Determination of emissivity profiles using a Bayesian data-driven approach

TL;DR

This paper presents a Bayesian data-driven method to determine spectral emissivity profiles by integrating a land cover database with high-resolution profiles, improving accuracy over traditional interpolation methods.

Contribution

The paper introduces a novel Bayesian approach that combines emissivity databases with land cover data to derive more accurate spectral emissivity profiles.

Findings

Outperforms linear spline interpolation of CAMEL data

Provides a more accurate initial guess for retrieval algorithms

Successfully tested on IASI data

Abstract

In this paper, we explore the determination of a spectral emissivity profile that closely matches real data, intended for use as an initial guess and/or a-priori information in a retrieval code. Our approach employs a Bayesian method that integrates the CAMEL (Combined ASTER MODIS Emissivity over Land) emissivity database with a land cover map. The solution is derived as a convex combination of high-resolution Huang profiles using the Bayesian framework. We test our method on IASI (Infrared Atmospheric Sounding Interferometer) data and find that it outperforms the linear spline interpolation of the CAMEL data.