Fitting the curve in Excel®: Systematic curve fitting of laboratory and remotely sensed planetary spectra

M.A. McCraig, G.R. Osinski, E.A. Cloutis, R.L. Flemming, M.R.M. Izawa, V. Reddy, S.K. Fieber-Beyer, L. Pompilio, F.D. van der Meer, J.A. Berger, M.S. Bramble, D.M. Applin

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Spectroscopy in planetary science often provides the only information regarding the compositional and mineralogical make up of planetary surfaces. The methods employed when curve fitting and modelling spectra can be confusing and difficult to visualize and comprehend. Researchers who are new to working with spectra may find inadequate help or documentation in the scientific literature or in the software packages available for curve fitting. This problem also extends to the parameterization of spectra and the dissemination of derived metrics. Often, when derived metrics are reported, such as band centres, the discussion of exactly how the metrics were derived, or if there was any systematic curve fitting performed, is not included. Herein we provide both recommendations and methods for curve fitting and explanations of the terms and methods used. Techniques to curve fit spectral data of various types are demonstrated using simple-to-understand mathematics and equations written to be used in Microsoft Excel® software, free of macros, in a cut-and-paste fashion that allows one to curve fit spectra in a reasonably user-friendly manner. The procedures use empirical curve fitting, include visualizations, and ameliorates many of the unknowns one may encounter when using black-box commercial software. The provided framework is a comprehensive record of the curve fitting parameters used, the derived metrics, and is intended to be an example of a format for dissemination when curve fitting data.
Original languageEnglish
Pages (from-to)103-114
JournalComputers & geosciences
Volume100
DOIs
Publication statusPublished - 2017

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Curve fitting
software
planetary surface
mathematics
visualization
parameterization
spectroscopy
Parameterization
laboratory
Software packages
Macros
modeling
Visualization
method
Spectroscopy

Keywords

  • METIS-321166
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

McCraig, M. A., Osinski, G. R., Cloutis, E. A., Flemming, R. L., Izawa, M. R. M., Reddy, V., ... Applin, D. M. (2017). Fitting the curve in Excel®: Systematic curve fitting of laboratory and remotely sensed planetary spectra. Computers & geosciences, 100, 103-114. https://doi.org/10.1016/j.cageo.2016.11.018
McCraig, M.A. ; Osinski, G.R. ; Cloutis, E.A. ; Flemming, R.L. ; Izawa, M.R.M. ; Reddy, V. ; Fieber-Beyer, S.K. ; Pompilio, L. ; van der Meer, F.D. ; Berger, J.A. ; Bramble, M.S. ; Applin, D.M. / Fitting the curve in Excel® : Systematic curve fitting of laboratory and remotely sensed planetary spectra. In: Computers & geosciences. 2017 ; Vol. 100. pp. 103-114.
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abstract = "Spectroscopy in planetary science often provides the only information regarding the compositional and mineralogical make up of planetary surfaces. The methods employed when curve fitting and modelling spectra can be confusing and difficult to visualize and comprehend. Researchers who are new to working with spectra may find inadequate help or documentation in the scientific literature or in the software packages available for curve fitting. This problem also extends to the parameterization of spectra and the dissemination of derived metrics. Often, when derived metrics are reported, such as band centres, the discussion of exactly how the metrics were derived, or if there was any systematic curve fitting performed, is not included. Herein we provide both recommendations and methods for curve fitting and explanations of the terms and methods used. Techniques to curve fit spectral data of various types are demonstrated using simple-to-understand mathematics and equations written to be used in Microsoft Excel{\circledR} software, free of macros, in a cut-and-paste fashion that allows one to curve fit spectra in a reasonably user-friendly manner. The procedures use empirical curve fitting, include visualizations, and ameliorates many of the unknowns one may encounter when using black-box commercial software. The provided framework is a comprehensive record of the curve fitting parameters used, the derived metrics, and is intended to be an example of a format for dissemination when curve fitting data.",
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author = "M.A. McCraig and G.R. Osinski and E.A. Cloutis and R.L. Flemming and M.R.M. Izawa and V. Reddy and S.K. Fieber-Beyer and L. Pompilio and {van der Meer}, F.D. and J.A. Berger and M.S. Bramble and D.M. Applin",
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McCraig, MA, Osinski, GR, Cloutis, EA, Flemming, RL, Izawa, MRM, Reddy, V, Fieber-Beyer, SK, Pompilio, L, van der Meer, FD, Berger, JA, Bramble, MS & Applin, DM 2017, 'Fitting the curve in Excel®: Systematic curve fitting of laboratory and remotely sensed planetary spectra' Computers & geosciences, vol. 100, pp. 103-114. https://doi.org/10.1016/j.cageo.2016.11.018

Fitting the curve in Excel® : Systematic curve fitting of laboratory and remotely sensed planetary spectra. / McCraig, M.A.; Osinski, G.R.; Cloutis, E.A.; Flemming, R.L.; Izawa, M.R.M.; Reddy, V.; Fieber-Beyer, S.K.; Pompilio, L.; van der Meer, F.D.; Berger, J.A.; Bramble, M.S.; Applin, D.M.

In: Computers & geosciences, Vol. 100, 2017, p. 103-114.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Fitting the curve in Excel®

T2 - Systematic curve fitting of laboratory and remotely sensed planetary spectra

AU - McCraig, M.A.

AU - Osinski, G.R.

AU - Cloutis, E.A.

AU - Flemming, R.L.

AU - Izawa, M.R.M.

AU - Reddy, V.

AU - Fieber-Beyer, S.K.

AU - Pompilio, L.

AU - van der Meer, F.D.

AU - Berger, J.A.

AU - Bramble, M.S.

AU - Applin, D.M.

PY - 2017

Y1 - 2017

N2 - Spectroscopy in planetary science often provides the only information regarding the compositional and mineralogical make up of planetary surfaces. The methods employed when curve fitting and modelling spectra can be confusing and difficult to visualize and comprehend. Researchers who are new to working with spectra may find inadequate help or documentation in the scientific literature or in the software packages available for curve fitting. This problem also extends to the parameterization of spectra and the dissemination of derived metrics. Often, when derived metrics are reported, such as band centres, the discussion of exactly how the metrics were derived, or if there was any systematic curve fitting performed, is not included. Herein we provide both recommendations and methods for curve fitting and explanations of the terms and methods used. Techniques to curve fit spectral data of various types are demonstrated using simple-to-understand mathematics and equations written to be used in Microsoft Excel® software, free of macros, in a cut-and-paste fashion that allows one to curve fit spectra in a reasonably user-friendly manner. The procedures use empirical curve fitting, include visualizations, and ameliorates many of the unknowns one may encounter when using black-box commercial software. The provided framework is a comprehensive record of the curve fitting parameters used, the derived metrics, and is intended to be an example of a format for dissemination when curve fitting data.

AB - Spectroscopy in planetary science often provides the only information regarding the compositional and mineralogical make up of planetary surfaces. The methods employed when curve fitting and modelling spectra can be confusing and difficult to visualize and comprehend. Researchers who are new to working with spectra may find inadequate help or documentation in the scientific literature or in the software packages available for curve fitting. This problem also extends to the parameterization of spectra and the dissemination of derived metrics. Often, when derived metrics are reported, such as band centres, the discussion of exactly how the metrics were derived, or if there was any systematic curve fitting performed, is not included. Herein we provide both recommendations and methods for curve fitting and explanations of the terms and methods used. Techniques to curve fit spectral data of various types are demonstrated using simple-to-understand mathematics and equations written to be used in Microsoft Excel® software, free of macros, in a cut-and-paste fashion that allows one to curve fit spectra in a reasonably user-friendly manner. The procedures use empirical curve fitting, include visualizations, and ameliorates many of the unknowns one may encounter when using black-box commercial software. The provided framework is a comprehensive record of the curve fitting parameters used, the derived metrics, and is intended to be an example of a format for dissemination when curve fitting data.

KW - METIS-321166

KW - ITC-ISI-JOURNAL-ARTICLE

UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2017/isi/vandermeer_fit.pdf

U2 - 10.1016/j.cageo.2016.11.018

DO - 10.1016/j.cageo.2016.11.018

M3 - Article

VL - 100

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EP - 114

JO - Computers & geosciences

JF - Computers & geosciences

SN - 0098-3004

ER -