A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing

Abstract

Aerosol retrieval algorithms used in conjunction with remote sensing are subject to ill-posedness. To mitigate non-uniqueness, extra constraints (in addition to observations) are valuable for stabilizing the inversion process. This paper focuses on the imposition of an empirical correlation constraint on the retrieved aerosol parameters. This constraint reflects the empirical dependency between different aerosol parameters, thereby reducing the number of degrees of freedom and enabling accelerated computation of the radiation fields associated with neighboring pixels. A cross-pixel constraint that capitalizes on the smooth spatial variations of aerosol properties was built into the original multi-pixel inversion approach. Here, the spatial smoothness condition is imposed on principal components (PCs) of the aerosol model, and on the corresponding PC weights, where the PCs are used to characterize departures from the mean. Mutual orthogonality and unit length of the PC vectors, as well as zero sum of the PC weights also impose stabilizing constraints on the retrieval. Capitalizing on the dependencies among aerosol parameters and the mutual orthogonality of PCs, a perturbation-based radiative transfer computation scheme is developed. It uses a few dominant PCs to capture the difference in the radiation fields across an imaged area. The approach is tested using 27 observations acquired by the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) during multiple NASA field campaigns and validated using collocated AERONET observations. In particular, aerosol optical depth, single scattering albedo, aerosol size, and refractive index are compared with AERONET aerosol reference data. Retrieval uncertainty is formulated by accounting for both instrumental errors and the effects of multiple types of constraints.

A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

“…Candidate algorithms for aerosol retrieval from information-rich future sensors also tend to use smoothness constraints (e.g. Xu et al, 2019). All these covariance matrices are assumed to be Gaussian, which may not always be true in practice.…”

Aerosol retrievals from the ACEPOL Campaign

“…AirMSPI (Diner et al, 2013) Xu et al (2017Xu et al ( , 2018Xu et al ( , 2019. In this study, only the step-and-stare measurements have been used as they provide a mult-angle-view of the same ground scene.…”

Inversion of multi-angular polarimetric measurements from the ACEPOL campaign: an application of improving aerosol property and hyperspectral ocean color retrievals

“…In order to retrieve aerosol information from polarimetric measurements over the ocean, a number of advanced aerosol retrieval algorithms have been developed for both airborne and spaceborne MAPs, such as POLDER/PARASOL (Hasekamp et al, 2011;Dubovik et al, 2011Dubovik et al, , 2014, AirMSPI (Xu et al, 2016(Xu et al, , 2019, SPEX Airborne (Fu and Hasekamp, 2018;Fu et al, 2019;Fan et al, 2019), RSP (Chowdhary et al, 2005;Wu et al, 2015;Stamnes et al, 2018;Gao et al, 2018Gao et al, , 2019, and Directional Polarimetric Camera (DPC)/GaoFen-5 (Wang et al, 2014;Li et al, 2018). In this study, we use the Multi-Angle Polarimetric Ocean coLor (MAPOL) retrieval algorithm, which is a joint aerosol and water-leaving radiance retrieval algorithm designed with the bio-optical models applicable to both open and coastal waters (Gao et al, 2018(Gao et al, , 2019.…”