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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 4 — Feb. 1, 2012
  • pp: 439–449

End-to-end sensor simulation for spectral band selection and optimization with application to the Sentinel-2 mission

Karl Segl, Rudolf Richter, Theres Küster, and Hermann Kaufmann  »View Author Affiliations

Applied Optics, Vol. 51, Issue 4, pp. 439-449 (2012)

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An end-to-end sensor simulation is a proper tool for the prediction of the sensor’s performance over a range of conditions that cannot be easily measured. In this study, such a tool has been developed that enables the assessment of the optimum spectral resolution configuration of a sensor based on key applications. It employs the spectral molecular absorption and scattering properties of materials that are used for the identification and determination of the abundances of surface and atmospheric constituents and their interdependence on spatial resolution and signal-to-noise ratio as a basis for the detailed design and consolidation of spectral bands for the future Sentinel-2 sensor. The developed tools allow the computation of synthetic Sentinel-2 spectra that form the frame for the subsequent twofold analysis of bands in the atmospheric absorption and window regions. One part of the study comprises the assessment of optimal spatial and spectral resolution configurations for those bands used for atmospheric correction, optimized with regard to the retrieval of aerosols, water vapor, and the detection of cirrus clouds. The second part of the study presents the optimization of thematic bands, mainly driven by the spectral characteristics of vegetation constituents and minerals. The investigation is performed for different wavelength ranges because most remote sensing applications require the use of specific band combinations rather than single bands. The results from the important “red-edge” and the “short-wave infrared” domains are presented. The recommended optimum spectral design predominantly confirms the sensor parameters given by the European Space Agency. The system is capable of retrieving atmospheric and geobiophysical parameters with enhanced quality compared to existing multispectral sensors. Minor spectral changes of single bands are discussed in the context of typical remote sensing applications, supplemented by the recommendation of a few new bands for the next generation of optical Sentinel sensors.

© 2012 Optical Society of America

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

ToC Category:
Remote Sensing and Sensors

Original Manuscript: July 15, 2011
Manuscript Accepted: September 8, 2011
Published: January 26, 2012

Karl Segl, Rudolf Richter, Theres Küster, and Hermann Kaufmann, "End-to-end sensor simulation for spectral band selection and optimization with application to the Sentinel-2 mission," Appl. Opt. 51, 439-449 (2012)

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  1. A. Börner, L. Wiest, P. Keller, R. Reulke, R. Richter, M. Schaepman, and D. Schläpfer, “SENSOR: a tool for the simulation of hyperspectral remote sensing systems,” ISPRS J. Photogram. Remote Sens. 55, 299–312 (2001). [CrossRef]
  2. J. P. Kerekes and J. E. Baum, “Full-spectrum spectral imaging system analytical model,” IEEE Trans. Geosci. Remote Sens. 43, 571–580 (2005). [CrossRef]
  3. W. Verhoef and H. Bach, “Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models,” Remote Sens. Environ. 87, 23–41 (2003). [CrossRef]
  4. L. Guanter, K. Segl, and H. Kaufmann, “Simulation of optical remote sensing scenes with application to the EnMAP hyperspectral mission,” IEEE Trans. Geosci. Remote Sens. 47, 2340–2351 (2009). [CrossRef]
  5. K. Segl, L. Guanter, and H. Kaufmann, “Simulation of spatial sensor characteristics in the context of the EnMAP hyperspectral mission,” IEEE Trans. Geosci. Remote Sens. 48, 3046–3054 (2010). [CrossRef]
  6. H. Kaufmann, D. Meißner, J. Bodechtel, and F.-J. Behr, “Design of spectral and panchromatic bands for the German MOMS-02 sensor,” Photogram. Eng. Remote Sens. 55, 875–881 (1989).
  7. M. Berger and H. Kaufmann, “MOMS-02—D2/STS-55 Mission—validation of spectral and panchromatic modules,” Geo-Informationssysteme 8/2, 21–31 (1995).
  8. ESA Sentinel-2 Team, GMES Sentinel-2 Mission Requirements Document EOP-SM/1163/MR-dr, http://www.esa.int/esaLP/SEMM4T4KXMF_LPgmes_0.html (2007).
  9. K. Segl, H. Kaufmann, and R. Richter, “Study for the consolidation of the Sentinel-2 spectral, radiometric and spatial resolution requirements,” ESA contract 19962/06/NL/E (2006).
  10. R. Richter, “Sentinel-2 MSI—Level 2A Products Algorithm Theoretical Basis Document”—Volume B (ATCOR), ESA contract 21450/08/I-EC (2010).
  11. A. Berk, L. S. Bernstein, G. P. Anderson, P. K. Acharya, D. C. Robertson, J. J. Chetwynd, and S. M. Adler-Golden, “MODTRAN cloud and multiple scattering upgrades with application to AVIRIS,” Remote Sens. Environ. 65, 367–375 (1998). [CrossRef]
  12. A. Berk, G. P. Anderson, P. K. Acharya, M. Hoke, J. H. Chetwynd, L. S. Bernstein, E. P. Shettle, M. W. Matthew, and S. M. Adler-Golden, MODTRAN4 Version 3 revision 1 User’s Manual (Air Force Research Laboratory, Hanscom Air Force Base, Mass., 2003).
  13. C. Schueler and L. Woody, “Digital electro-optical imaging sensors,” Int. J. Imaging Syst. Technol. 4, 170–200 (1992). [CrossRef]
  14. Y. J. Kaufman, A. E. Wald, L. A. Remer, B.-C. Gao, R.-R. Li, and L. Flynn, “The MODIS 2.1 μm channel—correlation with visible reflectance for use in remote sensing of aerosol,” IEEE Trans. Geosci. Remote Sens. 35, 1286–1298 (1997). [CrossRef]
  15. S. Liang, H. Fallah-Adl, S. Kalluri, J. Jaja, Y. J. Kaufman, and J. R. G. Townshend, “An operational atmospheric correction algorithm for Landsat Thematic Mapper imagery over the land,” J. Geophys. Res. 102, 17173–17186 (1997). [CrossRef]
  16. R. Santer, D. Ramon, J. Vidot, and E. Dilligeard, “A surface reflectance model for aerosol remote sensing over land,” Int. J. Remote Sens. 28, 737–760 (2007). [CrossRef]
  17. Y. J. Kaufman and D. Tanre, “Atmospherically resistant vegetation index (ARVI) for EOS-MODIS,” IEEE Trans. Geosci. Remote Sens. 30, 261–270 (1992). [CrossRef]
  18. J. L. Deuze, F. M. Breon, C. Devaux, P. Goloub, M. Herman, B. Lafrance, F. Maignan, A. Marchand, F. Nadal, G. Perry, and D. Tanre, “Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements,” J. Geophys. Res. 106, 4913–4926 (2001). [CrossRef]
  19. Y. J. Kaufman and C. Sendra, “Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery,” Int. J. Remote Sens. 9, 1357–1381 (1988). [CrossRef]
  20. R. Frouin, P. Y. Deschamp, and P. Lecomte, “Determination from space of atmospheric total water vapor amounts by differential absorption near 940 nm: theory and airborne verification,” J. Appl. Meteorol. 29, 448–460 (1990). [CrossRef]
  21. V. Carrere and J. E. Conel, “Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm—Sensitivity analysis and applications to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data,” Remote Sens. Environ. 44, 179–204 (1993). [CrossRef]
  22. D. Schläpfer, C. C. Borel, J. Keller, and K. I. Itten, “Atmospheric precorrected differential absorption technique to retrieve columnar water vapor,” Remote Sens. Environ. 65, 353–366 (1998). [CrossRef]
  23. B.-C. Gao, A. F. H. Goetz, and W. J. Wiscombe, “Cirrus cloud detection from airborne imaging spectrometer data using the 1.38 μm water vapor band,” Geophys. Res. Lett. 20, 301–304 (1993). [CrossRef]
  24. B.-C. Gao, P. Yang, W. Han, R.-R. Li, and W. J. Wiscombe, “An algorithm using visible and 1.38 μm channels to retrieve cirrus cloud reflectances from aircraft and satellite data,” IEEE Trans. Geosci. Remote Sens. 40, 1659–1668 (2002). [CrossRef]
  25. F. Boochs, G. Kufer, G. Docker, and W. Kuhbauch, “Shape of the red edge as vitality indicator for plants,” Int. J. Remote Sens. 11, 1741–1753 (1990). [CrossRef]
  26. J. R. Miller, Wu Jiyou, M. G. Boyer, M. Belanger, and E. W. Hare, “Seasonal patterns in leaf reflectance red edge characteristics,” Int. J. Remote Sens. 12, 1509–1523 (1991). [CrossRef]
  27. D. N. H. Horler, J. Barber, J. P. Darch, D. C. Ferns, and A. R. Barringer, “Approaches to detection of geochemical stress in vegetation,” Adv. Space Res. 3, 175–179 (1983). [CrossRef]
  28. J. G. P. W. Clevers and C. Buker, “Feasibility of the red edge index for the detection of nitrogen deficiency,” in Proceedings of the Fifth International Colloquium, Physical Measurements and Signatures in Remote Sensing (1991), pp. 165–168.
  29. N. K. Patel, C. Patnaik, S. Dutta, A. M. Shekh, and A. J. Dave, “Study of crop growth parameters using airborne imaging spectrometer data,” Int. J. Remote Sens. 22, 2401–2411 (2001).
  30. S. Jacquemoud, W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-Tejada, G. P. Asner, C. François, and S. L. Ustin, “PROSPECT+SAIL models: a review of use for vegetation characterization,” Remote Sens. Environ. 113, S56–S66 (2009). [CrossRef]
  31. J. B. Féret, C. François, G. P. Asner, A. A. Gitelson, R. E. Martin, L. P. R. Bidel, S. L. Ustin, G. le Maire, and S. Jacquemoud, “PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments,” Remote Sens. Environ. 112, 3030–3043 (2008). [CrossRef]
  32. S. Jacquemoud, S. L. Ustin, J. Verdebout, G. Schmuck, G. Andreoli, and B. Hosgood, “Estimating leaf biochemistry using the PROSPECT leaf optical properties model,” Remote Sens. Environ. 56, 194–202 (1996). [CrossRef]
  33. W. Verhoef, “Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model,” Remote Sens. Environ. 16, 125–141 (1984). [CrossRef]
  34. J. R. Miller, E. W. Hare, and J. Wu, “Quantitative characterization of vegetation red edge reflectance: an inverted-Gaussian reflectance model,” Int. J. Remote Sens. 11, 1744–1773 (1990).
  35. J. R. Miller, W. Jiyou, M. G. Boyer, M. Belanger, and E. W. Hare, “Seasonal patterns in leaf reflectance red edge characteristics,” Int. J. Remote Sens. 12, 1509–1523 (1991). [CrossRef]
  36. P. Ceccato, N. Gobron, S. Flasse, B. Pinty, and S. Tarantola, “Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: theoretical approach,” Remote Sens. Environ. 82, 188–197 (2002). [CrossRef]

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