Abstract

Absorption and scattering by molecules, aerosols and hydrosols, and the reflection and transmission over the sea surface can modify the original polarization state of sunlight. However, water-leaving radiance polarization, containing embedded water constituent information, has largely been neglected. Here, the efficiency of the parallel polarization radiance (PPR) for enhancing ocean color signal of suspended particulate matter is examined via vector radiative transfer simulations and laboratory experiments. The simulation results demonstrate that the PPR has a slightly higher ocean color signal at the top-of-atmosphere as compared with that of the total radiance. Moreover, both the simulations and laboratory measurements reveal that, compared with total radiance, PPR can effectively enhance the normalized ocean color signal for a large range of observation geometries, wavelengths, and suspended particle concentrations. Thus, PPR has great potential for improving the ocean color signal detection from satellite.

© 2017 Optical Society of America

1. Introduction

Based on spectral radiances measured by ocean color satellite sensors at the top of the atmosphere (TOA), water-leaving radiance can be retrieved after atmospheric correction, which can be further applied to retrieve oceanic constituents (e.g., phytoplankton, minerals, and colored dissolved organic matter). Currently, a variety of ocean color satellite sensors provide global data for scientific research, thereby enormously benefiting oceanography studies, such as primary productivity prediction [1–3], ocean carbon fluxes estimation [4–9], and climate change research [6,10–12]. However, most operational ocean color satellite sensors cannot measure the polarization properties of the radiation, which has been eliminated by the ocean color research community as noise [13]. Thus, utilization of the polarization properties of oceanic constituents inversion has largely been ignored [14]. In fact, scattering by atmospheric molecules, aerosols, water molecules, and particles and reflection and refraction at the sea surface can modify the polarization state of radiation [15]. As a result, solar radiation with no polarization becomes partially polarized after transmitting in the atmosphere and ocean, and the degree of linear polarization of the upward radiation at the TOA can reach 70% [16]. The pattern and degree of the polarization closely correlate with inherent optical properties (IOPs) as well as the concentrations and size distributions of water constituents [17,18]. Therefore, polarization information can be used in the retrieval of particulate concentrations [19,20], chlorophyll-a fluorescence signals [21,22], and attenuation/absorption ratios [23]. Recent studies using radiative transfer (RT) simulations and field measurements have provided convincing evidence that the application of polarized water-leaving radiance is considerably superior in the separation of organic and inorganic suspended particles [24], the retrieval of suspended particulate matter and IOPs in coastal waters [25], and the recognition of underwater targets [26,27]. To date, however, no ocean color satellite sensor, except for the POLDER (POLarization and Directionality of the Earth’s Reflectances) sensor instrument on the ADEOS-I (November 1996 to June 1997), ADEOS-II (April 2003 to October 2003), and PARASOL (December 2004 to December 2013) satellites, possesses the multi-directional and polarized measurement capability to determine the polarization of reflected radiation and thus acquire the physical, chemical, or radiation characteristics of global aerosols and clouds [28–30]. Moreover, Loisel et al. [31] have shown that marine polarized remote-sensing reflectance, as detected from the POLDER sensor, could be measured from space over bright waters and in the absence of aerosols. In addition, based on RT simulations and POLDER data, He et al. [32] have proposed, in place of the traditional total radiation intensity, a novel ocean color remote-sensing concept utilizing parallel polarization radiance (PPR), which could effectively diminish sun glint contamination and enhance the ocean color signal at the TOA.

Despite the importance of polarization information, relatively few in situ or laboratory observations of the polarization state of oceanic light have been carried out, because of the lack of suitable instruments and practical difficulties in obtaining reliable field data [19]. In this study, we built a multiple-angle apparatus to measure in the laboratory the polarization spectroscopy of the upward radiation above water. Moreover, we use a vector RT model (PCOART) [31,33] to simulate the polarized radiation at the TOA. Based on the results of the RT simulations and laboratory measurements, we examine the effectivity of PPR on the enhancement of the ocean color signal for estimating suspended particulate matter.

2. Theoretical background

2.1 The concept of parallel polarization radiance (PPR)

To describe the full polarization state of the radiation in a given direction, we adopt the Stokes vector convention as follows:

S=[IQUV]=[ElEl*+ErEr*ElEl*ErEr*ElEr*+ErEl*i(ElEr*ErEl*)]
where I is the total radiance (i.e., intensity measured by ocean color sensors), Q is the linearly polarized component in the meridian plane or perpendicular to the meridian plane, U is the linearly polarized component in the direction 45° or 135° to the meridian plane, and V is the circular polarized component. For the light on the ocean surface, V is negligible. El and Er are components of the electric vector in the meridian plane (determined by the viewing direction of the sensor and local zenith) and in the plane perpendicular to the meridian plane, respectively. El* and Er* are the respective conjugate values of El and Er. We neglected the common multipliers in Eq. (1). For a non-polarization light source such as solar radiation, ElEl*=ErEr*, and the total intensity I = 2ElEl* = 2ErEr*. To compare with I, we define PPR and vertical polarization radiance (VPR) as [32]:

{PPR=2ElEl*=I+QVPR=2ErEr*=IQ

Clearly, for non-polarization radiation, PPR = I. Thus, the PPR has a clear physical meaning representing the intensity of the parallel polarization component. In this study, we use PPR instead of VPR because the water surface reflecting coefficient of the VPR is generally larger than that of the PPR [32]. In other words, using the PPR can diminish the water surface reflecting effect, and thus it can improve the retrieval of water-leaving radiance. Moreover, unlike the Stokes vector and its polarization components (Q, U), PPR has a similar format to traditional radiation intensity and can be easily understood and exploited by the ocean color research community.

2.2 Definition of the normalized ocean color signal

The ocean color signal for total radiance at the TOA can be described as:

Ioc=ItIb
where It is the total upward radiance at the TOA with atmosphere and seawater, including the pure seawater and ocean color components (e.g., suspended particulate matter), and Ib is the background upward radiance at the TOA with atmosphere and pure seawater. Therefore, Ioc is the ocean color signal contributed by the ocean color components.

We normalize Eq. (3) according to Ib and obtain the normalized ocean color signal for the I component of the Stokes vector as:

IOC(N)=Ioc/Ib=It/Ib1
Similarly, the normalized ocean color signal for the PPR can be expressed as:
IPPR(N)=IPPR/IPPRb=IPPRt/IPPRb1
where IPPRb is the background PPR at the TOA with atmosphere and pure seawater, and IPPR is the PPR at the TOA with atmosphere, pure seawater, and ocean color components. In this study, we take into consideration suspended particulate matter (TSM) only, to examine the influence of PPR on the normalized ocean color signals.

3. Data and methods

3.1 Radiative transfer simulations

We apply the PCOART model [33], which uses the matrix-operator method to solve the vector RT in the coupled ocean–atmosphere system, to simulate the upward Stokes vector at the TOA. Given the setups of the IOPs in the water shown in Fig. 1, the PCOART model outputs the angular distribution of the upward Stokes vectors at the TOA. The IOPs of the ocean–atmosphere system required for RT simulations are absorption coefficients, scattering coefficients, and scattering phase matrices. In this section we report the input parameters used in the RT simulations, with a detailed description of the PCOART model as referred to in He et al. [34].

 figure: Fig. 1

Fig. 1 Flow diagram of the bio-optical models for the radiative transfer simulation using PCOART.θ is the sensor zenith angle, φ is the relative azimuth angles, ω is single-scattering albedo of ocean, τ is optical thicknesses of the ocean.

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Similar to other RT models, the ocean–atmosphere system combines three plane-parallel homogeneous layers. The upper layer comprises atmospheric molecules with Rayleigh scattering with single-scattering albedo of 1 and depolarization factor of 0.0279 [35]. The middle layer is the atmospheric aerosol with the maritime aerosol with 90% relative humidity (M90) and differing optical thicknesses. The lower oceanic layer contains pure seawater and suspended particulate matter. The IOPs of the pure seawater, such as spectral absorption and scattering coefficients, are adopted from Smith & Baker [36], Morel & Prieur [37], respectively, and the Rayleigh scattering is used for the scattering phase matrix. For the IOPs of the suspended particulate matter, the absorption and scattering coefficients are determined by the concentrations according to the bio-optical model from Bowles et al. [37]. The scattering phase matrices of the suspended particles are calculated by using Mie theory with a complex refractive index of 1.165–0.001i and the modified Junge size distribution (0.5–50 µm) with exponent value of 4. Then, the scattering phase matrices of the suspended particles and pure seawater are mixed according to their scattering coefficients [22]. The incident solar irradiances at the TOA are taken from Neckel & Labs [38].

We simulated the upward Stokes vectors at the TOA by using PCOART with differing wavelengths, solar zenith angles, and atmospheric and oceanic IOPs setups. The wavelengths were taken from 400 nm to 700 nm in steps of 10 nm. The solar zenith angles were taken as 0°, 30°, and 60°, respectively. The optical thicknesses of the aerosol were taken as 0.05, 0.1, and 0.2, respectively. The concentrations of suspended particulate matter were taken as 0.1, 1, 10, 30, 50, 100, 200, 300, and 500 mg/L, respectively. For each setup, we carried out two PCOART simulations: one for the background with atmosphere and pure seawater, the other with atmosphere, pure seawater, and suspended particulate matter. Then, according to Eqs. (4) and (5), we obtained the normalized ocean color signals of the total radiance and PPR at the TOA.

3.2 Laboratory measurement of the normalized ocean color signals

We carried out the laboratory measurements in a dark room. We used an ASD FieldSpec Spectroradiometer (350–2500 nm, Analytical Spectral Devices, Inc., Boulder, USA) with a linearly polarized filter at the entrance pupil of the sensor, thus acting as a polarized detector, to measure multi-angle polarization spectroscopy of the upward radiation above the water surface. The device uses a rotatable semi-circular orbit to control the observation azimuth and zenith angles of the detector, as shown in Fig. 2(a). We placed the orbit on a polyethylene cylinder container, which contained the water with suspended particulate matter at various concentrations. The polyethylene cylinder was coated with highly absorbent material (dumb-light paint) to prevent the influence of inner wall reflection on spectral measurements. We used four pumps, assembled on the bottom and inner wall, to prevent suspended particle sedimentation. The samples of the suspended particles were collected from the sediment of the Qiantang River, which is the upper stream of the Hangzhou Bay. We monitored variation in suspended particle concentrations simultaneously by using a HydroScat-6 spectral backscattering sensor (HS-6, HOBI Labs, Inc., USA). The HS-6 measurements indicated that the variation in suspended particle concentrations was small. In addition, we determined the size distribution of suspended particles by using a laser in situ scattering and transmissometry instrument (LISST-100X, Sequoia Scientific Inc., Bellevue, USA), as shown in Fig. 2(b). A xenon lamp was the light source with zenith angles of 40°. We measured the spectral irradiances of the lamp with the ASD using a standard reflecting plate, because of the saturation of the ASD for some of the wavelength when it pointed directly to the lamp, as shown in Fig. 2(c). Based on the factory testing, the xenon lamp was initially completely non-polarized. Moreover, the irradiances measured with the ASD indicate that the lamp was steady throughout the laboratory measurements, as shown in Fig. 2(c). We conducted the polarizer calibration before the measurements, and obtained the spectral transmittance of the polarizer (Fig. 2d).

 figure: Fig. 2

Fig. 2 (a) Observation geometry of spectral radiometer in a meridian plane. (b) Particle size distribution of suspended particles. (c) Irradiance of the xenon lamp and solar radiation. (d) Spectral transmittance of the polarizer.

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We measured the polarization spectra at various viewing zenith and azimuth angles under different suspended particulate matter concentrations. By controlling the sensors relative to the nadir direction, the sensor measured the polarization spectra in the meridian plane at different zenith angles, determined by the directional vectors of the sensor viewing and zenith, as shown in Fig. 2(a). Thus, the sensor viewing zenith angles ranged from 0° to 60° in steps of 20°, and the relative azimuth angles were 0° to 180° in steps of 45° referring to the xenon lamp azimuth. We rotated the linear polarizer in front of the ASD detectors, and three successive measurements were taken with the linearly polarized filter principal axis turned 0°, 60°, and 120°, respectively, thus providing the degree of polarization and three Stokes parameters (I, Q, U). First, we measured the polarization spectra of pure water (distilled water without suspended particles). We then added suspended particulate material into the container and mixed to the set concentrations, and polarization spectra measurements were taken for each concentration.

4. Results and discussion

4.1 Radiative transfer simulation results

As determined by the concentrations, constituents, and size distributions of marine constituents and the observation geometries, the polarization characteristics of ocean color signal vary with water optical properties [39–41]. Figure 3 shows a comparison of the intensities at 690 nm between PPR and total radiance under the solar zenith angle of 30°. Clearly, PPR has a similar angular distribution pattern compared to total radiance, but PPR has a slightly higher value, indicating that PPR can improve detection of the ocean color signal. Figure 4 shows comparisons of the normalized ocean color signals for I and PPR at the TOA under various solar zenith angles and suspended particle concentrations. Clearly, the PPR can generally enhance the normalized ocean color signal as compared with the total radiance. For example, taking the results at the solar zenith angle of 0°, enhancement by the PPR increases with the sensor viewing zenith angles. For viewing zenith angles lower than 20°, PPR exhibits a similar ability in the detection of ocean color signals to that of total radiance. The PPR demonstrates remarkable improvement in the observational capability at the sensor viewing zenith angle of 65°, and the maximum of PPR normalized ocean color signal can exceed 0.8 at 690 nm compared with 0.4 for the total radiance. Moreover, the enhancement of the normalized ocean color signal by PPR generally increases with wavelengths and suspended particle concentrations (Fig. 5). Figure 6 shows the results under different relative azimuth angles. Similarly, the PPR can increase the normalized ocean color signal as a whole, particularly at relative azimuth angles larger than 90°. Knowing that the intensity of PPR is slightly higher than that of total radiance, higher normalized ocean color signals of the PPR indicate that PPR has a better capacity to retrieve the suspended particle concentrations, particularly in highly turbid waters.

 figure: Fig. 3

Fig. 3 Angular distributions of the ocean color signals at 690 nm for the Ioc (upper row) and IPPR (bottom row) (with unit of mWcm−2µm−1sr−1) under differing total suspended matter (TSM) concentrations. The Ioc and IPPR were simulated by PCOART at the TOA with 30° solar zenith angle. The concentric circles correspond to sensor zenith angle (0° viewing angle is for the sensor looking vertically downward).

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 figure: Fig. 4

Fig. 4 Comparisons of the normalized ocean color signals for I (solid lines) and PPR (dashed lines) under differing suspended particle concentrations. The upper and bottom rows are the results at 570 nm and 690 nm with sensor azimuth angles of 180°, respectively. The normalized ocean color signals were simulated by PCOART at the TOA with solar zenith angles of 0°, 30°, and 60°.

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 figure: Fig. 5

Fig. 5 Comparisons of normalized ocean color signals for I (solid lines) and PPR (points) at the TOA with differing wavelengths (upper row) and suspended particulate matter concentrations (bottom row) with sensor azimuth angles of 180°. The normalized ocean color signals were simulated by PCOART at the TOA with solar zenith angles of 0°, 30°, and 60°.

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 figure: Fig. 6

Fig. 6 Comparison of the normalized ocean color signals for I (solid lines) and PPR (points) at the TOA under different TSM concentrations and relative azimuth angles, as simulated by PCOART.

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4.2 Angular distribution of the enhancements by PPR

Taking as an example the results at 690 nm and aerosol optical depth of 0.1 (see Section 4.3 for different optical thicknesses), we analyzed the angular distribution of the enhancement of the normalized ocean color signal by PPR. The deviation values (D) and relative deviation values (RD) of the normalized ocean color signals between PPR and total radiance are calculated as follows:

D=IPPR(N)IOC(N)
RD=(IPPR(N)IOC(N))×100%/IOC(N)
Figure 7 shows the angular distribution of D values at solar zenith angle of 30° under differing suspended particle concentrations. Overall, for most of the observation geometries, the D values are positive, indicating enhancement of the normalized ocean color signal by PPR. Moreover, the PPR can greatly improve the normalized ocean color signals in the reflection hemisphere, and the maximum enhancement is located at the specular reflectance geometries. Moreover, the enhancement by PPR increases with suspended particle concentrations ranging from 0.1 mg/L to 500 mg/L, which is consistent with the results shown in Fig. 5.

 figure: Fig. 7

Fig. 7 Angular distributions of the D values at 690nm with differing sensor observing angles and TSM concentrations simulated by PCOART at 30° solar zenith angle. The white contour lines represent the zero value.

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Figure 8 shows the angular distribution of RD values at solar zenith angle of 30° under differing suspended particle concentrations. Overall, the RD values exhibit a similar angular distribution pattern to that of the D values. The maximum RD is located at the specular reflectance geometry (180° relative azimuth and viewing zenith angles around 30°). The maximum enhancement can be up to 200%. It is also noticeable that RD degrades in the antispecular hemisphere between the sensor relative azimuth angles of 45° and 90°, where the normalized ocean color signal of total radiance is greater than that of PPR to some extent (~18%).

 figure: Fig. 8

Fig. 8 Same as Fig. 7, but for the RD values at 690nm.

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4.3 The influence of aerosol optical thickness

Previous research [42–46] has revealed that polarimetric observations have unique advantages in detecting and retrieving cloudy and aerosol optical properties, which are extensively used in remote-sensing studies. Thus, we discuss here the influence of aerosol optical thickness on the normalized ocean color signals. Figure 9 shows comparisons of the normalized ocean color signal under differing aerosol optical thicknesses (0.05, 0.1, and 0.2) and solar zenith angles (0°, 30°, and 60°). Both the normalized ocean color signals of total radiance and PPR decrease with the increment of aerosol optical thickness. As the aerosol optical thickness increases, the influence of atmosphere gradually strengthens because of more aerosol scattering. Moreover, the ocean color signals at the TOA decrease with increasing aerosol optical thicknesses because of decreasing atmospheric transmittance. In addition, the increase in aerosol scattering can slightly reduce the downwelling irradiance at the ocean surface, which will decrease the ocean color signal. Therefore, the increase in the aerosol optical thickness will increase the background signal and decrease the ocean color signal, which will result in the decrease in the normalized ocean color signal [47–49]. Nevertheless, the PPR can generally enhance the normalized ocean color signal under differing aerosol optical thicknesses.

 figure: Fig. 9

Fig. 9 Comparisons of normalized ocean color signals for PPR (points) and I (solid lines) at the TOA with differing aerosol optical thicknesses (0.05, 0.1, and 0.2) and suspended particle concentrations (in units of mg/L) for solar zenith angles of 0°, 30°, and 60°.

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4.4 Laboratory measurements

We performed laboratory measurements of the upward polarization spectrum under suspended particle concentrations ranging from 25 mg/L to 500 mg/L. Figure 10 shows the angular distribution of the I, Q, and U measured at the 40° lamp zenith angle and 100 mg/L suspended particle concentrations. The Q and U exhibit the same magnitudes, and are approximately one order less than the I. Figure 11 shows the angular distribution of the D values (defined in Eq. (6) measured at the viewing zenith angles of 0°, 20°, 40°, and 60° and relative azimuth angles of 0°, 45°, 90°, 135°, and 180°. Note that the interruption along the azimuth angles may be caused by the numeric interpolation. The results demonstrate that PPR can enhance the normalized ocean color signal for most viewing zenith angles, except for viewing angles slightly higher than 40° for relative azimuth angles ranging from 45° to 135°. The black regions around the azimuth angles of 180° are masked because of lamp glint contamination. The maximum enhancement appears at the relative azimuth angle of 135°, whereas the minimum enhancement is at the relative azimuth angle of 90° for the viewing zenith angle of 40°. The degree of enhancement of the normalized ocean color signal by PPR, compared with that of total radiance at the antispecular hemisphere, is slightly less than that at the specular reflectance hemisphere. In addition, the D values between PPR and total radiance mainly increase with increasing suspended particle concentrations, which is consistent with the RT simulations.

 figure: Fig. 10

Fig. 10 Angular distributions of I, Q, U (in units of mW cm−2 μm−1 sr−1) measured by laboratory experiments with 40° lamp zenith angle and 100 mg/L total suspended matter (TSM) concentration. The azimuth angles from 180° to 360° were symmetrical to the azimuth angles lower than 180°.

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 figure: Fig. 11

Fig. 11 Angular distribution of the D values at 690nm with differing sensor observing angles and suspended particle concentrations based on laboratory measurements with 40° lamp zenith angle. TSM represents suspended particle concentrations. Concentric circles represent the different sensor observation zenith angles. The black section is masked because of lamp glint contamination.

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To analyze quantitatively the degree of enhancement of PPR on normalized ocean color signal compared with total radiance, we also calculate the RD, with the results shown in Fig. 12. The RD exhibits a similar angular distribution pattern to that of the D values (Fig. 11). The maximum RD can exceed 108% for suspended particle concentrations of 25 mg/L. It is evident that RD degrades as the suspended particle concentrations increase, which is consistent with the RT simulations (Fig. 8).

 figure: Fig. 12

Fig. 12 Same as Fig. 11, but for angular distribution of the RD values at 690nm.

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4.5 Comparison of RT simulations and laboratory measurements

Overall, the patterns of D values between the laboratory measurements and the RT simulations are consistent (Fig. 13), although those obtained by the laboratory measurements exhibit a higher magnitude than those of the RT simulations. For the laboratory measurements, the positive D is located at the regions with viewing zenith angles less than 30°, and the negative D is located at the relative azimuth angles from 45° to 90° for viewing angles larger than 30°, which are consistent with RT simulations. Note that the laboratory measurements at specular reflectance geometry were masked (in the black regions in the figures) because of saturation of the ASD for some wavelengths. Moreover, the relative azimuth angles were measured in the laboratory in steps of 45°, and this coarse resolution would cause an interruption in the distribution along the azimuth angles because of the interpolation effect.

 figure: Fig. 13

Fig. 13 Comparison of the normalized ocean color signals between the PPR and I for the laboratory measurements (upper row) and the RT simulations (bottom row) with the incident zenith angle of 40°. TSM represents suspended particle concentrations. Concentric circles represent different sensor viewing zenith angles from 0° to 90°.

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There are several reasons for the observed difference in the magnitudes between the laboratory measurements and the RT simulations. First, the RT simulations were at the TOA and influenced by atmospheric molecules and aerosols, whereas the laboratory measurements were at the water surface with negligible influence by the atmosphere. Scattering of the atmospheric molecules and aerosols increased the background signal mainly at the TOA and resulted in the much smaller values of the normalized ocean color signal for the RT simulations. Moreover, the xenon lamp used in the laboratory experiments was probably a point light source instead of a parallel light source, whereas the solar radiation used in the RT simulations was a parallel light source. In addition, the spectral irradiances between the lamp and solar radiations differed, as shown in Fig. 2(c). Based on our experience of the laboratory measurements in this study, in our next field measurements we will use sunlight as the source. Nevertheless, the magnitudes in both the RT simulations and the laboratory experiments demonstrate that using PPR can enhance the normalized ocean color signal, which would improve the retrieval of oceanic constituents.

5. Conclusion

In this study, we examined the effectivity of PPR on the enhancement of the ocean color signal for estimating suspended particulate matter by using RT simulations and laboratory measurements. The RT simulations demonstrate that the PPR has a similar angular distribution pattern to that of total radiance, but the PPR has slightly higher values, indicating that using PPR would not decrease the ocean color signal at the TOA or even higher intensity, which could improve detection of the ocean color signal. Moreover, the normalized ocean color signal of the PPR is larger than that of total radiance for most of the observation geometries, particularly at the specular reflectance geometries, which can reduce contamination from sun glint reflection. In addition, the enhancement of the normalized ocean color signal by PPR strengthens with suspended particle concentrations increasing.

Based on established laboratory equipment, we also measured the polarization spectroscopy at the entire upward hemisphere. In spite of the observed differences in the magnitudes of the normalized ocean color signal and the distributions in some observation geometries, the results of the laboratory measurements are generally consistent with those of the RT simulations. Overall, both the RT simulations and the laboratory measurements demonstrate that, compared with total radiance, PPR can effectively enhance the normalized ocean color signal for a large range of observation geometries, wavelengths, and suspended particle concentrations. Thus, PPR shows great potential for further ocean color research and application, although the influence of atmosphere and sun glint on polarization spectral measurements should be taken into consideration.

Funding

National Basic Research Programme (“973” Programme) of China (grant #2015CB954002); National High Technology and Development Program of China (grant 2014AA123301); National Natural Science Foundation of China (NSFC) (grants #41676170, #41676172, #41476155 and #41621064); “Global Change and Air-Sea Interaction” project of China (grant # GASI-03-03-01-01); “Light of West China” Program of CSA (grant# XAB2015A07); public fund by State Key Laboratory of Satellite Ocean Environment Dynamics (Second Institute of Oceanography, State Oceanic Administration) (grant # SOED1602).

Acknowledgments

We thank the two anonymous reviewers for providing constructive comments which strengthen the manuscript largely.

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20. M. Chami and M. D. Platel, “Sensitivity of the retrieval of the inherent optical properties of marine particles in coastal waters to the directional variations and the polarization of the reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007). [CrossRef]  

21. A. Gilerson, J. Zhou, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. Ahmed, “Retrieval of chlorophyll fluorescence from reflectance spectra through polarization discrimination: modeling and experiments,” Appl. Opt. 45(22), 5568–5581 (2006). [CrossRef]   [PubMed]  

22. A. Gilerson, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarization discrimination fluorescence retrieval from reflectance spectra of algae in seawater: comparison of multicomponent Mie scattering and polarized radiative transfer models with laboratory and field tests,” Proc. SPIE 5977, 597708 (2005). [CrossRef]  

23. A. Ibrahim, A. Gilerson, T. Harmel, A. Tonizzo, J. Chowdhary, and S. Ahmed, “The relationship between upwelling underwater polarization and attenuation/absorption ratio,” Opt. Express 20(23), 25662–25680 (2012). [CrossRef]   [PubMed]  

24. M. Chami, “Importance of the polarization in the retrieval of oceanic constituents from the remote sensing reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007). [CrossRef]  

25. A. Gilerson, A. Ibrahim, R. Foster, C. Carrizo, A. Elhabashi, and S. Ahmed, “Retrieval of water optical properties using polarization of light underwater: case I and II waters,” Proc. SPIE 9240, 1235–1242 (2014).

26. M. Dubreuil, P. Delrot, I. Leonard, A. Alfalou, C. Brosseau, and A. Dogariu, “Exploring underwater target detection by imaging polarimetry and correlation techniques,” Appl. Opt. 52(5), 997–1005 (2013). [CrossRef]   [PubMed]  

27. A. Gilerson, C. Carrizo, A. Tonizzo, A. Ibrahim, A. El-Habashi, R. Foster, and S. Ahmed, “Polarimetric imaging of underwater targets,” Proc. SPIE 8724, 872403 (2013). [CrossRef]  

28. J. Chowdhary, B. Cairns, M. Mishchenko, and L. D. Travis, “Retrieval of aerosol properties over the ocean using multispectral and multiangle photopolarimetric measurements from the research scanning polarimeter,” Geophys. Res. Lett. 28(2), 243–246 (2001). [CrossRef]  

29. J. Chowdhary, B. Cairns, and L. D. Travis, “Case Studies of Aerosol Retrievals over the Ocean from Multiangle, Multispectral Photopolarimetric Remote Sensing Data,” J. Atmos. Sci. 59(3), 383–397 (2002). [CrossRef]  

30. F. M. Bréon and P. Goloub, “Cloud droplet effective radius from spaceborne polarization measurements,” Geophys. Res. Lett. 25(11), 1879–1882 (1998). [CrossRef]  

31. H. Loisel, L. Duforet, D. Dessailly, M. Chami, and P. Dubuisson, “Investigation of the variations in the water leaving polarized reflectance from the POLDER satellite data over two biogeochemical contrasted oceanic areas,” Opt. Express 16(17), 12905–12918 (2008). [CrossRef]   [PubMed]  

32. X. He, D. Pan, Y. Bai, D. Wang, and Z. Hao, “A new simple concept for ocean colour remote sensing using parallel polarisation radiance,” Sci. Rep. 4(6168), 3748 (2014). [PubMed]  

33. X. Q. He, D. L. Pan, Y. Bai, Q. K. Zhu, and F. Gong, “Vector radiative transfer numerical model of coupled ocean-atmosphere system using matrix-operator method,” Sci. China Earth Sci. 50(3), 442–452 (2007). [CrossRef]  

34. X. Q. He, Y. Bai, Q. K. Zhu, and F. Gong, “A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface,” J. Quant. Spectrosc. Radiat. Transf. 111(10), 1426–1448 (2010). [CrossRef]  

35. A. T. Young, “Revised depolarization corrections for atmospheric extinction,” Appl. Opt. 19(20), 3427–3428 (1980). [CrossRef]   [PubMed]  

36. R. C. Smith and K. S. Baker, “Optical properties of the clearest natural waters (200-800 nm),” Appl. Opt. 20(2), 177–184 (1981). [CrossRef]   [PubMed]  

37. A. Morel and L. Prieur, “Analysis of variation in coean color,” Limnol. Oceanogr. 22(4), 709–722 (1977). [CrossRef]  

38. D. G. Bowers, S. Boudjelas, and G. E. L. Harker, “The distribution of fine suspended sediments in the surface waters of the Irish Sea and its relation to tidal stirring,” Intern. J. Rem. Sens. 19(14), 2789–2805 (1998). [CrossRef]  

39. H. Neckel and D. Labs, “The solar radiation between 3300 and 12500 Å,” Sol. Phys. 90(2), 205–258 (1984). [CrossRef]  

40. A. Tonizzo, A. Gilerson, T. Harmel, A. Ibrahim, J. Chowdhary, B. Gross, F. Moshary, and S. Ahmed, “Estimating particle composition and size distribution from polarized water-leaving radiance,” Appl. Opt. 50(25), 5047–5058 (2011). [CrossRef]  

41. J. K. Lotsberg and J. J. Stamnes, “Impact of particulate oceanic composition on the radiance and polarization of underwater and backscattered light,” Opt. Express 18(10), 10432–10445 (2010). [CrossRef]   [PubMed]  

42. A. Tonizzo, J. Zhou, A. Gilerson, M. S. Twardowski, D. J. Gray, R. A. Arnone, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarized light in coastal waters: hyperspectral and multiangular analysis,” Opt. Express 17(7), 5666–5683 (2009). [CrossRef]   [PubMed]  

43. D. Antoine and A. Morel, “Relative importance of multiple scattering by air molecules and aerosols in forming the atmospheric path radiance in the visible and near-infrared parts of the spectrum,” Appl. Opt. 37(12), 2245–2259 (1998). [CrossRef]   [PubMed]  

44. O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002). [CrossRef]  

45. O. P. Hasekamp and J. Landgraf, “Retrieval of aerosol properties over the ocean from multispectral single-viewing-angle measurements of intensity and polarization: retrieval approach, information content, and sensitivity study,” J. Geophys. Res. 110(110), 1–16 (2005).

46. A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010). [CrossRef]  

47. T. Harmel and M. Chami, “Influence of polarimetric satellite data measured in the visible region on aerosol detection and on the performance of atmospheric correction procedure over open ocean waters,” Opt. Express 19(21), 20960–20983 (2011). [CrossRef]   [PubMed]  

48. X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754–20770 (2012). [CrossRef]   [PubMed]  

49. X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353–4364 (2011). [CrossRef]   [PubMed]  

References

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  4. C. G. Fichot and W. L. Miller, “An approach to quantify depth-resolved marine photochemical fluxes using remote sensing: application to carbon monoxide (CO) photoproduction,” Remote Sens. Environ. 114(7), 1363–1377 (2010).
    [Crossref]
  5. Y. Bai, D. L. Pan, W. J. Cai, X. Q. He, D. F. Wang, B. Y. Tao, and Q. K. Zhu, “Remote sensing of salinity from satellite-derived CDOM in the Changjiang River dominated East China Sea,” J. Geophys. Res. 118(1), 227–243 (2013).
    [Crossref]
  6. Y. Bai, X. Q. He, D. L. Pan, C. T. A. Chen, Y. Kang, X. Y. Chen, and W. J. Cai, “Summertime Changjiang River plume variation during 1998–2010,” J. Geophys. Res. 119(9), 6238–6257 (2014).
    [Crossref]
  7. Y. Bai, T. H. Huang, X. Q. He, S. L. Wang, Y. C. Hsin, C. R. Wu, W. Zhai, H.-K. Lui, and C.-T. A. Chen, “Intrusion of the Pearl River plume into the main channel of the Taiwan Strait in summer,” J. Sea Res. 95, 1–15 (2015).
    [Crossref]
  8. Y. Bai, W. J. Cai, X. Q. He, W. D. Zhai, D. L. Pan, M. H. Dai, and P. S. Yu, “A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: a case study from the East China Sea,” J. Geophys. Res. 120(3), 2331–2349 (2015).
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  9. X. Q. He, D. Xu, Y. Bai, D. L. Pan, C. T. A. Chen, X. Y. Chen, and F. Gong, “Eddy-entrained Pearl River plume into the oligotrophic basin of the South China Sea,” Cont. Shelf Res. 124, 117–124 (2016).
    [Crossref]
  10. X. Q. He, Y. Bai, D. L. Pan, C. T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of seasonal and inter-annual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011),” Biogeosciences 10(7), 4721–4739 (2013).
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  11. W. W. Gregg, M. E. Conkright, P. Ginoux, J. E. O’Reilly, and N. W. Casey, “Ocean primary production and climate: global decadal changes,” Geophys. Res. Lett. 30(15), 157–168 (2003).
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  13. X. Q. He, D. L. Pan, Y. Bai, Z. H. Mao, T. Y. Wang, and Z. Z. Hao, “A practical method for on-orbit estimation of polarization response of satellite ocean color sensor,” IEEE Trans. Geosci. Rem. Sens.. 54(4), 1967–1976 (2016).
    [Crossref]
  14. T. Harmel, A. Gilerson, A. Tonizzo, J. Chowdhary, A. Weidemann, R. Arnone, and S. Ahmed, “Polarization impacts on the water-leaving radiance retrieval from above-water radiometric measurements,” Appl. Opt. 51(35), 8324–8340 (2012).
    [Crossref] [PubMed]
  15. R. Foster and A. Gilerson, “Polarized transfer functions of the ocean surface for above-surface determination of the vector submarine light field,” Appl. Opt. 55(33), 9476–9494 (2016).
    [Crossref] [PubMed]
  16. G. Meister, E. J. Kwiatkowska, B. A. Franz, F. S. Patt, G. C. Feldman, and C. R. McClain, “Moderate-resolution imaging spectroradiometer ocean color polarization correction,” Appl. Opt. 44(26), 5524–5535 (2005).
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  18. M. Chami, R. Santer, and E. Dilligeard, “Radiative transfer model for the computation of radiance and polarization in an ocean-atmosphere system: polarization properties of suspended matter for remote sensing,” Appl. Opt. 40(15), 2398–2416 (2001).
    [Crossref] [PubMed]
  19. M. Chami and D. McKee, “Determination of biogeochemical properties of marine particles using above water measurements of the degree of polarization at the Brewster angle,” Opt. Express 15(15), 9494–9509 (2007).
    [Crossref] [PubMed]
  20. M. Chami and M. D. Platel, “Sensitivity of the retrieval of the inherent optical properties of marine particles in coastal waters to the directional variations and the polarization of the reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007).
    [Crossref]
  21. A. Gilerson, J. Zhou, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. Ahmed, “Retrieval of chlorophyll fluorescence from reflectance spectra through polarization discrimination: modeling and experiments,” Appl. Opt. 45(22), 5568–5581 (2006).
    [Crossref] [PubMed]
  22. A. Gilerson, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarization discrimination fluorescence retrieval from reflectance spectra of algae in seawater: comparison of multicomponent Mie scattering and polarized radiative transfer models with laboratory and field tests,” Proc. SPIE 5977, 597708 (2005).
    [Crossref]
  23. A. Ibrahim, A. Gilerson, T. Harmel, A. Tonizzo, J. Chowdhary, and S. Ahmed, “The relationship between upwelling underwater polarization and attenuation/absorption ratio,” Opt. Express 20(23), 25662–25680 (2012).
    [Crossref] [PubMed]
  24. M. Chami, “Importance of the polarization in the retrieval of oceanic constituents from the remote sensing reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007).
    [Crossref]
  25. A. Gilerson, A. Ibrahim, R. Foster, C. Carrizo, A. Elhabashi, and S. Ahmed, “Retrieval of water optical properties using polarization of light underwater: case I and II waters,” Proc. SPIE 9240, 1235–1242 (2014).
  26. M. Dubreuil, P. Delrot, I. Leonard, A. Alfalou, C. Brosseau, and A. Dogariu, “Exploring underwater target detection by imaging polarimetry and correlation techniques,” Appl. Opt. 52(5), 997–1005 (2013).
    [Crossref] [PubMed]
  27. A. Gilerson, C. Carrizo, A. Tonizzo, A. Ibrahim, A. El-Habashi, R. Foster, and S. Ahmed, “Polarimetric imaging of underwater targets,” Proc. SPIE 8724, 872403 (2013).
    [Crossref]
  28. J. Chowdhary, B. Cairns, M. Mishchenko, and L. D. Travis, “Retrieval of aerosol properties over the ocean using multispectral and multiangle photopolarimetric measurements from the research scanning polarimeter,” Geophys. Res. Lett. 28(2), 243–246 (2001).
    [Crossref]
  29. J. Chowdhary, B. Cairns, and L. D. Travis, “Case Studies of Aerosol Retrievals over the Ocean from Multiangle, Multispectral Photopolarimetric Remote Sensing Data,” J. Atmos. Sci. 59(3), 383–397 (2002).
    [Crossref]
  30. F. M. Bréon and P. Goloub, “Cloud droplet effective radius from spaceborne polarization measurements,” Geophys. Res. Lett. 25(11), 1879–1882 (1998).
    [Crossref]
  31. H. Loisel, L. Duforet, D. Dessailly, M. Chami, and P. Dubuisson, “Investigation of the variations in the water leaving polarized reflectance from the POLDER satellite data over two biogeochemical contrasted oceanic areas,” Opt. Express 16(17), 12905–12918 (2008).
    [Crossref] [PubMed]
  32. X. He, D. Pan, Y. Bai, D. Wang, and Z. Hao, “A new simple concept for ocean colour remote sensing using parallel polarisation radiance,” Sci. Rep. 4(6168), 3748 (2014).
    [PubMed]
  33. X. Q. He, D. L. Pan, Y. Bai, Q. K. Zhu, and F. Gong, “Vector radiative transfer numerical model of coupled ocean-atmosphere system using matrix-operator method,” Sci. China Earth Sci. 50(3), 442–452 (2007).
    [Crossref]
  34. X. Q. He, Y. Bai, Q. K. Zhu, and F. Gong, “A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface,” J. Quant. Spectrosc. Radiat. Transf. 111(10), 1426–1448 (2010).
    [Crossref]
  35. A. T. Young, “Revised depolarization corrections for atmospheric extinction,” Appl. Opt. 19(20), 3427–3428 (1980).
    [Crossref] [PubMed]
  36. R. C. Smith and K. S. Baker, “Optical properties of the clearest natural waters (200-800 nm),” Appl. Opt. 20(2), 177–184 (1981).
    [Crossref] [PubMed]
  37. A. Morel and L. Prieur, “Analysis of variation in coean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
    [Crossref]
  38. D. G. Bowers, S. Boudjelas, and G. E. L. Harker, “The distribution of fine suspended sediments in the surface waters of the Irish Sea and its relation to tidal stirring,” Intern. J. Rem. Sens. 19(14), 2789–2805 (1998).
    [Crossref]
  39. H. Neckel and D. Labs, “The solar radiation between 3300 and 12500 Å,” Sol. Phys. 90(2), 205–258 (1984).
    [Crossref]
  40. A. Tonizzo, A. Gilerson, T. Harmel, A. Ibrahim, J. Chowdhary, B. Gross, F. Moshary, and S. Ahmed, “Estimating particle composition and size distribution from polarized water-leaving radiance,” Appl. Opt. 50(25), 5047–5058 (2011).
    [Crossref]
  41. J. K. Lotsberg and J. J. Stamnes, “Impact of particulate oceanic composition on the radiance and polarization of underwater and backscattered light,” Opt. Express 18(10), 10432–10445 (2010).
    [Crossref] [PubMed]
  42. A. Tonizzo, J. Zhou, A. Gilerson, M. S. Twardowski, D. J. Gray, R. A. Arnone, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarized light in coastal waters: hyperspectral and multiangular analysis,” Opt. Express 17(7), 5666–5683 (2009).
    [Crossref] [PubMed]
  43. D. Antoine and A. Morel, “Relative importance of multiple scattering by air molecules and aerosols in forming the atmospheric path radiance in the visible and near-infrared parts of the spectrum,” Appl. Opt. 37(12), 2245–2259 (1998).
    [Crossref] [PubMed]
  44. O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002).
    [Crossref]
  45. O. P. Hasekamp and J. Landgraf, “Retrieval of aerosol properties over the ocean from multispectral single-viewing-angle measurements of intensity and polarization: retrieval approach, information content, and sensitivity study,” J. Geophys. Res. 110(110), 1–16 (2005).
  46. A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010).
    [Crossref]
  47. T. Harmel and M. Chami, “Influence of polarimetric satellite data measured in the visible region on aerosol detection and on the performance of atmospheric correction procedure over open ocean waters,” Opt. Express 19(21), 20960–20983 (2011).
    [Crossref] [PubMed]
  48. X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754–20770 (2012).
    [Crossref] [PubMed]
  49. X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353–4364 (2011).
    [Crossref] [PubMed]

2016 (3)

X. Q. He, D. Xu, Y. Bai, D. L. Pan, C. T. A. Chen, X. Y. Chen, and F. Gong, “Eddy-entrained Pearl River plume into the oligotrophic basin of the South China Sea,” Cont. Shelf Res. 124, 117–124 (2016).
[Crossref]

X. Q. He, D. L. Pan, Y. Bai, Z. H. Mao, T. Y. Wang, and Z. Z. Hao, “A practical method for on-orbit estimation of polarization response of satellite ocean color sensor,” IEEE Trans. Geosci. Rem. Sens.. 54(4), 1967–1976 (2016).
[Crossref]

R. Foster and A. Gilerson, “Polarized transfer functions of the ocean surface for above-surface determination of the vector submarine light field,” Appl. Opt. 55(33), 9476–9494 (2016).
[Crossref] [PubMed]

2015 (3)

M. Babin, S. Bélanger, I. Ellingsen, A. Forest, V. Le Fouest, T. Lacour, M. Ardyna, and D. Slagstad, “Estimation of primary production in the Arctic Ocean using ocean colour remote sensing and coupled physical-biological models: strengths, limitations and how they compare,” Prog. Oceanogr. 139, 197–220 (2015).
[Crossref]

Y. Bai, T. H. Huang, X. Q. He, S. L. Wang, Y. C. Hsin, C. R. Wu, W. Zhai, H.-K. Lui, and C.-T. A. Chen, “Intrusion of the Pearl River plume into the main channel of the Taiwan Strait in summer,” J. Sea Res. 95, 1–15 (2015).
[Crossref]

Y. Bai, W. J. Cai, X. Q. He, W. D. Zhai, D. L. Pan, M. H. Dai, and P. S. Yu, “A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: a case study from the East China Sea,” J. Geophys. Res. 120(3), 2331–2349 (2015).
[Crossref]

2014 (3)

Y. Bai, X. Q. He, D. L. Pan, C. T. A. Chen, Y. Kang, X. Y. Chen, and W. J. Cai, “Summertime Changjiang River plume variation during 1998–2010,” J. Geophys. Res. 119(9), 6238–6257 (2014).
[Crossref]

A. Gilerson, A. Ibrahim, R. Foster, C. Carrizo, A. Elhabashi, and S. Ahmed, “Retrieval of water optical properties using polarization of light underwater: case I and II waters,” Proc. SPIE 9240, 1235–1242 (2014).

X. He, D. Pan, Y. Bai, D. Wang, and Z. Hao, “A new simple concept for ocean colour remote sensing using parallel polarisation radiance,” Sci. Rep. 4(6168), 3748 (2014).
[PubMed]

2013 (4)

M. Dubreuil, P. Delrot, I. Leonard, A. Alfalou, C. Brosseau, and A. Dogariu, “Exploring underwater target detection by imaging polarimetry and correlation techniques,” Appl. Opt. 52(5), 997–1005 (2013).
[Crossref] [PubMed]

A. Gilerson, C. Carrizo, A. Tonizzo, A. Ibrahim, A. El-Habashi, R. Foster, and S. Ahmed, “Polarimetric imaging of underwater targets,” Proc. SPIE 8724, 872403 (2013).
[Crossref]

Y. Bai, D. L. Pan, W. J. Cai, X. Q. He, D. F. Wang, B. Y. Tao, and Q. K. Zhu, “Remote sensing of salinity from satellite-derived CDOM in the Changjiang River dominated East China Sea,” J. Geophys. Res. 118(1), 227–243 (2013).
[Crossref]

X. Q. He, Y. Bai, D. L. Pan, C. T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of seasonal and inter-annual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

2012 (3)

2011 (3)

2010 (4)

J. K. Lotsberg and J. J. Stamnes, “Impact of particulate oceanic composition on the radiance and polarization of underwater and backscattered light,” Opt. Express 18(10), 10432–10445 (2010).
[Crossref] [PubMed]

A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010).
[Crossref]

X. Q. He, Y. Bai, Q. K. Zhu, and F. Gong, “A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface,” J. Quant. Spectrosc. Radiat. Transf. 111(10), 1426–1448 (2010).
[Crossref]

C. G. Fichot and W. L. Miller, “An approach to quantify depth-resolved marine photochemical fluxes using remote sensing: application to carbon monoxide (CO) photoproduction,” Remote Sens. Environ. 114(7), 1363–1377 (2010).
[Crossref]

2009 (1)

2008 (1)

2007 (4)

X. Q. He, D. L. Pan, Y. Bai, Q. K. Zhu, and F. Gong, “Vector radiative transfer numerical model of coupled ocean-atmosphere system using matrix-operator method,” Sci. China Earth Sci. 50(3), 442–452 (2007).
[Crossref]

M. Chami, “Importance of the polarization in the retrieval of oceanic constituents from the remote sensing reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007).
[Crossref]

M. Chami and D. McKee, “Determination of biogeochemical properties of marine particles using above water measurements of the degree of polarization at the Brewster angle,” Opt. Express 15(15), 9494–9509 (2007).
[Crossref] [PubMed]

M. Chami and M. D. Platel, “Sensitivity of the retrieval of the inherent optical properties of marine particles in coastal waters to the directional variations and the polarization of the reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007).
[Crossref]

2006 (2)

A. Gilerson, J. Zhou, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. Ahmed, “Retrieval of chlorophyll fluorescence from reflectance spectra through polarization discrimination: modeling and experiments,” Appl. Opt. 45(22), 5568–5581 (2006).
[Crossref] [PubMed]

M. J. Behrenfeld, K. Worthington, R. M. Sherrell, F. P. Chavez, P. Strutton, M. McPhaden, and D. M. Shea, “Controls on tropical Pacific Ocean productivity revealed through nutrient stress diagnostics,” Nature 442(7106), 1025–1028 (2006).
[Crossref] [PubMed]

2005 (3)

G. Meister, E. J. Kwiatkowska, B. A. Franz, F. S. Patt, G. C. Feldman, and C. R. McClain, “Moderate-resolution imaging spectroradiometer ocean color polarization correction,” Appl. Opt. 44(26), 5524–5535 (2005).
[Crossref] [PubMed]

A. Gilerson, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarization discrimination fluorescence retrieval from reflectance spectra of algae in seawater: comparison of multicomponent Mie scattering and polarized radiative transfer models with laboratory and field tests,” Proc. SPIE 5977, 597708 (2005).
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O. P. Hasekamp and J. Landgraf, “Retrieval of aerosol properties over the ocean from multispectral single-viewing-angle measurements of intensity and polarization: retrieval approach, information content, and sensitivity study,” J. Geophys. Res. 110(110), 1–16 (2005).

2003 (1)

W. W. Gregg, M. E. Conkright, P. Ginoux, J. E. O’Reilly, and N. W. Casey, “Ocean primary production and climate: global decadal changes,” Geophys. Res. Lett. 30(15), 157–168 (2003).
[Crossref]

2002 (2)

J. Chowdhary, B. Cairns, and L. D. Travis, “Case Studies of Aerosol Retrievals over the Ocean from Multiangle, Multispectral Photopolarimetric Remote Sensing Data,” J. Atmos. Sci. 59(3), 383–397 (2002).
[Crossref]

O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002).
[Crossref]

2001 (2)

J. Chowdhary, B. Cairns, M. Mishchenko, and L. D. Travis, “Retrieval of aerosol properties over the ocean using multispectral and multiangle photopolarimetric measurements from the research scanning polarimeter,” Geophys. Res. Lett. 28(2), 243–246 (2001).
[Crossref]

M. Chami, R. Santer, and E. Dilligeard, “Radiative transfer model for the computation of radiance and polarization in an ocean-atmosphere system: polarization properties of suspended matter for remote sensing,” Appl. Opt. 40(15), 2398–2416 (2001).
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1998 (3)

F. M. Bréon and P. Goloub, “Cloud droplet effective radius from spaceborne polarization measurements,” Geophys. Res. Lett. 25(11), 1879–1882 (1998).
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D. Antoine and A. Morel, “Relative importance of multiple scattering by air molecules and aerosols in forming the atmospheric path radiance in the visible and near-infrared parts of the spectrum,” Appl. Opt. 37(12), 2245–2259 (1998).
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D. G. Bowers, S. Boudjelas, and G. E. L. Harker, “The distribution of fine suspended sediments in the surface waters of the Irish Sea and its relation to tidal stirring,” Intern. J. Rem. Sens. 19(14), 2789–2805 (1998).
[Crossref]

1992 (1)

W. Balch, R. Evans, J. Brown, G. Feldman, C. Mcclain, and W. Esaias, “The remote sensing of ocean primary productivity - use of a new data compilation to test satellite algorithms,” J. Geophys. Res. 97(C2), 2279–2293 (1992).
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1988 (1)

T. Platt and S. Sathyendranath, “Oceanic primary production: estimation by remote sensing at local and regional scales,” Science 241(4873), 1613–1620 (1988).
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1984 (1)

H. Neckel and D. Labs, “The solar radiation between 3300 and 12500 Å,” Sol. Phys. 90(2), 205–258 (1984).
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1981 (1)

1980 (1)

1977 (1)

A. Morel and L. Prieur, “Analysis of variation in coean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
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1973 (1)

G. W. Kattawar, G. N. Plass, and J. A. Guinn., “Monte Carlo calculations of the polarization of radiation in the earth’s atmosphere-ocean system,” J. Phys. Oceanogr. 3(4), 353–372 (1973).
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Ahmed, S.

Ahmed, S. A.

A. Tonizzo, J. Zhou, A. Gilerson, M. S. Twardowski, D. J. Gray, R. A. Arnone, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarized light in coastal waters: hyperspectral and multiangular analysis,” Opt. Express 17(7), 5666–5683 (2009).
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A. Gilerson, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarization discrimination fluorescence retrieval from reflectance spectra of algae in seawater: comparison of multicomponent Mie scattering and polarized radiative transfer models with laboratory and field tests,” Proc. SPIE 5977, 597708 (2005).
[Crossref]

Alfalou, A.

Antoine, D.

Ardyna, M.

M. Babin, S. Bélanger, I. Ellingsen, A. Forest, V. Le Fouest, T. Lacour, M. Ardyna, and D. Slagstad, “Estimation of primary production in the Arctic Ocean using ocean colour remote sensing and coupled physical-biological models: strengths, limitations and how they compare,” Prog. Oceanogr. 139, 197–220 (2015).
[Crossref]

Arnone, R.

Arnone, R. A.

Babin, M.

M. Babin, S. Bélanger, I. Ellingsen, A. Forest, V. Le Fouest, T. Lacour, M. Ardyna, and D. Slagstad, “Estimation of primary production in the Arctic Ocean using ocean colour remote sensing and coupled physical-biological models: strengths, limitations and how they compare,” Prog. Oceanogr. 139, 197–220 (2015).
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Bai, Y.

X. Q. He, D. Xu, Y. Bai, D. L. Pan, C. T. A. Chen, X. Y. Chen, and F. Gong, “Eddy-entrained Pearl River plume into the oligotrophic basin of the South China Sea,” Cont. Shelf Res. 124, 117–124 (2016).
[Crossref]

X. Q. He, D. L. Pan, Y. Bai, Z. H. Mao, T. Y. Wang, and Z. Z. Hao, “A practical method for on-orbit estimation of polarization response of satellite ocean color sensor,” IEEE Trans. Geosci. Rem. Sens.. 54(4), 1967–1976 (2016).
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Y. Bai, W. J. Cai, X. Q. He, W. D. Zhai, D. L. Pan, M. H. Dai, and P. S. Yu, “A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: a case study from the East China Sea,” J. Geophys. Res. 120(3), 2331–2349 (2015).
[Crossref]

Y. Bai, T. H. Huang, X. Q. He, S. L. Wang, Y. C. Hsin, C. R. Wu, W. Zhai, H.-K. Lui, and C.-T. A. Chen, “Intrusion of the Pearl River plume into the main channel of the Taiwan Strait in summer,” J. Sea Res. 95, 1–15 (2015).
[Crossref]

Y. Bai, X. Q. He, D. L. Pan, C. T. A. Chen, Y. Kang, X. Y. Chen, and W. J. Cai, “Summertime Changjiang River plume variation during 1998–2010,” J. Geophys. Res. 119(9), 6238–6257 (2014).
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X. He, D. Pan, Y. Bai, D. Wang, and Z. Hao, “A new simple concept for ocean colour remote sensing using parallel polarisation radiance,” Sci. Rep. 4(6168), 3748 (2014).
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Y. Bai, D. L. Pan, W. J. Cai, X. Q. He, D. F. Wang, B. Y. Tao, and Q. K. Zhu, “Remote sensing of salinity from satellite-derived CDOM in the Changjiang River dominated East China Sea,” J. Geophys. Res. 118(1), 227–243 (2013).
[Crossref]

X. Q. He, Y. Bai, D. L. Pan, C. T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of seasonal and inter-annual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011),” Biogeosciences 10(7), 4721–4739 (2013).
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X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754–20770 (2012).
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X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353–4364 (2011).
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X. Q. He, Y. Bai, Q. K. Zhu, and F. Gong, “A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface,” J. Quant. Spectrosc. Radiat. Transf. 111(10), 1426–1448 (2010).
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X. Q. He, D. L. Pan, Y. Bai, Q. K. Zhu, and F. Gong, “Vector radiative transfer numerical model of coupled ocean-atmosphere system using matrix-operator method,” Sci. China Earth Sci. 50(3), 442–452 (2007).
[Crossref]

Baker, K. S.

Balch, W.

W. Balch, R. Evans, J. Brown, G. Feldman, C. Mcclain, and W. Esaias, “The remote sensing of ocean primary productivity - use of a new data compilation to test satellite algorithms,” J. Geophys. Res. 97(C2), 2279–2293 (1992).
[Crossref]

Behrenfeld, M. J.

M. J. Behrenfeld, K. Worthington, R. M. Sherrell, F. P. Chavez, P. Strutton, M. McPhaden, and D. M. Shea, “Controls on tropical Pacific Ocean productivity revealed through nutrient stress diagnostics,” Nature 442(7106), 1025–1028 (2006).
[Crossref] [PubMed]

Bélanger, S.

M. Babin, S. Bélanger, I. Ellingsen, A. Forest, V. Le Fouest, T. Lacour, M. Ardyna, and D. Slagstad, “Estimation of primary production in the Arctic Ocean using ocean colour remote sensing and coupled physical-biological models: strengths, limitations and how they compare,” Prog. Oceanogr. 139, 197–220 (2015).
[Crossref]

Boudjelas, S.

D. G. Bowers, S. Boudjelas, and G. E. L. Harker, “The distribution of fine suspended sediments in the surface waters of the Irish Sea and its relation to tidal stirring,” Intern. J. Rem. Sens. 19(14), 2789–2805 (1998).
[Crossref]

Bowers, D. G.

D. G. Bowers, S. Boudjelas, and G. E. L. Harker, “The distribution of fine suspended sediments in the surface waters of the Irish Sea and its relation to tidal stirring,” Intern. J. Rem. Sens. 19(14), 2789–2805 (1998).
[Crossref]

Bréon, F. M.

F. M. Bréon and P. Goloub, “Cloud droplet effective radius from spaceborne polarization measurements,” Geophys. Res. Lett. 25(11), 1879–1882 (1998).
[Crossref]

Brosseau, C.

Brown, J.

W. Balch, R. Evans, J. Brown, G. Feldman, C. Mcclain, and W. Esaias, “The remote sensing of ocean primary productivity - use of a new data compilation to test satellite algorithms,” J. Geophys. Res. 97(C2), 2279–2293 (1992).
[Crossref]

Cai, W. J.

Y. Bai, W. J. Cai, X. Q. He, W. D. Zhai, D. L. Pan, M. H. Dai, and P. S. Yu, “A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: a case study from the East China Sea,” J. Geophys. Res. 120(3), 2331–2349 (2015).
[Crossref]

Y. Bai, X. Q. He, D. L. Pan, C. T. A. Chen, Y. Kang, X. Y. Chen, and W. J. Cai, “Summertime Changjiang River plume variation during 1998–2010,” J. Geophys. Res. 119(9), 6238–6257 (2014).
[Crossref]

Y. Bai, D. L. Pan, W. J. Cai, X. Q. He, D. F. Wang, B. Y. Tao, and Q. K. Zhu, “Remote sensing of salinity from satellite-derived CDOM in the Changjiang River dominated East China Sea,” J. Geophys. Res. 118(1), 227–243 (2013).
[Crossref]

Cairns, B.

J. Chowdhary, B. Cairns, and L. D. Travis, “Case Studies of Aerosol Retrievals over the Ocean from Multiangle, Multispectral Photopolarimetric Remote Sensing Data,” J. Atmos. Sci. 59(3), 383–397 (2002).
[Crossref]

J. Chowdhary, B. Cairns, M. Mishchenko, and L. D. Travis, “Retrieval of aerosol properties over the ocean using multispectral and multiangle photopolarimetric measurements from the research scanning polarimeter,” Geophys. Res. Lett. 28(2), 243–246 (2001).
[Crossref]

Carrizo, C.

A. Gilerson, A. Ibrahim, R. Foster, C. Carrizo, A. Elhabashi, and S. Ahmed, “Retrieval of water optical properties using polarization of light underwater: case I and II waters,” Proc. SPIE 9240, 1235–1242 (2014).

A. Gilerson, C. Carrizo, A. Tonizzo, A. Ibrahim, A. El-Habashi, R. Foster, and S. Ahmed, “Polarimetric imaging of underwater targets,” Proc. SPIE 8724, 872403 (2013).
[Crossref]

Casey, N. W.

W. W. Gregg, M. E. Conkright, P. Ginoux, J. E. O’Reilly, and N. W. Casey, “Ocean primary production and climate: global decadal changes,” Geophys. Res. Lett. 30(15), 157–168 (2003).
[Crossref]

Chami, M.

T. Harmel and M. Chami, “Influence of polarimetric satellite data measured in the visible region on aerosol detection and on the performance of atmospheric correction procedure over open ocean waters,” Opt. Express 19(21), 20960–20983 (2011).
[Crossref] [PubMed]

H. Loisel, L. Duforet, D. Dessailly, M. Chami, and P. Dubuisson, “Investigation of the variations in the water leaving polarized reflectance from the POLDER satellite data over two biogeochemical contrasted oceanic areas,” Opt. Express 16(17), 12905–12918 (2008).
[Crossref] [PubMed]

M. Chami, “Importance of the polarization in the retrieval of oceanic constituents from the remote sensing reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007).
[Crossref]

M. Chami and D. McKee, “Determination of biogeochemical properties of marine particles using above water measurements of the degree of polarization at the Brewster angle,” Opt. Express 15(15), 9494–9509 (2007).
[Crossref] [PubMed]

M. Chami and M. D. Platel, “Sensitivity of the retrieval of the inherent optical properties of marine particles in coastal waters to the directional variations and the polarization of the reflectance,” J. Geophys. Res. 112(C5), 395–412 (2007).
[Crossref]

M. Chami, R. Santer, and E. Dilligeard, “Radiative transfer model for the computation of radiance and polarization in an ocean-atmosphere system: polarization properties of suspended matter for remote sensing,” Appl. Opt. 40(15), 2398–2416 (2001).
[Crossref] [PubMed]

Chavez, F. P.

M. J. Behrenfeld, K. Worthington, R. M. Sherrell, F. P. Chavez, P. Strutton, M. McPhaden, and D. M. Shea, “Controls on tropical Pacific Ocean productivity revealed through nutrient stress diagnostics,” Nature 442(7106), 1025–1028 (2006).
[Crossref] [PubMed]

Chen, C. T. A.

X. Q. He, D. Xu, Y. Bai, D. L. Pan, C. T. A. Chen, X. Y. Chen, and F. Gong, “Eddy-entrained Pearl River plume into the oligotrophic basin of the South China Sea,” Cont. Shelf Res. 124, 117–124 (2016).
[Crossref]

Y. Bai, X. Q. He, D. L. Pan, C. T. A. Chen, Y. Kang, X. Y. Chen, and W. J. Cai, “Summertime Changjiang River plume variation during 1998–2010,” J. Geophys. Res. 119(9), 6238–6257 (2014).
[Crossref]

X. Q. He, Y. Bai, D. L. Pan, C. T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of seasonal and inter-annual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

Chen, C.-T. A.

Y. Bai, T. H. Huang, X. Q. He, S. L. Wang, Y. C. Hsin, C. R. Wu, W. Zhai, H.-K. Lui, and C.-T. A. Chen, “Intrusion of the Pearl River plume into the main channel of the Taiwan Strait in summer,” J. Sea Res. 95, 1–15 (2015).
[Crossref]

Chen, X. Y.

X. Q. He, D. Xu, Y. Bai, D. L. Pan, C. T. A. Chen, X. Y. Chen, and F. Gong, “Eddy-entrained Pearl River plume into the oligotrophic basin of the South China Sea,” Cont. Shelf Res. 124, 117–124 (2016).
[Crossref]

Y. Bai, X. Q. He, D. L. Pan, C. T. A. Chen, Y. Kang, X. Y. Chen, and W. J. Cai, “Summertime Changjiang River plume variation during 1998–2010,” J. Geophys. Res. 119(9), 6238–6257 (2014).
[Crossref]

Cheng, Q.

X. Q. He, Y. Bai, D. L. Pan, C. T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of seasonal and inter-annual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

Chowdhary, J.

T. Harmel, A. Gilerson, A. Tonizzo, J. Chowdhary, A. Weidemann, R. Arnone, and S. Ahmed, “Polarization impacts on the water-leaving radiance retrieval from above-water radiometric measurements,” Appl. Opt. 51(35), 8324–8340 (2012).
[Crossref] [PubMed]

A. Ibrahim, A. Gilerson, T. Harmel, A. Tonizzo, J. Chowdhary, and S. Ahmed, “The relationship between upwelling underwater polarization and attenuation/absorption ratio,” Opt. Express 20(23), 25662–25680 (2012).
[Crossref] [PubMed]

A. Tonizzo, A. Gilerson, T. Harmel, A. Ibrahim, J. Chowdhary, B. Gross, F. Moshary, and S. Ahmed, “Estimating particle composition and size distribution from polarized water-leaving radiance,” Appl. Opt. 50(25), 5047–5058 (2011).
[Crossref]

A. Gilerson, J. Zhou, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. Ahmed, “Retrieval of chlorophyll fluorescence from reflectance spectra through polarization discrimination: modeling and experiments,” Appl. Opt. 45(22), 5568–5581 (2006).
[Crossref] [PubMed]

A. Gilerson, M. Oo, J. Chowdhary, B. M. Gross, F. Moshary, and S. A. Ahmed, “Polarization discrimination fluorescence retrieval from reflectance spectra of algae in seawater: comparison of multicomponent Mie scattering and polarized radiative transfer models with laboratory and field tests,” Proc. SPIE 5977, 597708 (2005).
[Crossref]

J. Chowdhary, B. Cairns, and L. D. Travis, “Case Studies of Aerosol Retrievals over the Ocean from Multiangle, Multispectral Photopolarimetric Remote Sensing Data,” J. Atmos. Sci. 59(3), 383–397 (2002).
[Crossref]

J. Chowdhary, B. Cairns, M. Mishchenko, and L. D. Travis, “Retrieval of aerosol properties over the ocean using multispectral and multiangle photopolarimetric measurements from the research scanning polarimeter,” Geophys. Res. Lett. 28(2), 243–246 (2001).
[Crossref]

Conkright, M. E.

W. W. Gregg, M. E. Conkright, P. Ginoux, J. E. O’Reilly, and N. W. Casey, “Ocean primary production and climate: global decadal changes,” Geophys. Res. Lett. 30(15), 157–168 (2003).
[Crossref]

Dai, M. H.

Y. Bai, W. J. Cai, X. Q. He, W. D. Zhai, D. L. Pan, M. H. Dai, and P. S. Yu, “A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: a case study from the East China Sea,” J. Geophys. Res. 120(3), 2331–2349 (2015).
[Crossref]

Delrot, P.

Dessailly, D.

Deuzé, J. L.

A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010).
[Crossref]

Dilligeard, E.

Diner, D. J.

A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010).
[Crossref]

Dogariu, A.

Dubovik, O.

A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010).
[Crossref]

O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002).
[Crossref]

Dubreuil, M.

Dubuisson, P.

Ducos, F.

A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010).
[Crossref]

Duforet, L.

Eck, T. F.

O. Dubovik, B. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanré, and I. Slutsker, “Variability of absorption and optical properties of key aerosol types observed in worldwide locations,” J. Atmos. Sci. 59(3), 590–608 (2002).
[Crossref]

Elhabashi, A.

A. Gilerson, A. Ibrahim, R. Foster, C. Carrizo, A. Elhabashi, and S. Ahmed, “Retrieval of water optical properties using polarization of light underwater: case I and II waters,” Proc. SPIE 9240, 1235–1242 (2014).

El-Habashi, A.

A. Gilerson, C. Carrizo, A. Tonizzo, A. Ibrahim, A. El-Habashi, R. Foster, and S. Ahmed, “Polarimetric imaging of underwater targets,” Proc. SPIE 8724, 872403 (2013).
[Crossref]

Ellingsen, I.

M. Babin, S. Bélanger, I. Ellingsen, A. Forest, V. Le Fouest, T. Lacour, M. Ardyna, and D. Slagstad, “Estimation of primary production in the Arctic Ocean using ocean colour remote sensing and coupled physical-biological models: strengths, limitations and how they compare,” Prog. Oceanogr. 139, 197–220 (2015).
[Crossref]

Emde, C.

A. A. Kokhanovsky, J. L. Deuzé, D. J. Diner, O. Dubovik, F. Ducos, C. Emde, M. J. Garay, R. G. Grainger, A. Heckel, M. Herman, I. L. Katsev, J. Keller, R. Levy, P. R. J. North, A. S. Prikhach, V. V. Rozanov, A. M. Sayer, Y. Ota, D. Tanré, G. E. Thomas, and E. P. Zege, “The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light,” Atmos. Meas. Tech. 3(4), 909–932 (2010).
[Crossref]

Esaias, W.

W. Balch, R. Evans, J. Brown, G. Feldman, C. Mcclain, and W. Esaias, “The remote sensing of ocean primary productivity - use of a new data compilation to test satellite algorithms,” J. Geophys. Res. 97(C2), 2279–2293 (1992).
[Crossref]

Evans, R.

W. Balch, R. Evans, J. Brown, G. Feldman, C. Mcclain, and W. Esaias, “The remote sensing of ocean primary productivity - use of a new data compilation to test satellite algorithms,” J. Geophys. Res. 97(C2), 2279–2293 (1992).
[Crossref]

Feldman, G.

W. Balch, R. Evans, J. Brown, G. Feldman, C. Mcclain, and W. Esaias, “The remote sensing of ocean primary productivity - use of a new data compilation to test satellite algorithms,” J. Geophys. Res. 97(C2), 2279–2293 (1992).
[Crossref]

Feldman, G. C.

Fichot, C. G.

C. G. Fichot and W. L. Miller, “An approach to quantify depth-resolved marine photochemical fluxes using remote sensing: application to carbon monoxide (CO) photoproduction,” Remote Sens. Environ. 114(7), 1363–1377 (2010).
[Crossref]

Forest, A.

M. Babin, S. Bélanger, I. Ellingsen, A. Forest, V. Le Fouest, T. Lacour, M. Ardyna, and D. Slagstad, “Estimation of primary production in the Arctic Ocean using ocean colour remote sensing and coupled physical-biological models: strengths, limitations and how they compare,” Prog. Oceanogr. 139, 197–220 (2015).
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Foster, R.

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A. Gilerson, C. Carrizo, A. Tonizzo, A. Ibrahim, A. El-Habashi, R. Foster, and S. Ahmed, “Polarimetric imaging of underwater targets,” Proc. SPIE 8724, 872403 (2013).
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Figures (13)

Fig. 1
Fig. 1 Flow diagram of the bio-optical models for the radiative transfer simulation using PCOART. θ is the sensor zenith angle, φ is the relative azimuth angles, ω is single-scattering albedo of ocean, τ is optical thicknesses of the ocean.
Fig. 2
Fig. 2 (a) Observation geometry of spectral radiometer in a meridian plane. (b) Particle size distribution of suspended particles. (c) Irradiance of the xenon lamp and solar radiation. (d) Spectral transmittance of the polarizer.
Fig. 3
Fig. 3 Angular distributions of the ocean color signals at 690 nm for the Ioc (upper row) and IPPR (bottom row) (with unit of mWcm−2µm−1sr−1) under differing total suspended matter (TSM) concentrations. The Ioc and IPPR were simulated by PCOART at the TOA with 30° solar zenith angle. The concentric circles correspond to sensor zenith angle (0° viewing angle is for the sensor looking vertically downward).
Fig. 4
Fig. 4 Comparisons of the normalized ocean color signals for I (solid lines) and PPR (dashed lines) under differing suspended particle concentrations. The upper and bottom rows are the results at 570 nm and 690 nm with sensor azimuth angles of 180°, respectively. The normalized ocean color signals were simulated by PCOART at the TOA with solar zenith angles of 0°, 30°, and 60°.
Fig. 5
Fig. 5 Comparisons of normalized ocean color signals for I (solid lines) and PPR (points) at the TOA with differing wavelengths (upper row) and suspended particulate matter concentrations (bottom row) with sensor azimuth angles of 180°. The normalized ocean color signals were simulated by PCOART at the TOA with solar zenith angles of 0°, 30°, and 60°.
Fig. 6
Fig. 6 Comparison of the normalized ocean color signals for I (solid lines) and PPR (points) at the TOA under different TSM concentrations and relative azimuth angles, as simulated by PCOART.
Fig. 7
Fig. 7 Angular distributions of the D values at 690nm with differing sensor observing angles and TSM concentrations simulated by PCOART at 30° solar zenith angle. The white contour lines represent the zero value.
Fig. 8
Fig. 8 Same as Fig. 7, but for the RD values at 690nm.
Fig. 9
Fig. 9 Comparisons of normalized ocean color signals for PPR (points) and I (solid lines) at the TOA with differing aerosol optical thicknesses (0.05, 0.1, and 0.2) and suspended particle concentrations (in units of mg/L) for solar zenith angles of 0°, 30°, and 60°.
Fig. 10
Fig. 10 Angular distributions of I, Q, U (in units of mW cm−2 μm−1 sr−1) measured by laboratory experiments with 40° lamp zenith angle and 100 mg/L total suspended matter (TSM) concentration. The azimuth angles from 180° to 360° were symmetrical to the azimuth angles lower than 180°.
Fig. 11
Fig. 11 Angular distribution of the D values at 690nm with differing sensor observing angles and suspended particle concentrations based on laboratory measurements with 40° lamp zenith angle. TSM represents suspended particle concentrations. Concentric circles represent the different sensor observation zenith angles. The black section is masked because of lamp glint contamination.
Fig. 12
Fig. 12 Same as Fig. 11, but for angular distribution of the RD values at 690nm.
Fig. 13
Fig. 13 Comparison of the normalized ocean color signals between the PPR and I for the laboratory measurements (upper row) and the RT simulations (bottom row) with the incident zenith angle of 40°. TSM represents suspended particle concentrations. Concentric circles represent different sensor viewing zenith angles from 0° to 90°.

Equations (7)

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S = [ I Q U V ] = [ E l E l * + E r E r * E l E l * E r E r * E l E r * + E r E l * i ( E l E r * E r E l * ) ]
{ PPR = 2 E l E l * = I + Q VPR = 2 E r E r * = I Q
I o c = I t I b
I O C ( N ) = I oc / I b = I t / I b 1
I P P R ( N ) = I PPR / I PPRb = I PPRt / I PPRb 1
D = I P P R ( N ) I O C ( N )
RD = ( I P P R ( N ) I O C ( N ) ) × 100 % / I O C ( N )

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