Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean

Abstract

Abstract. Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

Determining the primary sources of uncertainty in the retrieval of marine remote sensing reflectance from satellite ocean color sensors II. Sentinel 3 OLCI sensors

“…Since the validity of the assumption that the uncertainties spectra are proportional to the spectra of the components themselves can be less accurate for aerosols than for other components, it is possible that the optimization procedure underestimates the contribution of the aerosol component to the total uncertainties. Generally, however, the aerosol uncertainty levels are consistent with their estimations or slightly below them (Wang, 2007;Gao et al, 2022), where they are at the level of 10 -4 -4*10 -4 sr −1 . LISCO station has a pronounced aerosol component for both OLCI S3A and S3B, which is not visible in VIIRS.…”

Biomass Burning Plume from Simultaneous Observations of Polarization and Radiance at Different Viewing Directions with SGLI

“…With the advent of POLDER sensors, multi-directional polarization observations have brought great progress in satellite remote sensing. Starting with the multi-viewing multi-channel multi-polarization imager (3MI) [39], the successor of POLDER, many multidirectional polarization sensors have been programmed to fly in the late 2020s [40][41][42]. In preparation for future polarimetric and multi-directional missions, global data from the SGLI provide an opportunity to demonstrate the utility of multi-directional and polarimetric measurements.…”

Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE

“…An uncertainty of AOD retrievals of 0.01 could lead to an uncertainty of aerosol radiative forcing of ∼ 0.5 W m −2 (Chylek et al, 2003;Hansen et al, 1995;Mishchenko et al, 2004). Two approaches are regularly used to quantify uncertainties of AOD retrievals: propagated (prognostic) uncertainty and truth-based (diagnostic) uncertainty (Gao et al, 2022;Sayer et al, 2020). The former is a theoretical estimate which is obtained by considering measurement uncertainties, forward model uncertainties, and a priori assumptions via minimizing a suitable cost function.…”

“…The latter is to compare the retrieval with a reference dataset. The two approaches are complementary to each other as the reference dataset can assess the theoretical estimate using appropriate statistical methods (Gao et al, 2022).…”