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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 18 — Jun. 20, 2012
  • pp: 4065–4072

Determination of combined measurement uncertainty via Monte Carlo analysis for the imaging spectrometer ROSIS

Karim Lenhard  »View Author Affiliations

Applied Optics, Vol. 51, Issue 18, pp. 4065-4072 (2012)

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To enable traceability of imaging spectrometer data, the associated measurement uncertainties have to be provided reliably. Here a new tool for a Monte-Carlo-type measurement uncertainty propagation for the uncertainties that originate from the spectrometer itself is described. For this, an instrument model of the imaging spectrometer ROSIS is used. Combined uncertainties are then derived for radiometrically and spectrally calibrated data using a synthetic at-sensor radiance spectrum as input. By coupling this new software tool with an inverse modeling program, the measurement uncertainties are propagated for an exemplary water data product.

© 2012 Optical Society of America

OCIS Codes
(120.0280) Instrumentation, measurement, and metrology : Remote sensing and sensors
(120.3940) Instrumentation, measurement, and metrology : Metrology
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Instrumentation, Measurement, and Metrology

Original Manuscript: January 5, 2012
Revised Manuscript: April 3, 2012
Manuscript Accepted: April 13, 2012
Published: June 13, 2012

Karim Lenhard, "Determination of combined measurement uncertainty via Monte Carlo analysis for the imaging spectrometer ROSIS," Appl. Opt. 51, 4065-4072 (2012)

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  1. J. Beekhuizen, M. Bachmann, E. Ben-Dor, J. Biesemans, M. Grant, G. Heuvelink, A. Hueni, M. Kneubuehler, E. de Miguel Llanes, A. Pimstein, E. Prado Ortega, I. Reusen, T. Ruhtz, and M. Schaale, “Report on full error propagation concept,” DJ2.1.2, EUFAR FP7 JRA2—HYQUAPRO (European Facility For Airborne Research, 2009).
  2. H. Kaufmann, K. Segl, S. Chabrillat, S. Hofer, T. Stuffler, A. Mueller, R. Richter, G. Schreier, R. Haydn, and H. Bach, “EnMAP a hyperspectral sensor for environmental mapping and analysis,” in Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006 (IEEE, 2006), pp. 1617–1619.
  3. Joint Comittee for Guides in Metrology, “JCGM 101: 2008, Evaluation of measurement data—Supplement 1 to the ‘Guide to the expression of uncertainty in measurement’—Propagation of distributions using a Monte Carlo method,” Tech. Rep. (Bureau International des Poids et Mesures, 2008).
  4. R. O. Green, “Spectral calibration requirement for Earth-looking imaging spectrometers in the solar-reflected spectrum,” Appl. Opt. 37, 683–690 (1998). [CrossRef]
  5. P. Gege, “The water colour simulator WASI: an integrating software tool for analysis and simulation of optical in situ spectra,” Comput. Geosci. 30, 523–532 (2004). [CrossRef]
  6. P. Schwind, R. Müller, G. Palubinskas, T. Storch, and C. Makasy, “A geometric simulator for the hyperspectral mission EnMAP,” presented at the Canadian Geomatics Conference, Calgary, Alberta, Canada, 15–18June2010.
  7. R. Richter and D. Schläpfer, “Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction,” Int. J. Remote Sens. 23, 2631–2649 (2002). [CrossRef]
  8. A. Berk, L. S. Bernstein, and D. C. Robertson, “MODTRAN: a moderate resolution model for LOWTRAN 7,” Tech. Rep. (Geophysics Laboratory, Air Force Command, U. S. Air Force, Hanscom Air Force Base, Massachusetts, USA, 1989).
  9. H. Neckel and D. Labs, “The solar radiation between 3300 and 12500 Å,” Sol. Phys. 90, 205–258 (1984). [CrossRef]
  10. P. Gege, D. Beran, W. Mooshuber, J. Schulz, and H. van der Piepen, “System analysis and performance of the new version of the imaging spectrometer ROSIS,” in Proceedings of the 1st EARSeL Workshop on Imaging Spectroscopy (European Association of Remote Sensing Laboratories, 1998), pp. 29–35.
  11. J. Schulz, “Systemtechnische Untersuchungen an dem abbildenden Spektrometer ROSIS-01 zur Erfassung und Interpretation der Meeresfarbe,” Ph.D. thesis (DLR Institut für Optoelektronik, 1997).
  12. Y. Zong, S. W. Brown, B. C. Johnson, K. R. Lykke, and Y. Ohno, “Simple spectral stray light correction method for array spectroradiometers,” Appl. Opt. 45, 1111–1119 (2006). [CrossRef]
  13. K. Lenhard, P. Gege, and M. Damm, “Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor,” presented at the 6th EARSeL Workshop on Imaging Spectroscopy, Tel Aviv, 16–19March2009.
  14. S. Lavender, O. F. D’Andon, S. Kay, L. Bourg, S. Emsley, N. Gilles, T. Nightingale, R. Quast, M. Bates, T. Storm, J. Hedley, M. Knul, G. Sotis, R. Nasir-Habeeb, and P. Goryl, “Applying uncertainties to ocean colour data,” Metrologia 49, S17–S20 (2012). [CrossRef]
  15. A. Börner, L. Wiest, P. Keller, R. Reulke, R. Richter, and M. Schaepman, “SENSOR: a tool for the simulation of hyperspectral remote sensing systems,” ISPRS J. Photogramm. Remote Sens. 55, 299–312 (2001). [CrossRef]

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