Linearity Characterization and Uncertainty Quantification of Spectroradiometers via Maximum Likelihood and the Non-parametric Bootstrap

Published

February 13, 2023

Author(s)

Adam L. Pintar, Zachary H. Levine, Howard W. Yoon, Stephen Maxwell

Abstract

A technique for characterizing and correcting the linearity of radiometric instruments is known by the names the 'flux-addition method' and the 'combinatorial technique'. In this paper, we develop a rigorous uncertainty quantification method for use with this technique and illustrate its use with both synthetic data and experimental data from a 'beam conjoiner' instrument. We present a probabilistic model that relates the instrument readout to a set of unknown fluxes via a set of polynomial coefficients. Maximum likelihood estimates (MLEs) of the unknown fluxes and polynomial coefficients are recommended, while a non-parametric bootstrap algorithm enables uncertainty quantification including standard errors and confidence intervals. The synthetic data represent plausible outputs of a radiometric instrument and enable testing and validation of the method. The MLEs for these data are found to be approximately unbiased, and confidence intervals derived from the bootstrap replicates are found to be consistent with their target coverage of 95%. For the polynomial coefficients, the observed coverages range from 91% to 99%. The experimental data set illustrates how a complete calibration with uncertainties can be achieved using the method plus one well-known flux level. The uncertainty contribution attributable to estimation of the instrument's non-linear response is less than 0.025% over most of its range.

Citation

Metrologia

Pub Type

Journals

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Keywords

Satellite Based Measurements, Spectroradiometer, Calibration, Maximum Likelihood, Bootstrap

Citation

Pintar, A. , Levine, Z. , Yoon, H. and Maxwell, S. (2023), Linearity Characterization and Uncertainty Quantification of Spectroradiometers via Maximum Likelihood and the Non-parametric Bootstrap, Metrologia, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934247 (Accessed October 8, 2024)