Detailed validation of the bidirectional effect in various Case I and Case II waters

Effects of water salinity on the multi-angular polarimetric properties of light reflected from smooth water surfaces

Salinity is an important environmental factor regulating the aquatic system structure of lakes and other water bodies. Changes in salinity, which can be caused by human activities, can adversely impact the life of water organisms. The refractive index, which can be directly related to water salinity, also controls the polarimetric properties of light reflected from the water surface. In this study, polarimetric measurements of smooth water surfaces with different salinity content were performed at different viewing zenith angles in the wavelength range of 450-1000 nm in the specular reflection directions. The results show that the light reflected from the water surface (defined as reflectance factor) in one measurement direction can be replaced by the reflectance factor derived from polarimetric measurements, and if the polarizer absorptance is considered, the average relative difference is less than 3%. The degree of linear polarization (DOLP) was used to retrieve the refractive indices of water with different salinities based on the Fresnel reflection coefficient. The inverted refractive indices not only have high accuracy (uncertainty from 0.9% to 1.8%) but also have a very strong relationship with the water salinity content. Our study shows the possibility of estimating the variation in water salinity using multi-angular polarimetric measurements.

Ancillary Data Uncertainties within the SeaDAS Uncertainty Budget for Ocean Colour Retrievals

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties.

Ocean Color Analytical Model Explicitly Dependent on the Volume Scattering Function

An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape factors. Besides remote sensing geometry considerations (solar zenith angle, viewing angle, and relative azimuth), the inputs are Inherent Optical Properties (IOPs) absorption a and backscattering bb coefficients, the shape of the particulate volume scattering function (VSF) in the backward direction, and the particulate backscattering ratio. Model performance (absolute error) is equivalent to full RT simulations for available high quality validation data sets, indicating almost all residual errors are inherent to the data sets themselves, i.e., from the measurements of IOPs and radiometry used as model input and in match up assessments, respectively. Best performance was observed when a constant backward phase function shape based on the findings of Sullivan and Twardowski (2009) was assumed in the model. Critically, using a constant phase function in the backward direction eliminates a key unknown, providing a path toward inversion to solve for a and bb. Performance degraded when using other phase function shapes. With available data sets, the model shows stronger performance than current state-of-the-art look-up table (LUT) based BRDF models used to normalize reflectance data, formulated on simpler first order RT approximations between rrs and bb/a or bb/(a + bb) (Morel et al., 2002; Lee et al., 2011). Stronger performance of ZTT relative to LUT-based models is attributed to using a more representative phase function shape, as well as the additional degrees of freedom achieved with several physically meaningful terms in the model. Since the model is fully described with analytical expressions, errors for terms can be individually assessed, and refinements can be readily made without carrying out the gamut of full RT computations required for LUT-based models. The ZTT model is invertible to solve for a and bb from remote sensing reflectance, and inversion approaches are being pursued in ongoing work. The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable. In summary, this new analytical model shows good potential for future ocean color inversion with low bias, well-constrained uncertainties (including the VSF), and explicit terms that can be readily tuned. Emphasis is put on application to the future NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission.

Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters

The upwelling spectral radiance distribution is polarized, and this polarization varies with the optical properties of the water body. Knowledge of the polarized, upwelling, bidirectional radiance distribution function (BRDF) is important for generating consistent, long-term data records for ocean color because the satellite sensors from which the data are derived are sensitive to polarization. In addition, various studies have indicated that measurement of the polarization of the radiance leaving the ocean can used to determine particle characteristics (Tonizzo et al., 2007; Ibrahim et al., 2016; Chami et al., 2001). Models for the unpolarized BRDF (Morel et al., 2002; Lee et al., 2011) have been validated (Voss et al., 2007; Gleason et al., 2012), but variations in the polarization of the upwelling radiance due to the sun angle, viewing geometry, dissolved material, and suspended particles have not been systematically documented. In this work, we simulated the upwelling radiance distribution using a Monte Carlo-based radiative transfer code and measured it using a set of fish-eye cameras with linear polarizing filters. The results of model-data comparisons from three field experiments in clear and turbid coastal conditions showed that the degree of linear polarization (DOLP) of the upwelling light field could be determined by the model with an absolute error of ±0.05 (or 5% when the DOLP was expressed in %). This agreement was achieved even with a fixed scattering Mueller matrix, but did require in situ measurements of the other inherent optical properties, e.g., scattering coefficient, absorption coefficient, etc. This underscores the difficulty that is likely to be encountered using the particle scattering Mueller matrix (as indicated through the remote measurement of the polarized radiance) to provide a signature relating to the properties of marine particles beyond the attenuation/absorption coefficient.

Correction for the non-nadir viewing geometry of AERONET-OC above water radiometry data: an estimate of uncertainties

The effects of non-nadir viewing geometry in above-water radiometry data were investigated using field measurements and two different correction approaches: one centered on chlorophyll-a concentration (Chla) developed for Case-1 waters, and the other relying on seawater inherent optical properties (IOP) proposed for any water type. With specific reference to data from the Ocean Color component of the AErosol RObotic NETwork (AERONET-OC), the study focused on the assessment of the uncertainties affecting corrections for non-nadir view of data collected with 40° in-air viewing angle and with 90° relative azimuth between viewing direction and sun. The study analyzed AERONET-OC water-leaving radiance data from different European seas to determine differences between corrections performed with the Chla- and the IOP-based approaches. Additionally, data collected in waters characterized by different optical complexity and comprising water-leaving radiances measured at nadir and with 28.6° in-water viewing angle (corresponding to 40° in-air) and 90° relative azimuth, were used to investigate the uncertainties of the two correction approaches. Results from the analysis of data from AERONET-OC sites characterized by a variety of optically complex waters, indicate corrections with uncertainties between 20% and 35% from 412 nm to 667 nm for the IOP-based approach. Conversely, uncertainties for the Chla-based one largely vary with wavelength and water type, with values of approximately 55% at 412 nm, 20-40% between 490 nm and 551 nm, and exceeding 60% at 667 nm.

Neural network method to correct bidirectional effects in water-leaving radiance

Ocean color algorithms that rely on "atmospherically corrected" nadir water-leaving radiances to infer information about marine constituents such as the chlorophyll concentration depend on a reliable method to convert the angle-dependent measured radiances from the observation direction to the nadir direction. It is also important to convert the measured radiances to the nadir direction when comparing and merging products from different satellite missions. The standard correction method developed by Morel and coworkers requires knowledge of the chlorophyll concentration. Also, the standard method was developed based on the Case 1 (open ocean) assumption, which makes it unsuitable for Case 2 situations such as turbid coastal waters. We introduce a neural network method to convert the angle-dependent water-leaving radiance (or the corresponding remote sensing reflectance) from the observation direction to the nadir direction. This method relies on neither an "atmospheric correction" nor prior knowledge of the water constituents or the inherent optical properties. It directly converts the remote sensing reflectance from an arbitrary slanted viewing direction to the nadir direction by using a trained neural network. This method is fast and accurate, and it can be easily adapted to different remote sensing instruments. Validation using NuRADS measurements in different types of water shows that this method is suitable for both Case 1 and Case 2 waters. In Case 1 or chlorophyll-dominated waters, our neural network method produces corrections similar to those of the standard method. In Case 2 waters, especially sediment-dominated waters, a significant improvement was obtained compared to the standard method.