Search

Multi-angle polarimeters (MAPs) are powerful instruments to perform remote sensing of the environment. Joint retrieval algorithms of aerosols and ocean color have been developed to extract the rich information content of MAPs. These are optimization algorithms that fit the sensor measurements with forward models, which include radiative transfer simulations of the coupled atmosphere and ocean systems (CAOSs). The forward model consists of sub-models to represent the optics of the atmosphere, ocean water surface and ocean body. The representativeness of these models for observed scenes and the number of retrieval parameters are important for retrieval success. In this study, we have evaluated the impact of three different ocean bio-optical models with one, three and seven optimization parameters on the accuracy of joint retrieval algorithms of MAPs. The Multi-Angular Polarimetric Ocean coLor (MAPOL) joint retrieval algorithm was used to process data from the airborne Research Scanning Polarimeter (RSP) instrument acquired in different field campaigns. We performed ensemble retrievals along three RSP legs to evaluate the applicability of bio-optical models in geographically varying water of clear to turbid conditions. The average differences between the MAPOL aerosol optical depth (AOD) and spectral remote sensing reflectance (Rrs(λ)) retrievals and the MODerate resolution Imaging Spectroradiometer (MODIS) products were also reported. We studied the distribution of retrieval cost function values obtained for the three bio-optical models. For the one-parameter model, the spread of retrieval cost function values is narrow regardless of the type of water even if it fails to converge over coastal water. For the three- and seven-parameter models, the retrieval cost function distribution is water type dependent, showing the widest distribution over clear, open water. This suggests that caution should be used when using the spread of the cost function distribution to represent the retrieval uncertainty. We observed that the three- and seven-parameter models have similar MAP retrieval performances in all cases, though they are prone to converge at local minima over open-ocean water. It is necessary to develop a screening algorithm to divide open and coastal water before performing MAP retrievals. Given the computational efficiency and the algorithm stability requirements, we recommend the three-parameter bio-optical model as the coastal-water bio-optical model for future MAPOL studies. This study provides important practical guides on the joint retrieval algorithm development for current and future satellite missions such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and ESA's Meteorological Operational-Second Generation (MetOp-SG) mission. [ABSTRACT FROM AUTHOR]

The University of Maryland, Baltimore County (UMBC) Hyper-Angular Rainbow Polarimeter (HARP2) will be on board NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in January 2024. In this study we systematically evaluate the retrievability and uncertainty of aerosol and ocean parameters from HARP2 multi-angle polarimeter (MAP) measurements. To reduce the computational demand of MAP-based retrievals and maximize data processing throughput, we developed improved neural network (NN) forward models for spaceborne HARP2 measurements over a coupled atmosphere and ocean system within the FastMAPOL retrieval algorithm. To this end, a cascading retrieval scheme is implemented in FastMAPOL, which leverages a series of NN models of varying size, speed, and accuracy to optimize performance. Two sets of NN models are used for reflectance and polarization, respectively. A full day of global synthetic HARP2 data was generated and used to test various retrieval parameters including aerosol microphysical and optical properties, aerosol layer height, ocean surface wind speed, and ocean chlorophyll a concentration. To assess retrieval quality, pixel-wise retrieval uncertainties were derived from error propagation and evaluated against the difference between the retrieval parameters and truth based on a Monte Carlo method. We found that the fine-mode aerosol properties can be retrieved well from the HARP2 data, though the coarse-mode aerosol properties are more uncertain. Larger uncertainties are associated with a reduced number of available viewing angles, which typically occur near the scan edge of the HARP2 instrument. Results of the performance assessment demonstrate that the algorithm is a viable approach for operational application to HARP2 data after the PACE launch. [ABSTRACT FROM AUTHOR]