Paper Info
Title
Unsupervised Sparse Unmixing of Atmospheric Trace Gases From Hyperspectral 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' endmembers 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 datasets. 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.
Year
DOI
Venue
2022
10.1109/LGRS.2022.3141551
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Gases, Absorption, Libraries, Signal to noise ratio, Hyperspectral imaging, Estimation, Satellites, Hyperspectral unmixing, sparse data recovery, trace gas concentration retrieval, TROPOspheric Monitoring Instrument (TROPOMI), unsupervised sparse learning
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Nicomino Fiscante100.34
Pia Addabbo200.34
Filippo Biondi300.34
Gaetano Giunta400.34
Danilo Orlando500.34