extending into the visible domain, CDOM absorption (acdom) reduces the photosynthetically active radiation available to phytoplankton and thus decreases primary production and affects ecosys-

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tem structure (Bidigare et al., 1993). High CDOM absorption in the visible domain can also degrade the accuracy of satellite determinations of phytoplankton biomass particularly in Case 2 waters (Carder et al., 1991; Mannino et al., 2008; Muller-Karger et al., 1989). CDOM is also a primary reactant in the photoproduction of atmospherically important trace gasses CO2, CO, H2O2 and COS (Nelson & Siegel, 2002), in addition to yielding low-molecular-weight labile carbonyl compounds that are readily available for consumption by microbial communities (Vodacek et al., 1997). Rivers form a critical role in deriving higher and more variable concentrations of CDOM of terrestrial origin (mostly humic substances) and transferring it to the coastal seas (Del Castillo et al., 2001; Nelson et al., 2007). Increased CDOM concentrations in coastal waters are also due to the in situ creation of fulvic acids produced from the seaweed decomposition (Sieburth & Jensen, 1969), as a by-product of primary production stimulated by nutrients (Carder et al., 1989), and the anthropogenic input of industrial or domestic effluents from populated areas (Bricaud et al., 1981). The geographical extent of the

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terrestrially and anthropogenically dominated regions varies seasonally, depending on the magnitude of freshwater inputs and land runoff (Kowalczuk et al., 2005; Vodacek et al., 1997) and its dilution by physical mixing processes in the continental shelf seas (Boss et al., 2001). Away from continental margins, the effect of rivers declines and CDOM is mostly produced locally as a result of heterotrophic processes near the surface and is partly destroyed by solar bleaching in stratified surface waters (Del Castillo et al., 2001; Nelson et al., 1998). Though the various local processes result in the degradation of CDOM and alter its optical properties, CDOM still plays a significant role for absorption of light in the open ocean (Nelson et al., 2007). Knowledge of CDOM distributions and dynamics, the processes controlling CDOM, and its influence on optical properties are limited by the methods currently used for measurement. For instance, the absorption of light in seawater due to CDOM has been previously studied using the high-resolution fluorescence and absorption spectroscopy (Green & Blough, 1994). These techniques are laborintensive and time consuming, which limit the number of samples that can be analyzed and the spatial resolution of the data. Spectrophotometers with 5 and 10 cm optical cells are in use for measuring CDOM absorption with sufficient sensitivity in the UV and visible wavebands for many coastal and shelf waters. However, in oligotrophic waters, the levels of CDOM absorption approach the detection limit of these instruments (D'Sa et al., 1999) and measuring CDOM absorption spectra in these waters requires long-pathlength cells (Bricaud et al., 1981) or sample concentration (Carder et al., 1989). To overcome these limitations, a long pathlength liquid capillary optical waveguide has been proposed (D'Sa et al., 1999), but to date these instruments are not in routine use except in a few laboratories (Nelson & Siegel, 2002). Other instruments like the WetLabs ac-9 are capable of improving sampling resolution and enabling researchers to simultaneously measure chlorophyll and CDOM absorbance. Although this helps to directly assess their individual contribution to light attenuation in the water column, absorbance measurements do not permit a detailed characterization of CDOM optical properties because CDOM absorption spectra are essentially featureless (Del Castillo et al., 2001). Spectrally, the absorption by CDOM exhibits an exponential decay with increasing wavelength and is generally modeled with exponentially decreasing function (Jerlov, 1976; Shifrin, 1988). To investigate the spatial and temporal dynamics of CDOM in coastal and open ocean waters, several modeling methods have been proposed to describe its absorption as a function of wavelength in the visible domain. The widely used models are the single exponential model (EM) with or without offset (Bricaud et al., 1981; Carder et al., 1989; Hansell & Carlson, 2001; Kowalczuk et al., 2006; Schwarz et al., 2002) or the hyperbolic model (HM) (Twardowski et al., 2004). The EM is routinely used in many semi-analytical algorithms developed in the context of remote sensing applications (for example, Garver–Siegel–Maritorena (GSM) model developed by Garver and Siegel (1997) and further tuned by Maritorena et al. (2002), Linear Matrix (LM) model developed by Hoge and Lyon (1996) and modified by Boss and Roesler (2006), Quasi Analytical Algorithm (QAA) developed by Lee et al. (2002), and Carder99 algorithm developed by Carder et al. (1999)). The key parameters for deriving absorption coefficients from these models are the spectral slope (generally denoted by “S”) and the absorption at the reference wavelength (mostly 412 or 443 nm). The slope describes an exponential decrease of absorption spectrum over the visible wavelength region and increases with decreasing wavelength (Carder et al., 1989; Loiselle et al., 2009; Markager & Vincet, 2000), thus considered as a proxy for CDOM composition including the ratio of fulvic to humic acids and molecular weight. The reference absorption is usually derived based on the single or multiple ratios of remote sensing reflectance (Rrs) involving wavelengths between 443 and 560 nm, and used together with the assumption of known slope values to determine the absorption by colored dissolved and deterital organic matter acdm(λ). It should be

noted that these two components are considered together because of the similar shape of their spectra, and thus the difficulty in differentiating them in absence of a mechanical (namely a filtering) treatment (Morel & Gentili, 2009). Between these two terms, the differences are most likely to be small as the absorption by the dissolved component dominates the detrital component in open ocean waters that are chiefly determined by phytoplankton (Morel & Prieur, 1977). Whereas in coastal waters that are determined by materials other than phytoplankton such as the suspended sediments and various dissolved components, the background (also termed as offset) for acdom is considered null because the CDOM absorption generally assumes negligible values above 650 nm (D'Sa et al., 1999; Ferrari, 2000). Differently, the detrital absorption of complex coastal waters is not negligible in the red and near-infrared (D'Alimonte et al., 2004; Tassan & Ferrari, 2003) and this causes significant differences between the detrital and dissolved components over the entire spectral range with the highest at λ N 555 nm. Therefore, the combined term acdm(λ) eventually limits our knowledge of CDOM dynamics and factors controlling its distributions in coastal ocean regions, leaving little information on its relationship to the total pool of dissolved organic carbon (DOC) and its effects on submarine radiative transfer (Siegel et al., 2002; Twardowski & Donaghay, 2001; Vodacek et al., 1997). Furthermore, S value is often considered a constant (normally within 0.014 to 0.016 nm− 1) in many of the above remote sensing bio-optical models employing the single exponential model, because it is thought to vary unpredictably (Maritorena et al., 2002). According to Table 1 presented in Twardowski et al. (2004), the S values are contradictory as several studies have reported its average values falling in the range 0.0003–0.0247 nm− 1. In spite of the methodological differences (arising from the heterogeneity between different studies in the acdom spectral band used to fit CDOM spectra) that affect the S, the variability of S found in these studies is reasonably large which suggests a high degree of in situ variability in the composition of CDOM (i.e., origin and its sensitivity to environmental forcing such as photobleaching or bacterial degradation processes). Thus, modeling the spectral behavior of CDOM requires a rigorous understanding of the spectral character of absorption by CDOM and an accurate determination of the slope parameter. Recent studies have attempted to estimate the S values as a function of light absorption by CDOM at 375 nm (Kowalczuk et al., 2006; Stedmon & Markager, 2001). However, they noted that this relationship is not stable and often affected by the influence of CDOM distribution and its optical properties that change temporally and spatially in coastal and estuarine waters due to a number of complex physical, biological and chemical processes (Boss et al., 2001; Siegel et al., 2002; Zhao et al., 2009). These observations suggest an alternate/additional slope parameter (for deriving CDOM composition) to be needed for accurate determination of the shape of the CDOM curves. Furthermore, the current remote sensing semianalytical models are restricted to the determination of acdom(λ) in the visible region (400–750 nm); however, the UV-A (wavelengths from 350 to 400 nm) band has become more important especially for studying Case 2 waters (phytoplankton, CDOM, and detrital) (Kahru & Mitchell, 1998) and is the region in which the acdom(λ) would provide a better signal-to-noise ratio (SNR) than that in the visible band attempting to identify CDOM (Kahru & Mitchell, 2001). In order to remotely estimate acdom at UV wavelengths, several empirical algorithms have been developed in the past and thoroughly validated with regional datasets. For instance, Kahru and Mitchell (2001) applied the SeaWiFS Rrs(443)/Rrs(510) band ratio to retrieve acdom(300) at the CalCOFI site in southern California. Johannessen et al. (2003) related ultraviolet (UV) attenuation coefficients (Kd) at 323 nm, 338 nm, and 380 nm to the Rrs(412)/Rrs(555) band ratio, and found a strong relationship between Kd and acdom for each of the UV bands for the coastal ocean adjacent to the Chesapeake and Delaware Bays. D'Sa and Miller (2003) found strong relationships between acdom(412) and several Rrs band ratios (between 412 and 555 nm) in the Mississippi

P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521 Table 1 Statistical comparisons between the modeled and the NOMAD in situ data of acdom for the SeaWiFS bands centered at 400, 412, 443, 490, 510, 550 and 670 nm. Validations results for the NOMAD in situ datasets (A) and NOMAD SeaWiFS match-ups datasets (B). MRE (%) (A) Hyperbolic model (HM) acdom(412) − 48.19 acdom(443) − 36.56 acdom(490) − 28.48 − 27.47 acdom(510) acdom(555) − 26.09 acdom(670) − 27.57 Exponential model (EM) acdom(412) − 48.19 acdom(443) − 34.54 − 22.48 acdom(490) acdom(510) − 19.76 acdom(555) − 14.57 acdom(670) − 7.41 New model acdom(412) − 5.64 acdom(443) − 3.16 0.30 acdom(490) acdom(510) 0.48 acdom(555) 1.78 acdom(670) 3.55 (B) Hyperbolic model (HM) acdom(412) − 5.64 acdom(443) − 5.73 acdom(490) − 6.37 acdom(510) − 7.47 acdom(555) − 9.48 acdom(670) − 15.40 Exponential model (EM) acdom(412) − 5.64 acdom(443) − 4.87 acdom(490) − 3.15 acdom(510) − 3.08 acdom(555) − 2.27 acdom(670) − 0.98 New model acdom(412) − 5.63 acdom(443) − 4.87 acdom(490) − 3.15 acdom(510) − 3.08 acdom(555) − 2.27 acdom(670) − 0.98

RMSE

Slope

Intercept

R²

N

0.4205 0.4278 0.4485 0.4731 0.5312 0.7659

0.7378 0.7489 0.7682 0.7777 0.8006 0.8747

− 0.3895 − 0.4488 − 0.5308 − 0.5778 − 0.6698 − 0.8970

0.8283 0.8148 0.7888 0.7746 0.7401 0.6345

216 216 216 216 216 216

0.4205 0.4135 0.3931 0.3931 0.3843 0.3831

0.7378 0.7530 0.7805 0.7943 0.8287 0.9450

− 0.3895 − 0.4336 − 0.4651 − 0.4807 − 0.4793 − 0.3060

0.8283 0.8147 0.7887 0.7744 0.7397 0.6337

216 216 216 216 216 216

0.2031 0.207 0.2177 0.2252 0.2459 0.3186

1.0233 1.0523 1.1040 1.1296 1.1936 1.4130

− 0.0217 − 0.0217 0.1264 0.1789 0.3525 1.1336

0.8277 0.8163 0.7933 0.7805 0.7489 0.6501

216 216 216 216 216 216

0.2977 0.2983 0.3088 0.3242 0.3686 0.5863

0.5511 0.5521 0.5620 0.5695 0.5928 0.6934

− 0.6059 − 0.7096 − 0.8491 − 0.9154 − 1.0383 − 1.2492

0.8592 0.8555 0.8237 0.7994 0.7316 0.5153

52 52 52 52 52 52

0.2977 0.2924 0.285 0.2863 0.2893 0.3219

0.5511 0.5549 0.5705 0.5809 0.6122 0.7455

− 0.6059 − 0.6986 − 0.8021 − 0.8457 − 0.9001 − 0.7912

0.8592 0.8555 0.8236 0.7993 0.7314 0.5150

52 52 52 52 52 52

0.1364 0.1357 0.1366 0.1458 0.1651 0.2408

0.9536 0.9735 1.0234 1.0526 1.1363 1.4863

− 0.1264 − 0.1093 − 0.0158 0.0391 0.2476 1.4256

0.8654 0.8614 0.8289 0.8041 0.7353 0.5163

52 52 52 52 52 52

River plume. Recently, Mannino et al. (2008) developed a robust relationship between the acdom(355) and Rrs ratios within visible domains to derive MODIS/Aqua and SeaWiFS products in coastal waters of the U.S Middle Atlantic Bight. Their validation analyses demonstrated the mean absolute percent differences within 12–19% for acdom at 355 and 443 nm. Morel and Gentili (2009) have proposed a simple 412 490 algorithm which relies on the spectral reflectance ratios R443 and R555 and a 2-D lookup table taking these ratios as input to estimate acdom at specific wavelengths (400, 412, 440, 490, and 555 nm). Since the algorithm primarily depends on Chl concentrations, the focus has been put on the CDOM content in Case 1 waters which are outside of significant terrigeneous influences and whose optical properties are chiefly determined by endogenous materials created through the biological activity. Thus, this algorithm is more applicable in Case 1 waters. Although these efforts have demonstrated success in satellite retrieval of acdom at certain wavelengths in various regional waters, a new model is needed to predict the spectral behavior of CDOM absorption over the range of wavelengths and identify the type and nature of CDOM pools in global waters. In the present study, our objectives are to develop an improved model to describe absorption by CDOM as a function of wavelength λ,

1503

to establish quantitative relationships between the optical properties acdom(λ) and S of CDOM, and to explore an innovative tool to partition between local/marine (i.e., remineralization) and terrestrial sources for the coastal and deep ocean reservoir of CDOM. The algorithms developed in this study can be used to improve ecological and biooptical models for remote sensing and to provide an opportunity to address important questions: whether, on a global scale, the parameters of the exponential model can be incorporated into biooptical models by finding relationships between them, whether the slopes can be related to chemical information which might provide insight into the natural water bodies, what extent coastal and oceanic CDOM is remnant of inputs from the terrestrial biosphere, is the product of in-situ biological processes, or is a derivative of both sources; what mechanisms regulate the distribution of coastally derived CDOM to the open ocean. In addition to addressing these, the developed relationships and models are rigorously tested based on the large bio-optical datasets collected around Korean and neighboring ocean waters and in various regional waters around the world. 2. Data and methods 2.1. In situ data In-situ measurements of the bio-optical properties were performed during 31 cruises carried out in the Korean waters, Yellow Sea, East China Sea and East Sea (covering 31°–38°N and 123°–133°E) through the years 2002–2007 and 2 cruises carried out in the Kongsfjorden coastal and offshore waters on the western side of Svalbard of the Arctic (covering 78° 45′–79° 20′N and 9° 30′–12° 30′E) in June 2007 and 2008. After a careful examination of these datasets, 919 in-water constituents profiles such as Chlorophyll (Chl), suspended sediment (SS) and absorption by CDOM (acdom) and 211 remote sensing reflectance (Rrs) profiles were selected representing a wide range waters covering both the coastal and open ocean environments with Chl 0.1–89.49 mg m− 3; SS 0.1–114 g m− 3; acdom 0.03–2.19 m− 1 (400). These were the primary datasets used in the development of algorithms/models and initial assessment of their validity. To test the model performance for globalscale applications, NASA bio-optical marine algorithm datasets (NOMAD) were obtained from the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) of the NASA Ocean Biology Processing Group. The NOMAD datasets are global high quality in situ bio-optical datasets acquired over a significant variety of mesotrophic and eutrophic water types with Chl 0.09–77.8 mg m− 3; acdom 0.019–1.9 m− 1 (412); bbp 0.0004–0.09 m− 1 (412). After scrutiny, 216 (in situ Rrs(λ) and acdom(λ)) and 53 (satellite Rrs(λ) and in situ acdom(λ)) match-ups data were selected for the in situ and satellite validations for the wavelengths corresponding to SeaWiFS bands centered at 412, 443, 490, 510, 555 and 670 nm. On the other hand, the scientific working group established by IOCCG provides the synthetic datasets (simulated for Sun at 30° from zenith) including 500 Rrs(λ) and acdom(λ) pairs. These three independent datasets were used to test the model performance and address its suitability for satellite applications of CDOM in the global ocean. 2.2. In situ CDOM absorption data Surface water samples for the determination of optical properties of CDOM were collected from each station using Niskin/pre-rinsed bucket, and filtered through 0.45 μm membrane syringe filters (25 mm) previously rinsed with ultra pure Milli-Q water. 50 ml of filtered sample were stored in glass flasks in the dark at 4 °C for analysis in laboratory. Immediately after returning to the laboratory, samples for spectroscopic analyses were allowed to warm to room temperature. Absorbance scans from 350 to 900 nm (at 1 nm sample intervals) were conducted using a dual-beam Perkin-Elmer Lambda19 Spectrophotometer connected to a desktop computer. The measurements were performed by placing a 10 cm quartz cuvette

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containing Milli-Q water in the optical path of the reference beam, and a 10 cm quartz cuvette containing the filtered seawater sample in the optical path of the sample beam. The spectral absorption coefficients acdom(λ) m− 1 at each wavelength (λ) were calculated from the measured absorbance (Acdom) resulting from the difference between the sample absorbance and the reference absorbance (Ferrari et al., 1996), from acdom ðλÞ = 2:3025 × Acdom ðλÞ=Lc

ð1Þ

where Lc is the path length of the cuvette (i.e., 0.1 in units of meters). The acdom(λ) values measured for a variety of water types were generally found to decrease almost exponentially throughout the UV and visible spectral domains (Bricaud et al., 1981; D'Sa et al., 1999; Kowalczuk et al., 2006; Stedmon & Markager, 2001; Twardowski & Donaghay, 2001). Scattering and refraction effects by particles less than 0.45 μm have been recognized as a potential source of error in making absorption measurements in the dissolved fraction of seawater (Aas, 2000; Twardowski et al., 2004). To remove these effects and estimate S and acdom at 350 and 412 nm, CDOM's optical properties were modeled from 350 to 650 nm using the following equation
 −1 −Sðλ−λi Þ acdom ðλÞ m = acdom ðλi Þe +Δ

ð2Þ

where λi is at 350 nm and Δ is background constant that allows for any baseline shifts or attenuation not due to organic matter (Kowalczuk et al., 2006; Stedmon & Markager, 2001). Following their recommendations, the parameters Δ, S and acdom at 350 and 412 nm were estimated simultaneously by applying a nonlinear regression of Eq. (2) on the raw absorption curve at a spectral range from 350 to 650 nm. This technique was found to be valid at wavelengths ranging from about 350 to 650 nm (Jerlov, 1976; Kirk, 1994) with the mean exponential slope taking on a value of 0.014 nm− 1 with a standard deviation of 0.0032 nm− 1 in different waters (Bricaud et al., 1981). It has also been found to give a better fit of the model to the observed spectrum than the linear regression of log transformed data by preferentially weighting regions of higher CDOM absorption rather than the areas of low absorption (Stedmon et al., 2000). This method permits the use of a fixed wavelength range for estimating S and therefore simplifies the comparison of S values from CDOM in different environments. Calculation of the slope coefficient in spectral range, 300–650 nm, was aimed to extend modeling of the absorption spectrum into the ultraviolet, which is important for excitation of specific fluorophores and for photochemical decomposition of CDOM (Kowalczuk et al., 2005). However, while modeling of CDOM spectra into the UV less than about 350 nm, consideration must be given to other compounds other than CDOM in the dissolved fraction of seawater, such as oxygen, bromide, nitrate, and seasalts that begin to absorb intensely at short wavelengths (Shifrin, 1988). This could induce biases in the CDOM spectra measurements below 350 nm and are therefore susceptible to significantly influence the spectral slopes calculation. Slopes calculated below 350 nm are therefore questionable and will not be applicable to the visible domain (Schwarz et al., 2002). The background was considered null (in open sea waters) or very small (in coastal waters) as the CDOM absorption generally assumes negligible above 650 nm (D'Alimonte et al., 2004; Loiselle et al., 2009). Small scattering errors might nonetheless remain, and hence a method was adopted to include Δ in the regression and subsequently subtract its derived values from the absorption spectra considered in this study. 2.3. Field radiometric data For each cruise and at each station, in-situ radiometric measurements (above the seawater) were performed in the 350–1050 nm spectral range (with a 1.4 nm spectral interval) from a calibrated ASD

FieldSpec Pro Dual VNIR Spectroradiometer. These measurements included the total water leaving radiance tL w(λ), sky radiance L sky(λ) and downwelling irradiance Ed (λ). The tL w(λ) and L sky(λ) were measured with a pointing angle θ ~25–30° from nadir and an azimuthal angle ϕ ~ 90o from the solar plane, whereas sky radiance was measured in the same plane as tL w(λ) but from a direction of θ ~30° from zenith. Ed (λ) was measured by pointing the sensor upward from the deck. Since tL w(λ) consisted of the desired water-leaving radiance L w(λ) and a contamination term ΔL = Fr ðλÞ × Lsky ðλÞ

ð3Þ

it was necessary to correct the recorded data (mW cm−2 μm−1 sr− 1) for these contributions of skylight reflection (Lsky ðλÞ) and Fresnel reflectance (Fr(λ)) of air–sea interface using Lw ðλÞ = tLw ðλÞ−ΔL:

ð4Þ

In this calculation, the values of Lsky(λ) were obtained from the sky radiometer and Fr value was assumed to be 0.025 (Austin, 1974). In fact, Fr varies with viewing geometry, sky conditions and sea surface roughness due to wind and is wavelength-dependent under a cloudy sky (Mobley, 1999). Applying these values in the above equation yielded the water-leaving radiance (Lw(λ)), which was then divided by downwelling irradiance to obtain remote sensing reflectance Rrs (λ) using

 þ þ Rrs 0 λ = Lw ðλÞ = Ed 0 λ

ð5Þ

The Rrs(λ), referred in units of sr− 1, is an apparent optical property (AOP) that contains the spectral information of the water-leaving radiance, but which is largely free of its magnitude variability (Chang et al., 2003). 2.4. Satellite data Full resolution (~ 1 km/pixel at nadir) Level 1A SeaWiFS ocean color radiances for eight narrow spectral channels in the visible and near-infrared spectral domains were acquired from the KORDI-HRPT station for the period of 19 September 2000. The Level 1A SeaWiFS data was atmospherically corrected using the SSMM technique (Shanmugam & Ahn, 2007) and processed to acdom (new model) and salinity (Ahn et al., 2008) for the preliminary investigation. For global analyses, SeaWiFS composite images of normalized waterleaving radiance (nLw(λ)) sampled at 9 km for wavebands centered at 443 and 555 nm were obtained from the NASA's Goddard Space Flight Center (http://oceancolor.gsfc.nasa.gov/SeaWiFS/) for four seasons (winter, spring, summer and autumn) of the years 1998 and 2007. The normalized water-leaving radiances extracted at 443 and 555 nm were converted to the remote sensing reflectance using Rrs(λ) = nL w(λ)/Fo(λ) where Fo is the mean extraterrestrial solar irradiance. All the SeaWiFS global coverage reflectances for the above periods were processed by the new model and the results were analyzed in conjugation with the previous findings and seasonal river discharge records obtained from the Global Runoff Data Centre (GRDC). 2.5. Performance assessment methods To assess the performance of the models, it is necessary to apply the models to validation datasets different from those used for development of the models. Three data sets were independent of our algorithm datasets: (1) NOMAD in situ datasets that contain in situ bio-optical measurements and concurrent in situ acdom(λ), (2) NOMAD datasets that contain SeaWiFS Rrs measurements and concurrent in situ acdom(λ), and (3) IOCCG-simulated datasets for algorithm calibration and

P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521

validation (Boss & Roesler, 2006). Two statistical measures were used to assess the differences between these in situ acdom(λ) and model-derived acdom(λ), namely the normalized root mean square error (RMSE) and mean relative error (MRE). The RMSE and MRE are defined as follows, 0

1 1=2

N

mod insitu 2 Þ C B ∑ ½logðacdom ðλÞ Þ−logðacdom ðλÞ Bi = 1 C RMSE = B C @ A N−2

N


 logðacdom ðλÞmod Þ−log acdom ðλÞinsitu

i=1

logðacdom ðλÞinsitu Þ

MRE = ∑

× 100

ð6Þ

ð7Þ

where acdom(λ)mod and acdom(λ)insitu are the model-derived acdom(λ) and in situ acdom(λ) at the desired wavelength λ, respectively. N is the number of valid retrievals. In addition to these errors, regression slope, intercept and correlation coefficient were also derived from the linear regression analysis to assess the accuracy of the models. 3. Modeling approach The beam attenuation coefficient c (m− 1) is the sum of the rate of radiation losses from absorption and scattering: cðλÞ = aðλÞ + bðλÞ

ð8Þ

where a(λ)(m− 1) is the absorption coefficient and b(λ)(m− 1) is the scattering coefficient. Scattering can be further characterized in terms of the angular distribution of the scattered light (van de Hulst, 1981), which is however beyond the scope of the present study. An analysis of the component light absorption provides valuable insights into the relative importance of CDOM to light availability and ocean color, as it is a measure of an inherent optical property (IOP) of the water, which means that it is a property of oceanic waters and fully dependent on the water composition. The total absorption coefficient, a(λ), may be partitioned into components due to pure seawater absorption aw(λ), absorption by phytoplankton aph(λ), absorption by colored dissolved organic matter acdom(λ), and absorption by detrital matter, adet(λ): aðλÞ = aw ðλÞ + aph ðλÞ + acdom ðλÞ + a det ðλÞ

ð9Þ

where aw ðλÞ is assumed to be a known constant (Pope & Fry, 1997; Smith & Baker, 1981). The latter two terms (acdom ðλÞ + a det ðλÞ) have similar spectral shapes decreasing monotonically with increasing wavelength, and hence inverse methods for quantifying ocean color spectra cannot yet differentiate between these two signals. Therefore, they are typically considered together and referred to as colored dissolved and detrital organic matter (acdm ðλÞ) by various bio-optical modeling studies. Available spectroscopic observations from the surface waters of many sites around the world suggest that CDOM absorption dominates the total absorption budget over most of the open ocean and the detrital particles will make a small contribution to the CDOM absorption signal. Here, CDOM is the primary interest of this investigation and is usually described by a negative exponential function (Bricaud et al., 1981): −Sðλ−λi Þ

acdom ðλÞ = acdom ðλi Þ:e

ð10Þ

where acdom(λi) is the absorption of CDOM at the scaling (or reference) wavelength λi and S is the spectral slope coefficient of exponential that determines the shape of the absorption curve and is assumed to represent changes in CDOM composition (Bricaud et al., 1981; Jerlov, 1976). Since the S values for a simple exponential curve fit are highly dependent on the wavelength range used for the calculation and a simple exponential curve may not be sufficient to describe the acdom(λ) (m− 1) spectra, an alternate model has been

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proposed by Twardowski et al. (2004) in the context of remote sensing applications (especially in the visible domain), which expresses acdom(λ) as a smoothly varying function:   λ −γ acdom ðλÞ = acdom ðλ412 Þ· λ412

ð11Þ

where acdom ðλ412 Þ is the reference absorption of CDOM and γ is the hyperbolic slope assumed to be 6.92 (dimensionless). Though this hyperbolic model (HM) has the convenience to be dependent of one parameter only (the hyperbolic slope) and is considered to be better descriptor of CDOM absorption spectra than the exponential model (EM), the wavelength range over which the model is applied is important and a constant slope used in HM model may not adequately account for the CDOM variability in a wide range of waters. For instance, in coastal waters, CDOM often represents the dominant optical signal, and its concentration to first order varies with the proximity to shore as terrestrial inputs represent the dominant source of CDOM. In bays near the mouths of rivers, where CDOM loading is high, the water is often referred to as black water. Water color in the coastal zone can vary rapidly in both space and time reflecting the mixing between blue ocean water and dark CDOM-dominated water. Such high variabilities of CDOM will likely invalidate the use/assumption of the constant slope value with either exponential or hyperbolic model. Furthermore, CDOM differs from water and particulates as its absorption decreases extremely in the visible portion of the spectrum (N500 nm) and increases approximately exponentially in the ultraviolet portion (300 to 400 nm) of the spectrum. Optical characterization of CDOM absorption in the UV domain is extremely important in this region, but is much less documented by the existing models and currently the open field of the investigation. To overcome these with the HM and EM, a new exponential form of equation is introduced with two slope parameters which describe appropriately the absorption of CDOM for the UV and visible wavelengths and provide a useful parameterization for modeling in coastal and ocean waters: acdom ðλÞ = acdom ðλi Þ:e

ð−Sðλ−λi Þ−γ o Þ

ð12Þ

where acdom ðλi Þ is absorption of CDOM at 350 nm obtained directly from satellite observation of water-leaving radiances and the γ o is an additional parameter independent of S value that takes into account the large variability of CDOM in coastal and ocean waters. The offset parameter is discarded because it is essentially a correction parameter required to obtain the true values for acdom ðλi Þ and S. Consideration of the SNR in open ocean waters, together with interest in the role played by CDOM in mitigating UV light levels in natural waters, has led to the use of 350 nm in the derivation of equation for calculating γ o as follows, γo =

acdom ð350Þ−ð1 = γÞ : acdom ð350Þ + ð1 = γÞ

ð13Þ

The HM slope γ can be retrieved by inversion or by fitting Eq. (11) to the absorption curve of CDOM in the same wavelength region from 350 to 650 nm. It then simplifies the comparison of γ and S values from CDOM in different environments. It is important to note that γ o is maximally sensitive to a wide range and the nature of CDOM variability since it is derived from the combined and difference ratios of “1/γ” with “acdom(350)”, the region important for excitation of specific fluorophores and for photochemical decomposition of CDOM. Many studies have chosen acdom(350) for describing changes in CDOM variability and differentiating CDOM because of its high response and variability around this wavelength (e.g., Jerlov, 1976; Kirk, 1994; Kowalczuk et al., 2005; Mannino et al., 2008). Consequently, γ o modifies the nature of basic exponential function and alters the

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P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521

amplitude of the absorption spectrum of CDOM to reduce uncertainties in its retrievals with other models. γ o values vary from −1 to + 1. A γ o value of −1 means CDOM pool originating from terrestrial inputs and close to +1 indicates the CDOM pool originating from local production and degradation mechanisms (in open ocean waters). The intermediate γ o values can explain the transport, mixing and in situ production processes. This means that, in addition to S, γ o can be used as a better indicator of the origin or history of a given CDOM water sample. With the knowledge of four parametersacdom ð350Þ, S, γ, and γ o, one is able to quantify and characterize the CDOM pool in natural waters, and thereby, also trace changes resulting from the production and removal of CDOM and the mixing of different pools (e.g., terrestrial and marine origin). Conventionally, measuring the absorption spectrum of CDOM is generally based on the spectrophotometry, custom long-path cells or capillary optical waveguides but with several limitations. Another important method for estimating CDOM absorption relies on its known impact on the underwater light field, which is usually based upon reflected light spectrum and may be applied to satellite ocean color datasets enabling global distributions to be assessed (Nelson & Siegel, 2002; Siegel et al., 2002). For remote sensing applications, acdom ð350Þ and acdom ð412Þ are to be estimated at first from spectral reflectances measured by the ocean color sensors. Presently, the empirical algorithms routinely employed for processing SeaWiFS and MODIS are based on the successive use of several ratios of reflectances involving wavelengths between 443 and 560 nm, to retrieve Chl concentrations (O'Reilly et al., 1998). For deriving acdom ð350Þ and acdom ð412Þ, the spectral reflectances of the ocean, as derived from ocean color remote sensing data, at two wavelengths (443 and 555 nm) are used to form a simple ratio, denoted 555 443Rrs, which is sensitive to the CDOM albeit influenced by the algal content. The 555 443Rrs decreases when CDOM absorption increases, and the variations in this relationship can be better established via a simple power-law function. This formula is effective and applies to a wide range of water types considered in various previous studies (Kutser et al., 2005). Kahru and Mitchell (2001) derived a similar empirical formula of acdom ð300Þ using their in situ datasets, wherein the normalized water-leaving radiance (nLw) ratios nLw(443)/nLw(520) for OCTS and nLw(443)/nLw(510) for SeaWiFS were used to avoid known problems with atmospheric correction at short wavelengths (note the inaccuracy of the atmospheric correction growing for decreasing wavelengths so that the 412 nm wave band is the most affected one (Morel & Gentili, 2009). Keeping this in view and the capabilities offered by spectral channels in the visible domain with current ocean color sensors, a straightforward tool is developed here which is applicable to ocean color remote sensing data to estimate CDOM optical properties in coastal and ocean waters.

between the log transformed data of S and γ that yielded approximately the following expression that best fits the data (Fig. 1): γ = 309:62 × S + 1:4625

with coefficient of determination R2 = 0.88. This equation allows us to estimate γ as a function of S values and use it in the HM that currently uses a fixed γ value (Twardowski et al., 2004). 4.1.2. Estimation of the slope parameters The most commonly used approach for identifying trends in CDOM-S is to plot S values against acdom(375) (e.g., Kowalczuk et al., 2006; Schwarz et al., 2002; Stedmon & Markager, 2001). A negative correlation between acdom(375) and S was found in these studies and this relationship enabled them to explain variations in the slope of CDOM that could be applied in terms of CDOM source-tracing. Nonetheless, pooling coastal/nearshore data into this relationship often presented a bidirectional variability in S which was found reasonably large suggesting a high degree of variability in the composition of CDOM in coastal waters. A similar variability of S at low and high CDOM concentrations in coastal and offshore waters was reported by Stedmon et al. (2000). There existed a clear overlap of some of the data from the deep waters with the coastal waters, whereas data from the highly turbid waters and clear waters did not exhibit the same behavior. Also, the lower precision of the estimation of S at these concentrations cannot explain the general inverse relationship seen in these studies. It is therefore questionable to apply this relationship to accurately retrieve the slope parameter over a range of water bodies. To overcome this problem, the ratio of acdom (412) and acdom(350) (acdom(412)/acdom(350)) was chosen here because of their increased absorption at these wavelengths and the consideration of the SNR in open ocean waters (Schwarz et al., 2002). Fig. 2 shows the values of acdom(412)/acdom(350) plotted against S and γ. These relationships significantly reduced the uneven spread of data points found in the plots of acdom(375) versus S and γ due to a larger proportion of the water samples originating from different regions (coastal, mixing and offshore). Regression analyses verified the relationships between acdom(412)/acdom(350) and S and γ that followed a non-linear power law function with coefficient of determination R2 = 0.78 and 0.71 respectively. The equations which fit the data best are expressed as follows:

S = 0:0058 ×

2

ð15Þ

R = 0:78

18 16 14

γ (Dimensionless)

4.1.1. Derivation of the S and γ parameters and their relationship The significance of the difference between the slope parameters of the exponential and HM models obtained by fitting methods and their relationship were studied. The histograms (not shown for brevity) of the acdom (375), exponential slope S (nm− 1) and hyperbolic slope γ (dimensionless) derived using our in-situ datasets (N= 919) showed that the acdom (375) varies from 0.0041 to 3.379 m− 1 with a peak at 0.324 m− 1, the S values range from 0.0068 to 0.0428 with a mean value of 0.015 nm− 1, and the γ values have the range 3.6 to 15 with a mean of 6.15. The mean values of S and γ are in agreement with or closer to those reported in various types of waters (Twardowski et al., 2004 and references therein). For establishing a relationship and identifying the trend between them, simple linear regression analysis was performed

  acdom ð412Þ −0:9677 ; acdom ð350Þ

20

4. Results and discussions 4.1. Algorithms and their assessment for the new model

ð14Þ

12 10 8

6

4 0.01

0.02

0.03

0.04

0.05 0.06

S (nm-1) Fig. 1. Co-variation of S and γ in log-transformed data sets (N = 825).

P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521

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allow the prediction of γo values of CDOM from measurement of the absorption at two wavelengths in the UV/visible domain. 4.1.3. Assessment of the estimated slope parameters To confirm the results provided by Eqs. (15) and (16), the departures of the modeled S and γ values from those estimated by the exponential and HM fits (as expressed by Smodel − Sinsitu and γmodel − γinsitu) were examined using our in situ data from each study site (Fig. 3). This illustration shows clear patterns in S and γ estimates with pronounced positive peaks and negative troughs across the sample points caused by the overprediction and underprediction of the models. However, most of the departures are within the range −0.002–0.002 nm− 1 S and −0.5–0.5 γ, although some overpredictions occurred in Arctic waters (sample points starting from 117 to 150 for August 2006), Yellow Sea and East China Sea waters (700–785 for Feb. 2002 and May 2003) and Korean South Sea waters (821–834 for June 2002) and underpredictions in ECS waters (52–80 for May 2005) and Korea coastal/red tide waters (259–282 for Nov. 2005). The mean departures show strikingly small values (−0.0001 nm− 1 S and −0.0169 γ) indicating that the models have the potential to return accurate retrievals of the S and γ parameters.

γ = 2:9332 ×

  acdom ð412Þ −0:7506 ; acdom ð350Þ

2

R = 0:71

ð16Þ

3

model

0.01 0.008 0.006 0.004 0.002 0 -0.002 -0.004 -0.006 -0.008 -0.01 51 101 151 201 251 301 351 401 451 501 551 601 651 701 751 801 851 901 r-estimates S-estimates

2 1 0 -1

-

insitu

(Dimensionless)

A tendency for lower ratio values (lower absorption) to be associated with higher slopes, and for higher ratio values (higher absorption) to be associated with lower slopes is observed, with reduced scattered points. The increased S and γ values with decreasing acdom(412)/acdom(350) clearly suggest that the above relationships can be effectively used to model the inverse behavior of marine CDOM in order to (1) allow the distinction between marine and terrestrial organic matter based on optical observations, and (2)

-2 -3 1

Smodel - Sinsitu (nm-1)

Fig. 2. Spectral slopes (S and γ) calculated from the exponential and HM fitting methods versus the ratio of in situ CDOM absorption coefficients at λ = 350 and 412 nm plotted on a log-log scale (N = 902).

4.1.4. Impact of the slope parameters on the determination of acdom(λ) To better understand the impact of slopes on the determination of acdom(λ), the exponential and HM fits were applied to the in situ acdom (λ350–650) spectra for three different water types (Type 1 — relatively clear water with Chl 0.85 mg m− 3, SS 1.64 g m− 3, and acdom (400) 0.18 m− 1; Type 2 — algal bloom water with Chl 11.5 mg m− 3, SS 7.69 g m− 3, and acdom (400) 0.91 m− 1; Type 3 — coastal waters with Chl 1.62 mg m− 3, SS 25 g m-3, and acdom (400) 1.94 m-1) using a constant slope value and the variable slopes calculated by this study (Eq. (15)). This is particularly important for the bio-optical modeling studies predicting the spectral slope curves of CDOM and comparing them between ecosystems to distinguish the dominant optical properties of the water body, across a range of conditions (Loiselle et al., 2009). Fig. 4 compares the modeled absorption curves of the exponential fit with in situ acdom (λ) spectra for the three water types and illustrates the differences between the modeled and in-situ acdom (λ) spectra. Note that in all the three cases, the in situ CDOM absorption spectra (brown color) decrease in a near-exponential manner from the UV to the far visible wavelengths, declining to near zero around 650 nm. Determination of the CDOM slope curves by a fixed slope value shows noticeable differences of spectral slope over wavelength. In particular, the spectral slope values show a steep increase from blue to UV wavelengths (400–350 nm) and a pronounced decrease at longer wavelengths (440–650 nm). This increase and decrease is apparent on the data collected in relatively clear waters (top panel). In case of type 2 and 3 waters, this trend is reversed with the highest negative difference (underprediction) in the UV region (350–400 nm) and a more prominent plateau

Number of observations from 2002-2007 Fig. 3. The absolute differences between model estimates of S and γ (Eqs. (15) and (16)) and those derived from the exponential and HM fits applied to the in situ CDOM spectra for each site. The mean difference is − 0.0001 nm− 1 S and − 0.0169 γ for N = 901.

P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521

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Type 1: Chl 0.85 mg m-3 SS 1.64 g m-3 acdom (400) 0.18 m-1

acdom (m-1)

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350

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-0.4 -0.6 -0.8

In-situ spectrum By model from this study By adopting a constant "S"

-1

Wavelength (nm)

Fig. 4. Exponential fitting (Eq. (10)) of the CDOM absorption coefficients for three typical cases (relatively clear waters — Type 1; bloom waters — Type 2; coastal waters — Type3) using the constant and variable slopes calculated from this study (Eq. (15)). acdom ðλi Þ is at 412 nm taken from the measured spectrum and a constant S value is 0.015 nm− 1.

(overprediction) that extends from 420 nm to 650 nm, with a center at 470 nm. The magnitude of the difference also increases with increasing CDOM concentrations (bottom panel). As expected, the spectral slope curves determined by the variable S values (Eq. (15)) are quite similar in magnitude and shape to those of the in situ spectra for all the three water types, although showing a systematic downward deviation (underprediction) when extending the modeling analysis of CDOM spectra into the UV wavelengths (from 350 to 380 nm). This may be because of the inadequacy of the slope parameter to account for the variability in spectral variations of the CDOM absorption in the complex waters. When conducting a similar analysis with HM, significant effects of the constant and variable γ values (from Eq. (16)) on the determinations of the absorption slope curves were noticed. Fig. 5 displays the measured and modeled absorption spectra (left panels) along with their differences (right panels). For the sake of comparison, the difference curves of the exponential fit that used the variable S values (Eq. (15)) for all three samples are also incorporated in this illustration. As seen before, absorption curve determined by the constant γ value significantly deviates upward (overprediction) at the UV wavelengths and downward (underprediction) at the visible wavelengths (425– 575 nm) in the case of type 1 water. In type 2 water, there appears to be a reversed trend with a more pronounced underprediction towards the UV region and overprediction towards the visible region. These differences are more apparent when extending the analysis for coastal waters (bottom panels), reconfirming that the single γ value may not be adequate to describe the CDOM variability in these waters. It is

important to note that having considered the variable γ values in HM fitting sharply declined the differences of slope curves as previously observed at the UV region for all three samples. Due to the limitation of the HM, a noticeable divergence is still present at the visible wavelengths. A pretty interesting comparison of the difference curves of the exponential and HM fits applied with their respective slope values (Eqs. (15) and (16)) leads to the conclusion that the later model works reasonably good at describing acdom (λ) spectra at the UV region whilst the former model enables more accurate acdom (λ) at the visible region. Nevertheless, both the models systematically induce an underprediction and overprediction of the absorption measurements in the spectral range of 350–650 nm, and therefore a new modeling approach is highly demanded for its accurate retrievals (Twardowski et al., 2004). 4.1.5. Estimation of the acdom (350) and acdom (412) To enable the new exponential model (Eq. (12)) to predict the spectral slope curves of absorption coefficient as a function of wavelength λ, acdom at 350 nm and 412 nm are to be estimated using the remote sensing reflectances. Taking the advantage of decreased reflectance in the blue (443 nm) and increased reflectance in the green (555 nm), a simple ratio of spectral reflectance 555 443Rrs was formed and directly related to acdom(350) and acdom(412) through statistical relationships. Indeed, it is one of the ratios routinely used in the maximum band ratio technique to retrieve Chl (e.g., O'Reilly et al., 1998) and is more robust than the 555 412Rrs (see details in Section 3). Fig. 6 shows the scatter plots of the relationships between the log-transformed acdom (350) and acdom(412) and the log-transformed spectral ratio 555 443Rrs

0.5

Type 1: Chl 0.85 mg m-3 SS 1.64 g m-3 acdom (400) 0.18 m-1

aCDOM (m-1)

0.4 0.3 0.2 0.1 0 350

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Modelled - In-situ aCDOM (m-1)

P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521

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aCDOM (m-1)

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4

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2

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0.12 0 -0.12

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-0.24 -0.36 -0.48 -0.6

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In-situ spectrum By adopting a constant "r"

By model from this study using "r" By model from this study using "S"

Fig. 5. Hyperbolic fitting (Eq. (11)) of the CDOM absorption coefficients for three typical cases (relatively clear waters — Type 1; bloom waters — Type 2; coastal waters — Type3) using the constant and variable slopes values calculated from this study. acdom ðλi Þ is at 412 nm taken from the measured spectrum and a constant γ value is found 6.12.

measured from a variety of waters, with a goodness of fit found between them. Of the other fitting methods examined, the power-law fitting is found highly consistent (R2 = 0.74 for acdom(350) versus 555 443Rrs and R2 = 0.76 for acdom(412) and 555 443Rrs) that captures a large fraction of the variation in the reflectance ratio and variation in the acdom(350) and acdom(412) values. This fitting is considered the best descriptor of the behavior of the absorption coefficient for the values of 555 443Rrsoutside the range of our measurements. As the best fits are obtained using the power-law function for a variety of samples, the resulting empirical equations can be described as follows:  
 R ð443Þ ð−2:0421Þ −1 = 0:5567 × rs acdom ð350Þ m Rrs ð555Þ

R = 0:74

 
 R ð443Þ ð−1:9668Þ −1 acdom ð412Þ m = 0:1866 × rs Rrs ð555Þ

R = 0:76:

2

ð17Þ

2

ð18Þ

Values 0.5567, − 2.0421, 0.1866 and −1.9668 are the regression coefficients and Rrs is the remote sensing reflectance. It becomes more important that the stability and statistical significance of the obtained relationships should be assessed prior to their implementation for remote sensing applications. Such an assessment is carried out in the following subsections.

4.2. Detailed validation of the new model The first part of the validation consisted of a total of 216 matching pairs of acdom(λ)mod and acdom(λ)insitu obtained from the NOMAD datasets. Statistical analyses of models used to evaluate the consistency between these datasets and regressional parameters defining the fitted acdom(λ) are described in Table 1A. Fig. 7 shows the scatter plots of the acdom(λ)mod versus and acdom(λ)insitu at six SeaWiFS wavebands (412, 443, 490, 510, 555, and 670 nm). It is important to note that HM and EM models tended to have performed in a similar manner showing large underestimations at lower CDOM contents and overestimations at higher CDOM contents. As a result, their predicted values deviated downward or upward from the 1:1 line. Although this trend is apparent at all wavelengths, acdom values of the former model were increasingly overestimated at longer wavelengths when compared with those of the later model. The high inaccuracy of HM may be due to the fact that the absorption by CDOM generally follows an exponentially decreasing function but not a smoothly varying function as described by the HM model (Bricaud et al., 1981; Jerlov, 1976; Roesler et al., 1989; Shifrin, 1988). Looking at the RMSE and MRE errors (MRE −48.19 to −27.57% and RMSE 0.42–0.766 (wavelength average 0.511) for HM; MRE −48.19 to −7.41% and RMSE 0.42– 0.383 (average 0.398) for EM), and slopes (0.73–0.87 for HM; −0.38 to −0.30 for EM), intercepts (−0.389 to −0.897 for HM; −0.389 to

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P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521

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aCDOM (350) (m-1)

10 5

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0.8

1

Rrs (443)/Rrs (555) Fig. 6. In situ acdom coefficients at two wavelengths (350 and 412 nm) versus the ratio of in situ Rrs at 443 nm and 555 nm plotted on a log–log scale (N = 211).

−0.306 for EM) and correlation coefficients (0.828–0.634 for HM; 0.828–0.633 for EM), it becomes obvious that the EM model appears to be more accurate than the HM at all visible wavelengths as previously noted. Nevertheless, high MRE and RMSE of the absorption coefficients of EM are not acceptable particularly in the blue wavebands, which are especially important at low values of CDOM concentrations. The previous investigations showed that such errors may be due to parameterization that varies significantly for different waters (Babin et al., 2003). In contrast, the acdom(λ)insitu and acdom(λ)model (new model) coefficients lay close to the 1:1 line indicating that the agreement between them is very good. As expected from the analysis, it is observed that MRE and RMSE values for the new model are significantly lower (MRE −5.64–3.55% and RMSE 0.203–0.318) than those derived for the EM and HM. High linear correlations, slopes close to unity and low intercept values (−0.02–1.13) all confirm that the acdom predicted by the new model at 412, 443, 490, 510 and 555 nm match their in situ acdom(λ) values very well. A validation of the models was also performed by comparing SeaWiFS estimates with concurrent in situ acdom(λ) measurements (Fig. 8, Table 1B). When applied to SeaWiFS reflectance measurements, it can be seen that both HM and EM exhibited their general behavior of the underestimation (when acdom(412, 443) b0.1 m− 1 and acdom(490, 510) b0.03 m− 1) and overestimation (when acdom(412, 443) N0.1 m− 1; acdom(490, 510) N0.03 m− 1; acdom(555) N0.01 m− 1; acdom(670) N0.001 m− 1) of the acdom values at the given wavebands. However, the analysis of the statistical parameters showed that the overall models' performance is notably increased (MRE −5.64 to −15.40% and RMSE

0.297–0.586 for HM; MRE −5.64 to −0.98% and RMSE 0.297–0.322 for EM), as compared to results from purely in situ data (previous validation). This may be explained by the relatively less and restricted range of data used for the validation. Contrary to what was observed in the analysis of purely in situ bio-optical datasets, acdom(λ) estimates (SeaWiFS) of the new model are tightly consistent with the in situ acdom(λ) data and agree better than the other two models when applied to the SeaWiFS data. Although MRE, slopes, intercepts and R2 values remain quite stable, RMSE of the new model are considerably lower (0.136–0.241) than those found with in situ data and those of the other two models, which signify that a better estimate occurs in waters with low and medium acdom contents. In order to further examine the spectral deviation by the three models, Fig. 9 shows mean values of the measured and modeled absorption coefficients of CDOM of all the data previously used for the validation and of the simulated data provided by the IOCCG working group. Note that the mean acdom spectra of in situ and simulated data show a near-exponential decay in all wavelengths from 410 nm to the instrument detection limit around 670 nm. This shape is currently believed to be associated with either the charge transfer interactions between donor–acceptors formed by the oxidation of the aromatic polymers present in the natural sample (Del Vecchio & Blough, 2004), or the superposition of independent chromophores (Loiselle et al., 2009). At wavelengths above 443 nm, independent of the datasets, both EM and HM tended to overestimate the absorption coefficients and this overestimation is further magnified by the later model. At wavelengths below 443 nm, spectral slopes of acdom predicted by HM and EM sharply increase eventually converging at 412 or 410 nm. This increase at short visible wavelengths is highly pronounced at increasing CDOM concentrations (low in middle panel, medium in top panel and high in bottom panel). On the contrary, the new model provided acdom values closest to the measured (in situ) values although showing slight differences with in situ acdom spectra (top and middle panels) in the blue region and almost negligible differences over wavelengths from 443 to 670 nm. The small differences may be attributed to the discrepancies in the absorption measurements provided by different instruments with different calibration standards, correction and analysis methods (Twardowski et al., 2004). Very slight underprediction of absorption curve is noticed in the simulation dataset (bottom panel), likely to be caused by the presence of offsets in that data at 670 nm. Overall, the new model provides an accurate estimation of the CDOM absorption coefficients in waters within the coastal and offshore domains and therefore enables us to extend it for global applications with remote sensing data. 5. Application to satellite imagery 5.1. Regional application The SeaWiFS local area coverage (LAC) Level 1A (~1 km/pixel at nadir) reflectances for the period 19 September 2000 were processed by the new models and surface distributions of CDOM and γowere derived in the context of assessing the models' performance on the regional scale (S and γ not used here for brevity). Known issues with the standard atmospheric correction procedure in turbid waters and yellow dustdominated coastal areas were avoided by using a regional atmospheric correction technique proposed for this region (Shanmugam & Ahn, 2007). To characterize better the effects of in situ processes on CDOM distribution, surface salinity was also derived from the CDOM absorption based on the relationship of Ahn et al. (2008). Fig. 10 shows regional patterns of acdom(350) and acdom(412), γ o and salinity in the East China Sea (ECS), Yellow Sea and coastal areas of Korean peninsula and Vladivostok and East Sea (ES). High values of CDOM (acdom(350)N 0.35 m− 1; acdom(412) N 0.17 m− 1) are found within regions of Yangtze River (YR) estuary and all along the coastal areas of Korean peninsula

P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521

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and Vladivostok while low values (acdom(350 and 412) b 0.12 m− 1) are observed within offshore domains where the Kuroshio current flows northward and then turns northeastward towards the East Sea (ES) (Shanmugam et al., 2008). Moderate values (acdom(350) 0.2–0.3 m− 1; acdom(412) 0.08–0.12 m− 1) typically occur in the YS waters. Note that at 412 nm waveband the strongest CDOM signals are still persistently associated with the YR plume and Korean and Vladivostok coastal waters, which indicate that the source of CDOM in these regions appears to be terrestrial in origin because of the strong inverse relationship of CDOM (as consequence of its fresh water origin and conservative behavior) with salinity (Ahn et al., 2008; Binding & Bowers, 2003; Boss et al., 2001; Twardowski & Donaghay, 2001). To verify this, Fig. 10c depicts the γ o signal as a proxy for CDOM composition. Interestingly, the γ o image demonstrates the rich diversity of patterns in CDOM in this region, allowing its partition into five broad pools (categories). γ o values higher than −0.6 (Type 1) indicate that the CDOM pool is mainly originating from a freshwater, terrigenous source (and perhaps from the effects of combined upwelling and high runoff/continental effects), γ o values between −0.2 to −0.6 (Type 2) are characteristic of type 1 CDOM but largely governed by advection/transport processes, γ o values −0.2–

0.2 (Type 3) are indicative of physical mixing between the terrestrial CDOM and CDOM produced in situ by phytoplankton activities (actually in situ production), values 0.2–0.6 (Type 4) are mostly of CDOM originating from in situ production processes (phytoplankton), γ o values 0.6–1 correspond to the background/marine CDOM (type 5) produced in the open ocean as a result of heterotrophic processes near the surface. This pool of CDOM is never completely eliminated by solar bleaching or other natural processes and the local sources and sinks are sufficient to account for its optical activity and seasonal cycles in the open ocean (Nelson et al., 1998). Having partitioned into different types of CDOM, surface distributions of γ o and salinity showed typical coastal patterns in the nearshore region, with highest negative values (−1 to −0.6) of γ o being associated with lowest salinity (27–30 psu) with freshwater input from YR and several other rivers of the Korean peninsula and the eastern Russia (Fig. 10c and d). These rivers are predominantly fed by summer rains, resulting in intense CDOM patches in the coastal regions. Looking at the ECS domain, the data showed unusually high concentrations of CDOM offshore. This could be attributed to an offshore extension of the YR plume depending on physical circulation in late spring and summer

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(Chen et al., 2008; Shanmugam et al., 2008). Because CDOM export is highly sensitive to circulation than particle export, further eastward dispersal of the terrigenous CDOM pool was detected over the broad ECS shelf, reaching as far as Jeju Island and the shelf-break, with varying salinities of 30–33 psu. During this period, favorable wind flow and the presence of an anticylonic eddy (by the intrusion of East Korean Warm Current) off the Korean East coast coupled with the high river flow tended to entrain high CDOM from coastal waters to the offshore and resulted in a variable mixing of terrestrially induced CDOM and CDOM produced locally (brown–yellow patterns in Fig. 10c). In contrast to what was observed as the complex patterns of CDOM export as induced by physical circulation in the ECS and ES, in the central YS away from the coast, γ o values ranged from −0.4 to −0.15 exhibiting almost a homogeneous patch (also see salinity 32–33 psu) which was not contiguous to the region of high CDOM in the vicinity of YR month; therefore, it is likely that CDOM is originated from the combined effects local river inputs, continental influences (dust and anthropogenic inputs), phytoplankton production (Ahn et al., 2004). A pronounced CDOM maximum (γ o 0.6–1) was found in the open ocean as characterized by the surface salinity of N34 psu.

5.2. Global application Seasonal global area coverage SeaWiFS ocean color data (composite images sampled at 9 km) for the year 1998 and 2007 were processed by the new models to enable the global assessment of CDOM and its variations in the world's oceans. Such analyses of the global data are essential for the long-term evaluation of ocean ecosystem health and for understanding changes in the ocean carbon cycle (Gregg & Conkright, 2001), in addition to helping to establish linkages across and between ocean basins. Fig. 11 shows the global estimates of CDOM and its composition (γ o) as derived from SeaWiFS images for the year 1998 (similar analysis done for the year 2007 but not included here for brevity). Overall, more intense CDOM patterns are found in the ocean margin waters and within regions of persistent large-scale upwelling and eddy systems (e.g., subarctic gyres, equatorial divergences, eastern/subarctic boundary currents, etc.) and less intense patterns in the subtropical ocean gyres/downwelling systems, with seasonal maxima in June–August and minima in December–February. The spatial distribution of CDOM shows a broad minimum in the tropical ocean and appears similar to chlorophyll or

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primary production distributions (Yoder & Kennelly, 2003) suggesting that similar processes may regulate CDOM (Siegel et al., 2002). As expected, values of CDOM are consistently greater for the Northern hemisphere than the Southern ocean, although CDOM maxima are evident in the Southern ocean during December–February and September–November. Comparison of these images for 1998 and 2007 reveals a notable interannual seasonal variability over tropical and subtropical waters particularly in June–February, likely to be caused by the ENSO event in 1998 (Yoder & Kennelly, 2003). Looking more closely at the fine scale details, at high mid-high latitudes (35–75° N), similarities and differences in seasonal changes of CDOM can be easily observed between two sites from the North Atlantic and North Pacific subtropical gyres (Figs. 12 and 13 top panels). Although both sites showed stronger seasonal patterns, CDOM concentrations are enhanced in March–May and June–August and reduced in December–February in the North Atlantic in contrast to the larger CDOM in March–May and September–November and reduced CDOM in December–February in the North Pacific subtropical gyres. Strikingly, high-latitude CDOM patterns (Arctic) are considerably similar to the CDOM patterns found in the North Atlantic, with changes being most evident in December–February (devoid of patterns due to the ice cover). Increasing arctic river discharge from six major rivers (Yenisey, Lena, OB, Pechora, Kolyma, and Severnaya Dvina with average annual discharge of about 128 km3 per year) has been reported to have caused high CDOM contents in the arctic waters during March–August (Manizza et al., 2009; Peterson et al., 2002; Retamal et al., 2007).

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At latitudes from 45 to 70°N, the highest concentrations of CDOM are found in coastal margins of oceans and in semi-enclosed seas, where direct sources of terrestrial organic matter are found (yellow color in Fig. 12). The geographical extent of these terrestrially dominated regions varies seasonally, depending on the magnitude of freshwater inputs, and its dilution by physical mixing processes in the coastal areas. Note the maximum CDOM concentrations present in the Baltic Sea, with a weak seasonality as compared to the central North Atlantic, and most of which are of terrestrial in origin connected to a very high input of freshwater from the surrounding drainage area with limited water exchange with the North Sea through the Danish Straits (Aas, 2000; Kratzer et al., 2003). Indeed, autochthonous production of CDOM cannot be ignored in the Southern Baltic Sea in March–May and September–November (Kowalczuk et al., 2006). Such production is also clearly seen in the Black Sea during December– February and September–November, while the previously elevated CDOM was significantly removed by solar bleaching and microbial degradation processes in the open sea although its coastal areas were predominantly affected by continental inputs in March–May and June–August (Ducklow et al., 2007). Persistent elevated concentrations of CDOM are evident particularly on the eastern England, likely to be induced by river plumes there and transported to the open sea (Foden et al., 2008; Worrall et al., 2003). Observations from the western North Atlantic Ocean show strong seasonal patterns, with a large pulse of CDOM in the Baffin Bay of Greenland, Hudson Strait and Canadian coastal areas and a relatively small pulse in the open ocean during March–November. It becomes apparent that the offshore patch is contiguous (as can be seen from the patterns of brown color overlying that of green color in Fig. 12) to the region of high CDOM along the coast, except in the southern North Atlantic; therefore, it is likely that it might originate from local river inputs. This reflects that the offshore distribution of terrestrial CDOM is mainly driven by the rapid advection and mixing processes of the basin and demonstrates that remineralization in the ocean interior is not a significant sink for CDOM (Nelson et al., 2007). It seems likely that in the Greenland Sea and North Atlantic a variable low intensity oceanic source is present over a background pool of refractory CDOM (Stedmon & Markager, 2001). Overall, our results may give clues to an ongoing debate about how much of the CDOM pool has its origin from land and how much is produced autocthonously in the marine environment in the coastal areas and in the open oceans, like the Greenland Sea (Opsahl et al., 1999; Wheeler et al., 1997). Similar to the North Atlantic, our results showed strong seasonal increases in CDOM concentrations in the tropical and subtropical systems in the North Pacific (Fig. 13 top panels; also seen Figs. 10 and 11), where the surface waters of the March–May and September– November periods were enriched in CDOM by the input of terrigenous organic matter via high fluvial fluxes to the coastal areas of Russia, Korea and China (yellow color). Moderate CDOM contents were found to be carried into continental slope waters by the meso-scale eddy activities, submesoscale front variability, western boundary current (Kuroshio), and upwelling (Sasai et al., 2007; Shanmugam et al., 2008). The intermediate (green color) between terrestrial and oceanic CDOM signals (green color in Fig. 13 top panels) indicates the production of CDOM from greater mixing and higher biological productivity, a characteristic of systems that receive high inputs of new nutrients over the late December–February and March–May periods (Yamada et al., 2004). Not surprisingly, intricate CDOM patterns are captured throughout the Arabian Sea with seasonal maxima centered in June–August and September–November and minima in December–February. The spatial pattern of CDOM is strongly negative (γ o N −0.6) in zones under the direct influence of continents on the southern coast India and Oman in the Arabian Sea. Slightly away from coastal areas, mixing structures are found to move southward along the African coast and along the southern Indian coast where γ o values are diluted to −2–

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Fig. 10. (a–c) Surface patterns of acdom(350), acdom(412) (m− 1) and γo predicted by the new models and (d) salinity field (psu) estimated by the regional algorithm (Ahn et al., 2008) using SeaWiFS image (19 Sept. 2000) for the East China Sea, Korean Sea and East Sea.

0.2. Further offshore, CDOM is more extensively dispersed eastward in the high flow period (Southwest Monsoon Current) in June–August (Fig. 13 bottom panels; also see Fig. 11 bottom panels). Higher CDOM and larger dispersion are likely caused by high rate of upwelling associated with monsoon circulations and advection processes (Breves & Reuter, 2000; Vinayachandran et al., 2004). In situ production of CDOM typically occurs in the open sea waters and lowest concentrations (b−4) are evident in the Indian Ocean due to the absence of terrestrial component or less significant biological production. At mid-latitudes (20–45°N), the Gulf of Mexico (GOM) showed distinct maxima in December–February and minima in September– November (Figs. 11 and 14 top panels). High CDOM plume is generally attached to the northern GOM coast, a region dominated by freshwater inputs from the Mississippi River through the Birdfoot region and to the west by discharge from the Atchafalaya River. This plume is extensively dispersed into the western Florida Shelf in December–February and March–May and moderately dispersed in June–August and September–November (Hu et al., 2006; MullerKarger et al., 1991; Walker et al., 1996) (Fig. 14 top panels; and Fig. 11 bottom panels). The data show unusually large patches of low concentrations of CDOM offshore, extended southward as far as the

southern coast of Gulf of Mexico in December–February. Although these were attributed to an offshore extension of the Mississippi River plume (Gilbes, 1996), other processes such as phytoplankton production or anthropogenic inputs might play an important role in enhancing CDOM levels over the background marine pool. Note that highly intense thick patches of CDOM are found to be hugging the coastal and continental shelf regions of the east coast of the U.S and Canada, with seasonal cycle similar to that of the GOM. It is speculated that the major source of CDOM in these coastal regions of the December–February period is river runoff of terrigenous organic matter (Keith et al., 2002; Moran et al., 1991). In June–August, CDOM absorptions in the region rapidly decline to minimum values as surface waters of these coastal regions respond to intrusions of higher salinity waters from the ocean. Away from continental margins (e.g., Sargasso Sea), the effect of rivers declines and CDOM is mostly composed of material produced in the oceans, thereby showing weak seasonality. In contrast, the west coast of the USA and Canada presents a stark contrast, with seasonal maxima in June–August and minima in December–February (see the yellow patch indicative of terrestrial CDOM). Highest CDOM concentrations are evident in the Gulf of Georgia waters and along the west coast coinciding with the highflow period in late March–May and June–August. The likely source for

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Fig. 11. Global patterns of acdom(350) (m− 1) and γ o from SeaWiFS images for the December–February (winter), March–May (spring), June–August (summer) and September– November (autumn) 1998. acdom(350) (m− 1) and γ o were calculated by the new models. SeaWiFS composite images of remote sensing reflectance (Rrs) sampled at 9 km for wavebands centered on 443 nm and 555 nm were used for deriving these maps.

the high CDOM contents is the Fraser River plume and perhaps anthropogenic inputs that are strongly dependent on wind forcing (e.g., Twardowski & Donaghay, 2001). Autochthonous production of CDOM is found to be a significant source of CDOM in the California Current, with a maximum in December–February and a minimum in June–August, which is consistent with previous studies (e.g., Kahru & Mitchell, 2001). Looking at the South America showing contrasting temporal changes, three areas of more persistent elevated concentrations of CDOM are observed, with two of them being centered around the equator (Peru coast/Equatorial Current regions, and Amazon and Orinoco River outflow regions with overall maximum concentrations in June–August and minimum in December–February) and the other being associated with South Georgia Region in the Atlantic south of the equator (maxima in December–February and minima in June– August). Previous studies showed significant and positive correlations between CDOM and chlorophyll in the Peru upwelling (Bruland, 2002; Hutchins et al., 2002; Yoder & Kennelly, 2003) which indicate that the decomposition and degradation of phytoplankton may be an important source of CDOM other than the inputs from several rivers. The CDOM distribution clearly reflects the longitudinal pattern of production, wherein CDOM is produced in the Peru upwelling system and transported by the Peru Current System and South Equatorial

Current toward the central Pacific Ocean where it is consumed and subducted (evident in Figs. 11 and 14 bottom panels). In contrast, the γ o images showed a large area of intense CDOM (yellow color showing maxima in June–August) in the Amazon and Orinoco River outflow regions, where previous studies concluded that high amounts of CDOM are discharged by outflows of these rivers and transported hundreds to thousands of kilometers away from the coast by Southern Equatorial and Caribbean Currents (Cunha et al., 2007; McClain et al., 1997; Muller-Karger et al., 1989; Siegel et al., 2002). Low CDOM concentrations also occur off the coast and may be attributed to enhanced primary production in the ocean. On the other hand, observations of a significant seasonal cycle in the CDOM distribution at mid-high latitudes in the South Georgia Region of South America provide further evidence supporting the roles of continental effects and phytoplankton production on CDOM dynamics. Very high CDOM values are found from Parana River to Strait of Magellan in December– February and June–August, but more extensive eastward dispersion of the coastally induced CDOM, reaching as far as the offshore south of Australia, is particularly evident in December–February. It seems likely that high CDOM contents along the coast have a terrestrial origin and are noticeably transported into the offshore by Malvinas Current, where the effects of continental effects decline and CDOM is mostly composed of material produced by phytoplankton blooms

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Congo River outflow off western African coast in June–August (Cunha et al., 2007). At this time, the Benguela Current flowing northward joins the Angola Current at the Angola-Benguela front causing a deep penetration of high-CDOM plume into the Atlantic Ocean (Fig. 14 right panels; Fig. 11 bottom panels). As it becomes obvious, local production of CDOM is characteristic of such a frontal system in the offshore region. Considering the North African coast which is arid, so that the contribution of runoff to the coastal waters is minimal, coastal CDOM distributions reflect terrestrial input as the results of frequent dust deposition (continental effects), however open ocean CDOM distributions in the equatorial divergence zone appear to be regulated by phytoplankton production in response to the dust events (Siegel et al., 2002; Walsh et al., 2006). 5.3. Satellite assessments of the S, γ, and γ° and their implications

Fig. 12. Enhanced views of acdom-compositional patterns (γo) in the North Atlantic and Arctic obtained from SeaWiFS images for the December–February, March–May, June– August and September–November 1998.

triggered by high inputs of new nutrients resulting from strong upwelling in December–February (Fig. 14 bottom panels, and Fig. 11 bottom panels) (Cunha et al., 2007; Meskhidze et al., 2007; Yoder & Kennelly, 2003). Dynamics of CDOM in the Antarctic Circumpolar Current (ACC) region of the SO between 30°S and 60°S suggest that eddies and frontal circulation can lead to redistribution of major nutrients and can therefore sustain phytoplankton growth during its advection in ACC currents. The coupled physical and biological processes ultimately result in enhancement and subsequent eastward advection of CDOM in ACC currents (Hansell & Carlson, 2001). Upon closer inspection, extensive regions of elevated CDOM are found along the west coast of Africa, where seasonal cycles show maxima in the southern region during the June–August period and maxima in the northern region during the December–February period (Fig. 14 right panels and Fig. 11 bottom panels). It is likely that elevated CDOM along the southwestern South African coast are the results of enhanced phytoplankton concentration triggered by an active coastal upwelling of the Benguela Current that flows northward along the coast and then turns northwestward in June–August. Nevertheless, under the effect of waves, re-suspension of locally formed sediments, or mobilization of organic compounds from the bottom and sea grass, cannot be excluded, at least in the shallowest waters (Morel et al., 2006). On the other hand, highly intensive patches of CDOM (middle west region) are likely due to the increased

This study allows for the first time to our knowledge the comprehensive mapping of the slope S, γ, and γ° parameters. Our findings based upon global observations reveal the dramatic variability in the CDOM absorption that clearly illustrates the high degree of variability present in coastal and its immediate offshore waters that could not have been resolved by traditional discrete CDOM sampling techniques (Fig. 15). This is of particular importance to remote sensing applications in the continental shelf waters, where the horizontal variation in slope parameters is the largest due to the dilution of terrestrial CDOM down to similar levels as oceanic CDOM. This variability thus reflects the composition of the material present within the CDOM pool. Given this, slope maps would allow a set of environmental paradigms to be tested by means of relating variability in these values to autotrophic particulate carbon, total organic carbon, primary productivity, and nutrient fluxes. This will be especially important for coastal water quality managers where CDOM cycling is greatest reflecting higher levels of primary production (which yield larger quantities of total DOM) and CDOM loading from terrestrial runoff. It is also interesting to note the lower slope values in the open ocean regions, which suggest that CDOM in these waters exhibit a similar behavior to that in the coastal waters. A typical example is the North Atlantic which is under the influence of local terrestrial input from its coastal regions (e.g., North Sea, Baltic Sea, Greenland Sea, and Labrador Sea), whereas the deeper waters of the North Atlantic are more oceanic in character. The results from previous studies combined with this study's results allow us to conclude that the major and variable fraction of the CDOM in the North Atlantic is derived from autochthonous sources and advection processes. Thus, the optical properties and abundance of CDOM in offshore waters vary seasonally with respect to productivity in the water column and solar irradiance. As the autochthonous CDOM degrades, the spectral slope coefficient increases. These results show that it is possible to model and distinguish between different pools of CDOM in the coastal and oceanic environments, using optical measurements provided by the satellite ocean color sensors. This in turn will help to investigative a useful tool in coastal ocean carbon cycling studies. The models presented in this study also provide a useful tool for CDOM studies, aiding the identification and location of non-conservative processes occurring during the mixing of CDOM reservoirs. 5.4. Relationship between river discharge and global terrestrial CDOM distribution Our observation suggests that there is the consistency of the global terrestrial CDOM distribution with characteristics of the river inputs in coastal regions. In the open sea, the consistency with the large-scale circulation implies vertical mixing and advective processes play an important role in the distribution of CDOM. To examine this, observed monthly river discharge records were obtained from the Global Runoff Data Centre (GRDC) for the 24 major river basins in all

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Fig. 13. Enhanced views of acdom-compositional patterns (γo) in the Northwest Pacific (top four panels) and Arabian Sea–Indian Ocean–Bay of Bengal (bottom four panels) obtained from SeaWiFS images for the December–February, March–May, June–August and September–November 1998.

continents and in various climatic zones, including tropical (Amazon and Congo), arid (Amu Darya, Euphrates, Huang He, Murray, Nile, Rio Grande, and Syr Darya), midlatitude rainy (Columbia, Danube, Mississippi, Parana, Rhine, and Volga), Asian monsoon (Changjiang, Ganges, and Mekong), and high latitudes (Amur, Lena, MacKenzie, OB, Yenisei, and Yukon). Averaged over many years for four seasons, the river discharges represent natural discharges but are affected by evaporation from the river surface and an artificial control of the river flow during specific periods. Fig. 16 depicts seasonal patterns of the global coverage area with terrestrially-derived CDOM (top panel with γ o −1 to − 0.6 for both 1998 and 2007) and the mean global river discharge data (bottom panel). All two years images generally showed similar temporal patterns, although the somewhat higher interannual variability in the spring (March–May), summer (June– August) and autumn (September–November) was likely to be caused by the ENSO event in 1998. Comparison of these patterns with seasonal mean river discharge records (bottom panel) reveals important information: i.e., the seasonal global coverage area of the terrestrially-derived CDOM is strongly consistent with the seasonal mean river discharge data, with a maximum in summer and a

minimum in winter (December–February). Note that the seasonal global coverage area of the terrestrially-derived CDOM (for 1998 and 2007) steadily increases with increasing river discharge from winter to summer and decreases towards the autumn as river discharge declines. Evidence supporting the role of vertical mixing/advection processes and primary production (in situ) could be found in observations of increased global coverage area of the terrestrial CDOM combined with in situ CDOM. Its seasonal patterns do not correspond to the seasonal mean river discharge data because of the deviations arising from transport processes and in situ production that may include resuspension from the bottom, reworked products of microbial production and increased planktonic production (Siegel et al., 2002; Twardowski & Donaghay, 2001). Based upon the observed global patterns of CDOM, it is hypothesized that the change in river discharge largely affects the river runoff of terrigenous CDOM in the coastal, estuarine and continental shelf regions, whereas the change in circulation and primary production affects the in situ CDOM and terrestrial sources in the open sea. It is predicted that the regions of high-latitude rivers (Amur, Lena, MacKenzie, OB, Yenisei, and Yukon), where the discharge increases, and the peak timing shifts earlier

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photobleacing, subsurface water mass renewal and bacterial and primary production regulate the global distribution of CDOM in the open oceans (Nelson et al., 1998; Siegel et al., 2002). 6. Implications for studying of dissolved organic carbon (DOC)

Fig. 14. Enhanced views of acdom-compositional patterns (γo) in the Gulf of Mexico (top two panels) and South Atlantic regions (bottom two panels) obtained from SeaWiFS images for the December–February and June–August 1998.

because of an earlier snowmelt caused by global warming (Cunha et al., 2007; Pastor et al., 2003), are likely to be affected by high CDOM contents of the terrestrial origin than currently observed in spring. As the discharge tends to decrease for the rivers in Europe to the Mediterranean region (Danube, Euphrates, and Rhine), and southern United Sates (Rio Grande), the current CDOM trend is likely to decline in these regions. Previous studies have noted the lack of correlation between CDOM and chlorophyll concentration in the open ocean (where γ o 0.6–1), which suggests that CDOM production is not simply the result of primary production but likely to result from heterotrophic bacterial productivity (Nelson et al., 1998; Siegel et al., 2002). Little is known concerning its dynamics, but it is likely that rates of

Accurate quantification and partitioning of CDOM and its global coverage patterns provide opportunities to study the DOC export from major rivers to the ocean. Recent studies showed that DOC export draining peat-rich uplands has increased by several orders of magnitude over the last two decades (Baker & Spencer, 2004; Worrall et al., 2003). The increase has been suggested to result from either or both of rising temperatures and catchment land use change driving the process by stimulating the export of DOC from peatlands (Worrell et al., 2003). With global climate change the rate of movement is likely to increase further if global temperatures increase; thus resulting in a key terrestrial carbon store relocating to the oceans. It is noteworthy that there exists a good correspondence between terrestrial-origin CDOM and DOC distributions in estuarine and coastal waters (Del Castillo & Miller, 2008; Ferrari, 2000; Mannino et al., 2008; Twardowski & Donaghay, 2001; Vodacek et al., 1997), hence it has been proposed that DOC concentrations can be remotely monitored using satellite retrievals of CDOM (Ferrari, 2000; Hoge et al., 1995; Mannino et al., 2008). To truly estimate DOC transport remotely, simple empirical relationships between DOC and CDOM, CDOM and salinity, and salinity and river flow were established using in situ measurements (Ahn et al., 2008; Baker & Spencer, 2004; Del Castillo & Miller, 2008; Twardowski & Donaghay, 2001). Although such relationships can be applicable to relatively small spatial and temporal scales in coastal regions, when derived using large in situ datasets

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2.00x107

production, with secondary maxima typically between 40° and 50° latitude in both hemispheres, which is consistent with CDOM patterns observed in this study. At present, it seems likely that knowledge on CDOM and DOC relationships is insufficient because only few, although significant, works have been specifically addressed to this subject. This potentially limits the DOM/DOC flux studies to evaluate the influence of the coastal zones on the open ocean. It is clear that the present models have great potential for remote sensing to quantify the fluxes of terrigenous and ocean CDOM and can lead to an increased understanding of the terrigenous DOC export in the coastal ocean.

Global coverage area (km2)

o

γ = –1 ~ –0.6 1.80x107

2007

1.60x107 1998 1.40x107 1.20x107 1.00x107 8.00x106 Dec-Feb.

March-May

June-Aug.

1519

Sept-Nov

7. Summary and conclusions

Period

River discharge (m3 s-1)

3.0x104 2.7x104 2.4x104 2.1x104 1.8x104 1.5x104 1.2x104 Dec-Feb.

March-May

June-Aug.

Sept-Nov

Period Fig. 16. Seasonal patterns of the global coverage area with CDOM terrestrially transported into the coastal seas via rivers and runoff from land (top panel with γo −1 to −0.6). Bottom panel shows the seasonal river discharge records from the Global Runoff Data Centre (GRDC).

from a wide range of the coastal and shelf environments, they will have practical implications for remote sensing leading to a better understanding of any potential long term trends in carbon export by rivers which has global importance in terms of understanding carbon cycling and budgets (Hansell & Carlson, 2001). Detailed long term spatial and temporal monitoring of DOC from such relationships would also help separate the relative contributions of land use and climate change factors on DOC export to the ocean in different catchments. In open ocean environments, simple linear relationships may not work reasonably well as there is the lack of a correspondence between global DOC and CDOM distributions which is at odds with investigations made in coastal environments (Hansell & Carlson, 2001; Nelson et al., 1998; Siegel & Michaels, 1996; Siegel et al., 2002). This could be attributed to several factors including the destroy of colored compounds without significantly altering organic carbon content by photobleaching processes (e.g., Kieber et al., 1989), the deep winter mixing of the upper water column that brings up new nutrients and CDOM from depth and reduce near-surface DOC concentrations, and the onset of summer stratification that results in a reduction in mixed layer CDOM absorption while upper layer DOC from late spring remains elevated (Carder et al., 1999). This suggests that the mechanisms deriving the surface DOC distribution are different from those deriving the CDOM distribution. It should be noted that zonal average semilabile DOC from several models and that based on observations revealed highest DOC in tropical waters and decreased pattern with latitude (Najjar, 2007). They also noted that this is only hinted at in the observation-based estimate and perhaps cannot be captured in simple algorithms based on SST. The model DOC distribution however clearly reflected the latitudinal pattern of

New models have been developed for satellite remote sensing to predict the spectral behavior of CDOM and discriminate its signals into different types of pools in the global ocean. There was an excellent quantitative agreement between the absorption spectra estimated by the present models and the spectra measured by spectrophotometers on discrete water samples. The model achieves the uncertainty less than 10% (in the visible domain) in our remote assessments of the pools of CDOM in different regions, so that the data can be used for calibration or validation exercises with equal efficacy. Some of the variability between the estimated and measured CDOM values can be attributed to the different methods for analyzing water samples with different correction procedures. The good correlation between the estimated and measured spectra suggests that the present model is capable of accurately quantifying absorption coefficients of CDOM and separating its signals into five broad pools. The origin of CDOM pools has not been clarified by recent investigations. Our findings based upon global observations demonstrate that terrestrially transported CDOM and a pool produced in offshore waters can account for seasonal and annual fluctuations and provide information that the deep-ocean may have a significant terrestrial component. From this study, it also becomes clear that finescale structure in CDOM distributions exists and is more spatially and temporally dynamics than currently thought in coastal regions. Therefore, the present models (including γ°) are expected to allow an increased understanding of the properties and dynamics of CDOM that include origin of CDOM, rates of formation of CDOM and its global distribution, and the ultimate goal of CDOM in biogeochemical cycles. The new models may help standardize the analysis of the CDOM absorption curve and provide more complete information on the chemical composition of CDOM. The results discussed in this paper have important implications for ocean color research and provide exciting opportunities for remotely assessing ocean biological and biogeochemical processes. The present models and parameterizations will contribute to significantly improve the modeling of the inherent optical properties and thus the analysis of water-leaving radiances in optically complex waters, which are of great interest for satellite remote sensing applications.

Acknowledgements This work was supported by grants from the Space Application Centre (SAC), Ahmedabad (No.OEC/0809/089/SACX/PSHA). The author gratefully acknowledges Dr. Yu-Hwan Ahn and Dr. Joo-Hyung Ryu, Korea Ocean Research and Development Institute, Seoul, Korea for the KORDI in situ datasets. The author gratefully acknowledges the NASA Ocean Biology Processing Group for making available the global, high quality in situ bio-optical (NOMAD) dataset as well as the global coverage SeaWiFS data to this study. The authors would also like to thank many scientists who have shared their in situ data in NOMAD and IOCCG databases.

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References Aas, E. (2000). Spectral slope of yellow substance: problems caused by small particles, Proceedings Ocean Optics XV, Monaco, 16-20 October Office of Naval Research, USA CD-ROM. Ahn, Y. H., Shanmugam, P., & Moon, J. E. (2004). Spatial and temporal patterns in satellite-derived chlorophyll-a concentration and their relation to oceanic processes in the East China Sea and Yellow Sea. Proceedings of the Spring Meeting of the Korean Society of Oceanography, Pusan, Korea, 13–14 May 2004 (pp. 183−190). Ahn, Y. H., Shanmugam, P., Moon, J. E., & Ryu, J. H. (2008). Satellite remote sensing of a low-salinity water plume in the East China Sea. Annales Geophysicae, 26, 2019−2035. Austin, R. W. (1974). Inherent spectral radiance signatures of the ocean surface: Ocean color analysis. La Jolla, CA: Scripps Institute of Oceanography. Babin, M., Stramski, D., Ferrari, G. M., Claustre, H., Bricaud, A., Obolensky, G., & Hoepffner, N. (2003). Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. Journal of Geophysical Research, 108(C7). doi:10.1029/2001JC000882 Baker, A., & Spencer, R. G. M. (2004). Characterization of dissolved organic matter from source to sea using fluorescence and absorbance spectroscopy. The Science of the Total Environment, 333, 217−232. Bidigare, R. R., Ondrusek, M. E., & Brooks, J. M. (1993). Influence of the Orinoco River outflow on distributions of algal pigments in the Caribbean Sea. Journal of Geophysical Research, 98, 2259−2269. Binding, C. E., & Bowers, D. G. (2003). Measuring the salinity of the Clyde Sea from remotely sensed ocean color. Estuarine, Coastal and Shelf Science, 57, 605−611. Blough, N. V., & Green, S. A. (1995). Spectroscopic characterization and remote sensing of non-living organic matter. In R. G. Zepp, & C. Sonntag (Eds.), The role of non-living organic matter in the earths carbon cycle (pp. 23−45). : John Wiley & Sons. Boss, E., Pegau, W. S., Zaneveld, R. V., & Barnard, A. H. (2001). Spatial and temporal variability of absorption by dissolved material at a continental shelf. Journal of Geophysical Research, 106, 9499−9507. Boss, E., & Roesler, C. (2006). Over constrained linear matrix inversion with statistical selection. In Z. Lee (Ed.), Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications, IOCCG report number 5. Breves, W., & Reuter, R. (2000). Bio-optical properties of gelbstoff in the Arabian Sea at the onset of the southwest monsoon. Proceedings of the Indian Academy of Sciences Earth and Planetary Sciences, 109, 415−425. Bricaud, A., Morel, A., & Prieur, L. (1981). Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains. Limnology and Oceanography, 26, 43−53. Bruland, K. W. (2002). Phytoplankton iron limitation in the Humboldt Current and Peru upwelling. Limnology and Oceanography, 47, 997−1011. Carder, K. L., Chen, F. R., Lee, Z. P., Hawes, S. K., & Kamykowski, D. (1999). Semi-analytic MODIS algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures. Journal of Geophysical Research, 104, 5403−5421. Carder, K. L., Hawes, S. K., Baker, K. A., Smith, R. C., Steward, R. G., & Mitchell, B. G. (1991). Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products. Journal of Geophysical Research, 96, 20599−20611. Carder, K. L., Steward, R. G., Harvey, G. R., & Ortner, P. B. (1989). Marine humic and fulvic acids: Their effects on remote sensing of ocean chlorophyll. Limnology and Oceanography, 34, 68−81. Chang, G. C., Dickey, T. D., Mobley, C. D., Boss, E., & Pegau, W. S. (2003). Toward closure of upwelling radiance in coastal waters. Applied Optics, 42, 1574−1582. Chen, C., Xue, P., Ding, P., Beardsley, R. C., Xu, Q., Mao, X., Gao, G., Qi, J., Li, C., Lin, H., Cowles, G., & Shi, M. (2008). Physical mechanisms for the offshore detachment of the Changjiang Diluted Water in the East China Sea. Journal of Geophysical Research, 113, C02002. doi:10.1029/2006JC003994 Cunha, L. C., Buitenhuis, E. T., Quere, C. L., Giraud, X., & Ludwig, W. (2007). Potential impact of changes in river nutrient supply on global ocean biogeochemistry. Global Biogeochemical Cycles, 21, GB4007. doi:10.1029/2006GB002718 D'Sa, E. J., & Miller, R. L. (2003). Bio-optical properties in waters influenced by the Mississippi River during low flow conditions. Remote Sensing of Environment, 84, 538−549. D'Sa, E. J. R., Steward, G., Vodacek, A., Blough, N. V., & Phinney, D. (1999). Optical absorption of seawater colored dissolved organic matter determined using a liquid capillary waveguide. Limnology and Oceanography, 44, 1142−1148. D'Alimonte, D., Zibordi, G., & Berthon, J. -F. (2004). Determination of CDOM and NPPM absorption coefficient spectra from coastal water remote sensing reflectance. IEEE Transactions on Geoscience and Remote Sensing, 42, 1770−1777. Del Castillo, C. E., Coble, P. G., Conmy, R. N., Muller-Karger, F. E., Vanderbloemen, L., & Vargo, G. A. (2001). Multispectral in situ measurements of organic matter and chlorophyll fluorescence in seawater: Documenting the intrusion of the Mississippi River plume in the West Florida Shelf. Limnology and Oceanography, 46, 1836−1843. Del Castillo, C. E., & Miller, R. L. (2008). On the use of ocean color remote sensing to measure the transport of dissolved organic carbon by the Mississippi River Plume. Remote Sensing of Environment, 112, 836−844. Del Vecchio, R., & Blough, N. V. (2004). On the origin of the optical properties of humic substances. Environmental Science & Technology, 38, 3885−3891. Ducklow, H. W., Hansell, D. A., & Morgan, J. A. (2007). Dissolved organic carbon and nitrogen in the Western Black Sea. Marine Chemistry, 105, 140−150. Ferrari, G. M. (2000). The relationship between chromophoric dissolved organic matter and dissolved organic carbon in the European Atlantic coastal area and in the west Mediterranean Sea (Gulf of Lions). Marine Chemistry, 70, 339−357.

Ferrari, G. M., Dowell, M. D., Grossi, S., & Targa, C. (1996). Relationship between the optical properties of chromophoric dissolved organic matter and total concentration of dissolved organic carbon in the southern Baltic Sea region. Marine Chemistry, 55, 299−316. Foden, J., Sivyer, D. B., Mills, D. K., & Devlin, M. J. (2008). Spatial and temporal distribution of chromophoric dissolved organic matter (CDOM) fluorescence and its contribution to light attenuation in UK waterbodies. Estuarine, Coastal and Shelf Science, 79, 707−717. Garver, S. A., & Siegel, D. (1997). Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation 1. Time series from the Sargasso Sea. Journal of Geophysical Research, 102, 18607−18625. Gilbes, F. (1996). Analysis of episodic phytoplankton blooming and associated oceanographic parameters on the West Florida Shelf using remote sensing and field data. Ph.D. thesis, University of South Florida, St. Petersburg. Green, S. A., & Blough, N. V. (1994). Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters. Limnology and Oceanography, 39, 1903−1916. Gregg, W. W., & Conkright, M. E. (2001). Global seasonal climatologies of ocean chlorophyll: Blending in situ and satellite data for the Coastal Zone Color Scanner era. Journal of Geophysical Research, 106, 2499−2515. Hansell, D. A., & Carlson, C. A. (2001). Marine dissolved organic matter and the carbon cycle. Oceanography, 41−49. Hoge, F. E., Williams, M. E., Swift, R. N., Yungel, J. K., & Vodacek, A. (1995). Satellite retrieval of the absorption coefficient of chromophoric dissolved organic matter in continental margins. Journal of Geophysical Research, 102, 847–24,854 Hoge, F. E., & Lyon, P. E. (1996). Satellite retrieval of inherent optical properties by linear matrix inversion of oceanic radiance models: an analysis of model and radiance measurement errors. Journal of Geophysical Research, 101, 16631−16648. Hu, C., Lee, Z., Muller-Karger, F. E., Carder, K. L., & Walsh, J. J. (2006). Ocean color reveals phase shift between marine plants and yellow substance. IEEE Geoscience and Remote Sensing Letters, 3, 262−266. Hutchins, D. A., Hare, C. E., Weaver, R. S., Zhang, Y., Firme, G. F., DiTullio, G. R., Alm, M., Riseman, S. F., Aucher, J. M., Geesey, M. E., Trick, C. G., Smith, G. J., Rue, E. L., Conn, L., & Bruland, K. W. (2002). Phytoplankton iron limitation in the Humboldt Current and Peru upwelling. Limnology and Oceanography, 47, 997−1011. Jerlov, N. G. (1976). Optical oceanography. New York: Elsevier. Johannessen, S. C., Miller, W. L., & Cullen, J. J. (2003). Calculation of UV attenuation and colored dissolved organic matter absorption spectra from measurements of ocean color. Journal of Geophysical Research, 108(9), C3301. doi:10.1029/2000JC000514 Kahru, M., & Mitchell, B. G. (1998). Spectral reflectance and absorption of massive red tide off southern California. Journal of Geophysical Research, 103, 21601−21609. Kahru, M., & Mitchell, B. G. (2001). Seasonal and nonseasonal variability of satellitederived chlorophyll and colored dissolved organic matter concentration in the California Current. Journal of Geophysical Research, 106, 2517−2529. Keith, D. J., Yoder, J. A., & Freeman, S. A. (2002). Spatial and temporal distribution of colored dissolved organic matter (CDOM) in Narragansett Bay, Rhode Island: Implications for phytoplankton in coastal waters. Estuarine, Coastal and Shelf Science, 55, 705−717. Kieber, D. J., McDaniel, J., & Mopper, K. (1989). Photochemical source of biological substrates in sea water — Implications for carbon cycling. Nature, 341, 637−639. Kirk, J. T. O. (1994). Light and photosynthesis in aquatic ecosystems (2nd Edition). : Cambridge University Press. Kowalczuk, P., Egiert, J. S., Cooper, W. J., Whitehead, R. F., & Durako, M. J. (2005). Characterization of chromophoric dissolved organic matter (CDOM) in the Baltic Sea by excitation emission matrix fluorescence spectroscopy. Marine Chemistry, 96, 273−292. Kowalczuk, P., Stedmon, C., & Markager, A. S. (2006). Modelling absorption by CDOM in the Baltic Sea from season, salinity and chlorophyll. Marine Chemistry, 101, 1−11. Kratzer, S. (2003). Testing long-term trends in turbidity in the Manai strait, North Wales. Estuarine and Coastal shelf science, 56, 221−226. Kutser, T., Pierson, D. C., Tranvik, L., Reinart, A., Sobek, S., & Kallio, K. (2005). Using satellite remote sensing to estimate the colored dissolved organic matter absorption coefficient in lakes. Ecosystems, 8, 709−720. Lee, Z. P., Carder, K. L., & Arnone, R. (2002). Deriving inherent optical properties from water color: A multi-band quasi-analytical algorithm for optically deep waters. Applied Optics, 41, 5755−5772. Loiselle, S. A., Bracchini, L., Dattilo, A. M., Ricci, M., & Tognazzi, A. (2009). Optical characterization of chromophoric dissolved organic matter using wavelength distribution of absorption spectral slopes. Limnology and Oceanography, 54, 590−597. Manizza, M., Follows, M. J., Dutkiewicz, S., McClelland, J. W., Menemenlis, D., Hill, C. N., Townsend-Small, A., & Peterson, B. J. (2009). Modeling transport and fate of riverine dissolved organic carbon in the Arctic Ocean. Global Biogeochemical Cycles, 23, GB4006. doi:10.1029/2008GB003396 Mannino, A., Russ, M. E., & Hooker, S. B. (2008). Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight. Journal of Geophysical Research, 113, C07051. doi:10.1029/2007JC004493 Maritorena, S., Siegel, D. A., & Peterson, A. R. (2002). Optimization of a semianalytical ocean color model for global-scale applications. Applied Optics, 41, 2705−2714. Markager, S., & Vincet, W. (2000). Spectral light attenuation and the absorption of UV and blue light in natural waters. Limnology and Oceanography, 45, 642−650. McClain, M. E., Richey, J. E., & Brandes, J. A. (1997). Dissolved organic matter and terrestrial-lotic linkages in the central Amazon basin of Brazil. Global Biogeochemical Cycles, 11, 295−311. Meskhidze, N., Nenes, A., Chameides, W. L., Luo, C., & Mahowald, N. (2007). Atlantic Southern Ocean productivity: Fertilization from above or below? Global Biogeochemical Cycles, 21, GB2006. doi:10.1029/2006GB002711 Mobley, C. D. (1999). Estimation of the remote sensing reflectance from above-sea surface. Applied Optics, 38, 7442−7455.

P. Shanmugam / Remote Sensing of Environment 115 (2011) 1501–1521 Moran, M. A., Pomeroy, L. R., Sheppard, E. S., Atkinson, L. P., & Hodson, R. E. (1991). Distribution of terrestrially derived dissolved organic matter on the southeastern U.S. continental shelf. Limnology and Oceanography, 36, 1134−1149. Morel, A., & Gentili, B. (2009). A simple band ratio technique to quantify the colored dissolved and detrital organic material from ocean color remotely sensed data. Remote Sensing of Environment, 113, 998−1011. Morel, A., Gentili, B., Chami, M., & Ras, D. J. (2006). Bio-optical properties of high chlorophyll Case 1 waters and of yellow-substance-dominated Case 2 waters. DeepSea Research Part I, 53, 1439−1459. Morel, A., & Prieur, L. (1977). Analysis of variation in ocean color. Limnology and Oceanography, 22, 709−722. Muller-Karger, F. E., McClain, C. R., Fisher, T. R., Esaias, W. E., & Varela, R. (1989). Pigment distribution in the Caribbean Sea: Observations from space. Progress in Oceanography, 23, 23−64. Muller-Karger, F. E., Walsh, J. J., Evans, R. H., & Meyers, M. B. (1991). On the seasonal phytoplankton concentration and sea surface temperature cycles of the Gulf of Mexico as determined by satellites. Journal of Geophysical Research, 96, 12645−12665. Najjar, R. G. (2007). Impact of circulation on export production, dissolved organic matter, and dissolved oxygen in the ocean: Results from Phase II of the Ocean Carbon-cycle Model Intercomparison Project (OCMIP-2). Global Biogeochemical Cycles, 21, GB3007. doi:10.1029/2006GB002857 Nelson, N. B., & Siegel, D. A. (2002). Chromophoric DOM in the open ocean. In D. A. Hansell, & C. A. Carlson (Eds.), Biogeochemistry of marine dissolved organic matter (pp. 547−578). San Diego, California, USA: Academic. Nelson, N. B., Siegel, D. A., Carlson, C. A., Swan, C., Smethie, W. M., & Khatiwala, S. (2007). Hydrography of chromophoric dissolved organic matter in the North Atlantic. Deep-Sea Research I, 54, 710−731. Nelson, N. B., Siegel, D. A., & Michaels, A. F. (1998). Seasonal dynamics of colored dissolved material in the Sargasso Sea. Deep-Sea Research I, 45, 931−957. O'Reilly, J. E., Moritorena, S., Mitchell, B. G., Seigel, D. S., Carder, K. L., Garver, S. A., Kahru, M., & McClain, C. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research, 103, 24937−24953. Opsahl, S., Benner, R., & Amon, R. W. (1999). Major flux of terrigenous dissolved organic matter through the Arctic Ocean. Limnology and Oceanography, 44, 2017−2023. Pastor, J., Solin, J., Bridgham, S. D., Updegraff, K., Harth, C., Weishampel, P., & Dewey, B. (2003). Global warming and the export of dissolved organic carbon from boreal peatlands. Oikos, 100, 380−386. Peterson, B. J., Holmes, R. M., McClelland, J. W., Vorosmarty, C. J., Lammers, R. B., Shiklomanov, A. I., Shiklomanov, I. A., & Rahmstorf, S. (2002). Increasing river discharge to the Arctic Ocean. Science, 298, 2171−2173. Pope, R. M., & Fry, E. S. (1997). Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Applied Optics, 36, 8710−8723. Retamal, L., Vincent, W. F., Martineau, C., & Osbum, C. L. (2007). Comparison of the optical properties of dissolved organic matter in two river-influenced coastal regions of the Canadian Arctic. Estuarine, Coastal and Shelf Science, 72, 261−272. Roesler, C. S., Perry, M. J., & Carder, K. L. (1989). Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters. Limnology and Oceanography, 34, 1510−1523. Sasai, Y., Sasaoka, K., Sasaki, H., & Ishida, A. (2007). Seasonal and intra-seasonal variability of chlorophyll-a in the North Pacific: Model and satellite data. Journal of the Earth Simulator, 8, 3−11. Schwarz, J. N., Kowalczuk, P., Cota, G. F., Mitchell, B. G., Kahru, M., Chavez, F. P., Cunningham, A., McKee, D., Gege, P., Kishino, M., Phinney, D. A., & Raine, R. (2002). Two models for absorption by colored dissolved organic matter (CDOM). Oceanologia, 44, 209−241. Shanmugam, P., & Ahn, Y. H. (2007). New atmospheric correction technique to retrieve the ocean color from SeaWiFS imagery in complex coastal waters. Journal of Optics A: Pure and Applied Optics, 9, 511−530.

1521

Shanmugam, P., Ahn, Y. H., & Ram, P. S. (2008). SeaWiFS sensing of hazardous algal blooms and their underlying physical mechanisms in shelf-slope waters of the Northwest Pacific during summer. Remote Sensing of Environment, 112, 3248−3270. Shifrin, K. S. (1988). Physical optics of ocean water (pp. 285). College Park, MD: American Institute of Physics. Sieburth, J. M., & Jensen, A. (1969). Studies on algal substances in the sea. II. The formation of gelbstoff (humic material) by exudates of phaeophyta. Journal of Experimental Marine Biology and Ecology, 3, 275−289. Siegel, D. A., Maritorena, S., & Nelson, N. B. (2002). Global distribution and dynamics of colored dissolved and detrital organic materials. Journal of Geophysical Research, 107(C12), 3228. doi:10.1029/2001JC000965 Siegel, D. A., & Michaels, A. F. (1996). Quantification of non-algal light attenuation in the Sargasso Sea: Implications for biogeochemistry and remote sensing. Deep-Sea Research II, 43, 321−345. Smith, R. C., & Baker, K. S. (1981). Optical properties of the clearest natural waters (200– 800 nm). Applied Optics, 20, 177−184. Stedmon, C. A., & Markager, S. (2001). The optics of chromophoric dissolved organic matter (CDOM) in the Greenland Sea: An algorithm for differentiation between marine and terrestrially derived organic matter. Limnology and Oceanography, 46, 2087−2093. Stedmon, C. A., Markager, S., & Kaas, H. (2000). Optical properties and signatures of chromophoric dissolved organic matter (CDOM) in Danish coastal waters. Estuarine, Coastal and Shelf Science, 51, 267−278. Tassan, S., & Ferrari, G. M. (2003). Variability of light absorption by aquatic particles in the near-infrared spectral region. Applied Optics, 42, 4802−4810. Twardowski, M. S., Boss, E., Sullivan, J. M., & Donaghay, P. L. (2004). Modeling spectral absorption by chromophoric dissolved organic matter (CDOM). Marine Chemistry, 89, 69−88. Twardowski, M. S., & Donaghay, P. L. (2001). Separating in situ and terrigenous sources of absorption by dissolved material in coastal waters. Journal of Geophysical Research, 106(C2), 2545−2560. van de Hulst, H. C. (1981). Light scattering by small particles.Mineola, N. Y.: Dover 470 pp. Vinayachandran, P. N., Chauhan, P., Mohan, M., & Nayak, S. (2004). Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters, 31, L01302. doi:10.1029/2003GL018533 Vodacek, A., Blough, N. V., DeGrandpre, M. D., Peltzer, E. T., & Nelson, R. K. (1997). Seasonal variation of CDOM and DOC in the Middle Atlantic Bight: Terrestrial inputs and photooxidation. Limnology and Oceanography, 42, 674−686. Walker, N. D., Huh, O. K., Rouse, L. J., & Murray, S. P. (1996). Evolution and structure of a coastal squirt off the Mississippi River delta: Northern Gulf of Mexico. Journal of Geophysical Research, 101, 20643−20655. Walsh, J. J., Jolliff, J. K., Darrow, B. P., Lenes, J. M., Milroy, S. P., Remsen, A., Dieterle, D. A., Carder, K. L., et al. (2006). Red tides in the Gulf of Mexico: Where, when, and why? Journal of Geophysical Research, 111(C11), C11003. Wheeler, P. A., Watkins, J. M., & Hansing, P. L. (1997). Nutrients, organic carbon and organic nitrogen in the upper water column of the Arctic Ocean: Implications for the sources of dissolved organic carbon. Deep-Sea Research, 44, 1571−1592. Worrall, F., Burt, T., & Shedden, R. (2003). Long term records of riverine dissolved organic matter. Biogeochemisty, 64, 165−178. Yamada, K., Ishizaka, J., Yoo, S., Kim, H., & Chiba, S. (2004). Seasonal and interannual variability of sea surface chlorophyll a concentration in the Japan/East Sea (JES). Progress in Oceanography, 61, 193−211. Yoder, J. A., & Kennelly, M. A. (2003). Seasonal and ENSO variability in global ocean phytoplankton chlorophyll derived from four years of SeaWiFS measurements. Global Biogeochemical Cycles, 17, 1112. doi:10.1029/2002GB001942 Zhao, J., Cao, W., Wang, G., Yang, D., Yang, Y., Sun, Z., Zhou, W., & Liang, S. (2009). The variations in optical properties of CDOM throughout an algal bloom event. Estuarine, Coastal and Shelf Science, 82, 225−232.