Preprints
https://doi.org/10.48550/arXiv.2404.05298
https://doi.org/10.48550/arXiv.2404.05298
17 Apr 2024
 | 17 Apr 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

In-Flight Estimation of Instrument Spectral Response Functions Using Sparse Representations

Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet, Jean-Yves Tourneret, and Herwig Wendt

Abstract. Accurate estimates of Instrument Spectral Response Functions (ISRFs) are crucial in order to have a good characterization of high resolution spectrometers. Spectrometers are composed of different optical elements that can induce errors in the measurements and therefore need to be modeled as accurately as possible. Parametric models are currently used to estimate these response functions. However, these models cannot always take into account the diversity of ISRF shapes that are encountered in practical applications. This paper studies a new ISRF estimation method based on a sparse representation of atoms belonging to a dictionary. This method is applied to different high-resolution spectrometers in order to assess its reproducibility for multiple remote sensing missions. The proposed method is shown to be very competitive when compared to the more commonly used parametric models, and yields normalized ISRF estimation errors less than 1 %.

Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet, Jean-Yves Tourneret, and Herwig Wendt

Status: open (until 12 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet, Jean-Yves Tourneret, and Herwig Wendt
Jihanne El Haouari, Jean-Michel Gaucel, Christelle Pittet, Jean-Yves Tourneret, and Herwig Wendt

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Short summary
This paper explores new techniques based on sparse representations for estimating the spectral response functions of high-resolution spectrometers. The method is highly competitive with commonly used parametric models yielding more accurate estimates while accounting for wavelength dependence. The resulting normalized estimation errors of the spectrometer spectral responses are less than 1 %, which will allow better quantification of trace gas concentrations at the Earth surface.