Unsupervised Sparse Unmixing of Atmospheric Trace Gases from Hyperspectral Satellite Data

TL;DR

This paper introduces a novel unsupervised sparse unmixing method leveraging compressive sensing to accurately retrieve atmospheric trace gases from hyperspectral satellite data, validated with real volcanic emission data.

Contribution

It presents a new unsupervised sparse unmixing approach for trace gas retrieval using hyperspectral data and a spectral library, incorporating compressive sensing techniques.

Findings

Effective retrieval of sulfur dioxide during volcanic emissions.

Comparable results with existing differential optical absorption spectroscopy.

Validated with both simulated and real satellite data.

Abstract

In this letter, a new approach for the retrieval of the vertical column concentrations of trace gases from hyperspectral satellite observations, is proposed. The main idea is to perform a linear spectral unmixing by estimating the abundances of trace gases spectral signatures in each mixed pixel collected by an imaging spectrometer in the ultraviolet region. To this aim, the sparse nature of the measurements is brought to light and the compressive sensing paradigm is applied to estimate the concentrations of the gases' endemembers given by an a priori wide spectral library, including reference cross sections measured at different temperatures and pressures at the same time. The proposed approach has been experimentally assessed using both simulated and real hyperspectral dataset. Specifically, the experimental analysis relies on the retrieval of sulfur dioxide during volcanic emissions using data collected by the TROPOspheric Monitoring Instrument. To validate the procedure, we also compare the obtained results with the sulfur dioxide total column product based on the differential optical absorption spectroscopy technique and the retrieved concentrations estimated using the blind source separation.