Atmospheric correction in coastal region using same-day observations of different sun-sensor geometries with a revised POLYMER model.

In this paper, with a revised POLYMER (POLYnomial based approach applied to MERIS data) atmospheric correction model, we present a novel scheme (two-angle atmospheric correction algorithm, termed as TAACA) to remove atmospheric contributions in satellite ocean color measurements for coastal environments, especially when there are absorbing aerosols. TAACA essentially uses the same water properties as a constraint to determine oceanic and atmospheric properties simultaneously using two same-day consecutive satellite images having different sun-sensor geometries. The performance of TAACA is first evaluated with a synthetic dataset, where the retrieved remote-sensing reflectance (Rrs) by TAACA matches very well (the coefficient of determination (R2) ≥ 0.98) with the simulated Rrs for each wavelength, and the unbiased root mean square error (uRMSE) is ∼12.2% for cases of both non-absorbing and strongly absorbing aerosols. When this dataset is handled by POLYMER, for non-absorbing aerosol cases, the R2 and uRMSE values are ∼0.99 and ∼7.5%, respectively, but they are ∼0.92 and ∼39.5% for strongly absorbing aerosols. TAACA is further assessed using co-located VIIRS measurements for waters in Boston Harbor and Massachusetts Bay, and the retrieved Rrs from VIIRS agrees with in situ measurements within ∼27.3% at the visible wavelengths. By contrast, a traditional algorithm resulted in uRMSE as 3890.4% and 58.9% at 410 and 443 nm, respectively, for these measurements. The Rrs products derived from POLYMER also show large deviations from in situ measurements. It is envisioned that more reliable Rrs products in coastal waters could be obtained from satellite ocean color measurements with a scheme like TAACA, especially when there are strongly absorbing aerosols.

Vicarious calibration of COCTS-HY1C at visible and near-infrared bands for ocean color application.

Remote sensing reflectance obtained from space-borne ocean color sensors is of great importance to carbon cycle and ocean-atmospheric interactions by providing biogeochemical parameters on the global scale using specific algorithms. Vicarious calibration is necessary for obtaining accurate remote sensing reflectance that meets the application demands of atmospheric correction algorithms. For ocean color sensors, vicarious calibration must be done prior to atmospheric correction. The third Chinese Ocean Color and Temperature Scanner (COCTS) aboard the HY1C satellite was launched on September 7, 2018, and it will provide essential ocean color data that will complement those of existing missions. We used field measurements from the Marine Optical Buoy (MOBY) and aerosol information provided by the MODerate Imaging Spectroradiometer (MODIS) aboard the Terra satellite to calculate vicarious calibration coefficients, and we further evaluated the applicability of the established vicarious calibration approach by cross-calibration using MODIS data on the global scale. Finally, the established vicarious calibration coefficients were used to retrieve the aerosol optical depth and remote sensing reflectance, which were compared to Aerosol Robotic Network-Ocean Color (AERONET-OC) data and MODIS-Terra and Ocean and Land Color Instrument (OLCI)-Sentinel-3A operational products. The results show that the vicarious calibration coefficients are relatively stable and reliable for all bands ranging from visible to near-infrared and can be used to obtain accurate high-quality data.

Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing.

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (Rrs) and Secchi disk depth (ZSD) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01-6.8 m-1) from Rrs(λ) for the 350-560 nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly "exact" analytical inversion of an Rrs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350-560 nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of ZSD places an extra constraint in the inversion of Rrs, no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of ZSD and Rrs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

Secchi disk observation with spectral-selective glasses in blue and green waters.

Radiative transfer modeling of Secchi disk observations has historically been based on conjugated signals of eye response and radiance, where water's attenuation in the entire visible band is included in the attenuation when deciding the Secchi disk depth in water. Aas et al. [Ocean Sci.10(2), 177 (2014)Remote Sens. Environ.169, 139 (2015)] hypothesized that it is actually the attenuation in water's transparent window that matters to the observation of a Secchi disk in water. To test this hypothesis, observations of Secchi disks in blue and green waters were conducted via naked eyes, blue-pass glasses, and green-pass glasses. Measurement results indicate that for blue waters, the observed Secchi depths via naked eyes match the depths obtained with blue-pass glasses and much deeper than the depths with green-pass glasses, although the green-pass glasses match the highest response of human eyes. These observations experimentally support the hypothesis that our eye-brain system uses the contrast information in the transparent window to make a judgement decision regarding sighting a Secchi disk in water.

Spectral sea surface reflectance of skylight.

In examining the dependence of the sea surface reflectance of skylight ρs on sky conditions, wind speed, solar zenith angle, and viewing geometry, Mobley [Appl. Opt.38, 7442 (1999).10.1364/AO.38.007442] assumed ρs is independent of wavelength. Lee et al. [Opt. Express18, 26313 (2010).10.1364/OE.18.026313] showed experimentally that ρs does vary spectrally due to the spectral difference of sky radiance coming from different directions, which was ignored in Mobley's study. We simulated ρs from 350 nm to 1000 nm by explicitly accounting for spectral variations of skylight distribution and Fresnel reflectance. Furthermore, we separated sun glint from sky glint because of significant differences in magnitude, spectrum and polarization state between direct sun light and skylight light. The results confirm that spectral variation of ρs(λ) mainly arises from the spectral distribution of skylight and would vary from slightly blueish due to normal dispersion of the refractive index of water, to neutral and then to reddish with increasing wind speeds and decreasing solar zenith angles. Polarization moderately increases sky glint by 8 - 20% at 400 nm but only by 0 - 10% at 1000 nm. Sun glint is inherently reddish and becomes significant (>10% of sky glint) when the sun is at the zenith with moderate winds or when the sea is roughened (wind speeds > 10 m s-1) with solar zenith angles < 20°. We recommend a two-step procedure by first correcting the glint due to direct sun light, which is unpolarized, followed by removing the glint due to diffused and polarized skylight. The simulated ρs(λ) as a function of wind speeds, sun angles and aerosol concentrations for currently recommended sensor-sun geometry, i.e., zenith angle = 40° and azimuthal angle relative to the sun = 45°, is available upon request.

An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance.

Remote-sensing reflectance (R(rs)), which is defined as the ratio of water-leaving radiance (L(w)) to downwelling irradiance just above the surface (E(d)(0⁺)), varies with both water constituents (including bottom properties of optically-shallow waters) and angular geometry. L(w) is commonly measured in the field or by satellite sensors at convenient angles, while E(d)(0⁺) can be measured in the field or estimated based on atmospheric properties. To isolate the variations of R(rs) (or L(w)) resulting from a change of water constituents, the angular effects of R(rs) (or L(w)) need to be removed. This is also a necessity for the calibration and validation of satellite ocean color measurements. To reach this objective, for optically-deep waters where bottom contribution is negligible, we present a system centered on water's inherent optical properties (IOPs). It can be used to derive IOPs from angular Rrs and offers an alternative to the system centered on the concentration of chlorophyll. This system is applicable to oceanic and coastal waters as well as to multiband and hyperspectral sensors. This IOP-centered system is applied to both numerically simulated data and in situ measurements to test and evaluate its performance. The good results obtained suggest that the system can be applied to angular R(rs) to retrieve IOPs and to remove the angular variation of R(rs).

[The influence of nonuniform vertical profiles of chlorophyll concentration on apparent optical properties].

Previous researches on ocean optics and ocean color were based on the assumption that inherent optical properties and optically significant constituents of seawater are homogeneous in the vertical direction. However, oceanographic observations show that the assumption is not always exact and the vertical inhomogeneity of them exists in the upper ocean. The purpose of the present research is to study the effect of nonuniform vertical profiles of chlorophyll concentration on apparent optical properties with radiative transfer model Hydrolight. The vertical profiles of chlorophyll concentration were approximated according to a Gaussian function (Lewis et al, 1983). The apparent optical properties of seawater with nonuniform chlorophyll concentration profiles were simulated with Hydrolight radiative transfer model and case-1 bio-optical model, and then compared with those for homogenous ocean whose chlorophyll concentration was identical to the background chlorophyll concentration of inhomogenous cases. The results reveal that the subsurface maximal chlorophyll concentration increases the remote sensing reflectance at the blue wavelength and decreases it at the green wavelength, nonuniform vertical profiles of chlorophyll concentration change the diffuse attenuation coefficient profiles and the angular structure of the light field in the seawater, and the diffuse attenuation coefficients maximum and average cosines minimum appear at the depth of the maximal chlorophyll concentration.

Effects of molecular and particle scatterings on the model parameter for remote-sensing reflectance.

For optically deep waters, remote-sensing reflectance (r(rs)) is traditionally expressed as the ratio of the backscattering coefficient (b(b)) to the sum of absorption and backscattering coefficients (a + b(b)) that multiples a model parameter (g, or the so-called f'/Q). Parameter g is further expressed as a function of b(b)/(a + b(b)) (or b(b)/a) to account for its variation that is due to multiple scattering. With such an approach, the same g value will be derived for different a and b(b) values that provide the same ratio. Because g is partially a measure of the angular distribution of upwelling light, and the angular distribution from molecular scattering is quite different from that of particle scattering; g values are expected to vary with different scattering distributions even if the b(b)/a ratios are the same. In this study, after numerically demonstrating the effects of molecular and particle scatterings on the values of g, an innovative r(rs) model is developed. This new model expresses r(rs) in two separate terms: one governed by the phase function of molecular scattering and one governed by the phase function of particle scattering, with a model parameter introduced for each term. In this way the phase function effects from molecular and particle scatterings are explicitly separated and accounted for. This new model provides an analytical tool to understand and quantify the phase-function effects on r(rs), and a platform to calculate r(rs) spectrum quickly and accurately that is required for remote-sensing applications.