Retrieval of aerosol properties and water-leaving reflectance from multi-angular polarimetric measurements over coastal waters

Satellite retrieval of the linear polarization components of the water-leaving radiance in open oceans

Atmospheric correction (AC) of polarized radiances acquired by polarization satellite sensors, remains a challenge due to the complex radiative transfer processes of the coupled ocean-atmosphere system. In this study, we proposed an innovative polarized AC algorithm built on the near-infrared band (PACNIR) with an emphasis on the retrieval of the linear polarization components of the water-leaving radiance in clear open oceans. This algorithm was based on the black ocean assumption in the near-infrared band and fitted polarized radiance measurements along multiple observation directions with nonlinear optimized processing. Our retrieval algorithm notably inverted the linearly polarized components of the water-leaving radiance and aerosol parameters. Compared with that of the simulated linear polarization components of the water-leaving radiance via the vector radiative transfer model for the studied sea regions, the mean absolute error of the PACNIR-retrieved linearly polarized components (nQw and nUw) exhibited a magnitude of 10^-4, while the magnitude of that of the simulated nQw and nUw data was 10^-3. Moreover, the PACNIR-retrieved aerosol optical thicknesses at 865 nm exhibited a mean absolute percentage error of approximately 30% relative to in situ values obtained from Aerosol Robotic Network-Ocean Color (AERONET-OC) sites. The PACNIR algorithm could facilitate AC of the polarized data provided by the next generation of multiangle polarization satellite ocean color sensors.

Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color

We investigated the optimal number of independent parameters required to accurately represent spectral remote sensing reflectances (R _rs) by performing principal component analysis on quality controlled in situ and synthetic R _rs data. We found that retrieval algorithms should be able to retrieve no more than four free parameters from R _rs spectra for most ocean waters. In addition, we evaluated the performance of five different bio-optical models with different numbers of free parameters for the direct inversion of in-water inherent optical properties (IOPs) from in situ and synthetic R _rs data. The multi-parameter models showed similar performances regardless of the number of parameters. Considering the computational cost associated with larger parameter spaces, we recommend bio-optical models with three free parameters for the use of IOP or joint retrieval algorithms.

Instantaneous photosynthetically available radiation models for ocean waters using neural networks

Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR prediction models based on the neural network (NN) approach, one for open ocean and the other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute the subsurface IPAR profile very accurately up to the euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011m^-1 and 0.036m^-1 for open ocean and coastal waters, respectively. The performance of the NN models is better than presently available semi-analytical models, with significant superiority in coastal waters.

Universal Synthesizer of Mueller Matrices Based on the Symmetry Properties of the Enpolarizing Ellipsoid

Polarimetry is today a widely used and powerful tool for nondestructive analysis of the structural and morphological properties of a great variety of material samples, including aerosols and hydrosols, among many others. For each given scattering measurement configuration, absolute Mueller polarimeters provide the most complete polarimetric information, intricately encoded in the 16 parameters of the corresponding Mueller matrix. Thus, the determination of the mathematical structure of the polarimetric information contained in a Mueller matrix constitutes a topic of great interest. In this work, besides a structural decomposition that makes explicit the role played by the diattenuation-polarizance of a general depolarizing medium, a universal synthesizer of Muller matrices is developed. This is based on the concept of an enpolarizing ellipsoid, whose symmetry features are directly linked to the way in which the polarimetric information is organized.

Atmospheric correction over the ocean for hyperspectral radiometers using multi-angle polarimetric retrievals

We developed a fast and accurate polynomial based atmospheric correction (POLYAC) algorithm for hyperspectral radiometric measurements, which parameterizes the atmospheric path radiances using aerosol properties retrieved from co-located multi-wavelength multi-angle polarimeter (MAP) measurements. This algorithm has been applied to co-located spectrometer for planetary exploration (SPEX) airborne and research scanning polarimeter (RSP) measurements, where SPEX airborne was used as a proxy of hyperspectral radiometers, and RSP as the MAP. The hyperspectral remote sensing reflectance obtained from POLYAC is accurate when compared to Aerosol Robotic Network (AERONET), and Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products. POLYAC provides a robust alternative atmospheric correction algorithm for hyperspectral or multi-spectral radiometric measurements for scenes involving coastal oceans and/or absorbing aerosols, where traditional atmospheric correction algorithms are less reliable.

Mantis: an all-sky visible-to-near-infrared hyper-angular spectropolarimeter

In this paper, we introduce and present first results from Mantis, a pushbroom type spectropolarimeter recently acquired by the Naval Research Laboratory and built by Polaris Sensor Technologies, Inc. The instrument is designed for high spatial and spectral resolution polarimetric imaging of downwelling skylight. Linear Stokes vectors are acquired over the spectral range of 382-1017 nm, with ≈0.64nm channel spacing, and each line scan consists of 2226 pixels over a 72° field of view (0.75 mrad instantaneous). Measurement of the full sky dome is achieved through the use of a high-precision motorized pan-tilt unit and systematic scanning. An automated Sun shade allows for data collection in the main solar plane without saturation of the focal plane. The uncertainty in the degree of linear polarization varies between 0.07% and 0.5%, depending on incidence angle and wavelength. The total radiometric uncertainty is 2.07% to 2.5%, of which 2% is absolute calibration error. Preliminary data analysis reveals the instrument has a large potential for remote sensing applications.

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Hyperspectral polarimetric imaging of the water surface and retrieval of water optical parameters from multi-angular polarimetric data

Total and polarized radiances from above the ocean surface are measured by a state-of-the-art snapshot hyperspectral imager. A computer-controlled filter wheel is installed in front of the imager allowing for recording of division-of-time Stokes vector images from the ocean surface. This system, to the best of our knowledge, for the first time provided a capability of hyperspectral polarimetric multi-angular measurements of radiances from above the water surface. Several sets of measurements used in the analysis were acquired from ocean platforms and from shipborne observations. Measurements made by the imager are compared with simulations using a vector radiative transfer (VRT) code showing reasonable agreement. Analysis of pixel-to-pixel variability of the total and polarized above-water radiance for the viewing angles of 20°-60° in different wind conditions enable the estimation of uncertainties in measurements of these radiances in the polarized mode for the spectral range of 450-750 nm, thus setting requirements for the quality of polarized measurements. It is shown that there is a noticeable increase of above-water degree of linear polarization (DoLP) as a function of the viewing angle, which is due both to the larger DoLP of the light from the water body and the light reflected from the ocean surface. Results of measurements and VRT simulations are applied for the multi-angular retrieval of the ratio of beam attenuation coefficient (c_tot) to absorption coefficient (a_tot) in addition to the other parameters such as absorption and backscattering coefficients retrieved from traditional unpolarized methods.

Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements

For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties

UV-NIR approach with non-zero water-leaving radiance approximation for atmospheric correction of satellite imagery in inland and coastal zones

In the atmospheric correction process of the satellite ocean color data, the removal of the aerosol scattering contribution over the coastal and inland water bodies has been a major challenge with the standard algorithms. In this work, a practical method is proposed based on a combination of NIR and ultraviolet (UV) bands (named as UVNIR-ex) for the succeeding generation of space borne multispectral and hyperspectral sensors. This scheme replaces the black-ocean assumption and accounts for non-zero water-leaving radiance contributions in the NIR and UV bands. The aerosol contributions are thus deduced for these two bands and used to select the appropriate aerosol models to retrieve aerosol optical properties and hence, water-leaving radiances in the UV, Visible and NIR bands. The performance of the UVNIR-ex algorithm was tested and evaluated based on match-ups between HICO and in-situ observations in optically complex coastal and inland waters and by comparison with three alternative aerosol correction methods based on UV-NIR, Spectral Shape Parameter (SSP) and iterative NIR (INIR) approaches. A preliminary comparison with in-situ aerosol optical thickness (AOT) measurements from AERONET-OC sites revealed that the UVNIR-ex algorithm significantly improved the AOT retrievals with a mean relative error (MRE) around 25%, while the UVNIR, SSP and INIR algorithms showed performance degradation with a MRE of 27%, 34%, and 42%, respectively. The comparison with AERONET-OC and regional in-situ measurements from turbid and productive waters further showed that the INIR algorithm underestimated the nL_w retrievals in blue bands in turbid waters (MRE > 100%) and negligible nL_w in red-NIR bands and high anomalous radiances in UV-Blue bands in productive waters (MRE 53%). The SSP and UVNIR algorithms performed better in retrieving the nL_w in green-NIR bands but showed significant errors in UV-blue bands in both turbid and productive waters. Based on these match-up analyses, the UVNIR-ex algorithm yielded best nL_w retrievals across all the UV-NIR bands in terms of accuracy and performance. The highest accuracy and consistency of the UVNIR-ex algorithm indicates that it is more suited for estimating the aerosol optical properties and water-leaving radiance and has a significant advantage over the requirement of shortwave infrared bands for turbid and productive waters.

Study on polarization scattering applied in aerosol recognition in the air

In this work, we present an in situ online aerosol recognition scheme by synchronized parallel polarization scattering analysis. By theoretical simulations, we select the feasible scattering angles and evaluate the potential of Stokes parameters to identify aerosols. Correspondingly, we develop a measurement system based on multi-angle optical scattering and multidimensional polarization analyzing technique. We construct two index groups based on non-normalized and normalized polarization parameters respectively, and employ their frequency distribution histograms instead of the simple average values to identify and classify different types of aerosols. The experimental verification confirms a future way of a multi-dimensional polarization parameter group applied in a fast and effective air pollutants monitoring.

Radiative Transfer Modeling of Phytoplankton Fluorescence Quenching Processes

We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photochemical and non-photochemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation wavelengths. The photochemical and nonphotochemical quenching processes change the quantum yield based on the photosynthetic active radiation. A sensitivity study was performed to demonstrate the dependence of the fluorescence signal on chlorophyll a concentration, aerosol optical depths and solar zenith angles. This work enables us to better model the phytoplankton fluorescence, which can be used in the design of new space-based sensors that can provide sufficient sensitivity to detect the phytoplankton fluorescence signal. It could also lead to more accurate remote sensing algorithms for the study of phytoplankton physiology.