Estimating the influence of spectral and radiometric calibration uncertainties on EnMAP data products-examples for ground reflectance retrieval and vegetation indices

Remote Sensing

Volumn 7, Issue 8, 2015, Pages 10689-10714

  Bachmann, Martin ^a   Makarau, Aliaksei ^a   Segl, Karl ^b   Richter, Rudolf ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b GFZ GERMAN RESEARCH CENTRE FOR GEOSCIENCES   (Germany)

Author keywords

Atmospheric correction; EnMAP; Hyperspectral; Imaging spectroscopy; Radiometric stability; Spectral stability; Uncertainty

Indexed keywords

CALIBRATION; RADIOMETRY; REFLECTION; STABILITY; STATISTICAL TESTS; VEGETATION;

ATMOSPHERIC CORRECTIONS; ENMAP; HYPERSPECTRAL; IMAGING SPECTROSCOPY; SPECTRAL STABILITY; UNCERTAINTY;

UNCERTAINTY ANALYSIS;

EID: 84977563786     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70810689     Document Type: Article

References (34)

1. QA4EO Task Team. A Quality Assurance Framework for Earth Observation: Principles. Available online: http://qa4eo.org/docs/QA4EO_Principles_v4.0.pdf (accessed on 28 May 2015).
2. Fox, N. A Guide to Expression of Uncertainty of Measurements. Available online: http://qa4eo.org/docs/QA4EO-QAEO-GEN-DQK-006_v4.0.pdf (accessed on 28 May 2015).
3. Evaluation of Measurement Data-Guide to the Expression of Uncertainty in Measurement. Available online: http://www.bim.government.bg/0/3D3F3380D3DB8BE2C22579000029F199/$FILE/JCGM_100_2008_E_GUM.pdf (accessed on 2 June 2015).
4. Bachmann, M.; Adar, S.; Ben-Dor, E.; Biesemans, J.; Briottet, X.; Grant, M.; Hanus, J.; Holzwarth, S.; Hueni, A.; Kneubuehler, M.; et al. Towards agreed data quality layers for airborne hyperspectral imagery. In Proceedings of the 7th EARSeL-SIG-IS, Edinburgh, UK, 11-13 April 2011.
5. Beekhuizen, J.; Bachmann, M.; Ben-Dor, J.; Biesemans, M.; Grant, G.; Heuvelink,G.; Hueni, A.; Kneubuehler, M.; de Miguel, E.; Pimstein, A.; et al. Report on Full Error Propagation Concept; European Comission EUFAR Office: Toulouse, France, 2009.
6. Woolliams, E.; Hueni, A.; Gorrono, J. Intermediate Uncertainty Analysis for Earth Observation (Instrument Calibration). NPL Training Course Textbook. Available online: http://www.emceoc.org/documents/uaeo-int-trg-course.pdf (accessed on 28 May 2015).
7. Schaepman, M.; Jehle, M.; Hueni, A.; D'Odorico, P.; Damm, A.; Weyermann, J.; Schneider, F.; Laurent, V.; Popp, C.; Seidel, F.; et al. Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX). Remote Sens. Environ. 2014, 158, 207-219.
8. Kerr, G.; Fischer, C.; Reulke, R. A blind-deconvolution approach for a data-driven estimation of image reflectance variability. IEEE Trans. Geosci. Remote Sens. 2015, submitted.
9. Segl, K.; Guanter, L.; Kuester, T.; Roessner, R.; Kaufmann, H.; Sang, B.; Mogulsky, V.; Hofer, S. EeteS-The EnMAP end-to-end simulation tool. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 522-530.
10. Storch, T.; Bachmann, M.; Honold, H.-P.; Kaufmann, H.; Krawczyk, H.; Müller, R.; Sang, B.; Schneider, M.; Segl, K.; Chlebek, C. EnMAP data product standards. In Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS), Quebec, QC, Canada, 13-18 July 2014.
11. Richter, R.; Schläpfer, D. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction. Int. J. Remote Sens. 2002, 23, 2631-2649.
12. Stuffler, T.; Kaufmann, H.; Hofer, S.; Foerster, K.-P.; Schreier, G.; Mueller, A.; Eckardt, A.; Bach, H.; Penne, B.; Benz, U.; et al. The EnMAP hyperspectral imager-An advanced optical payload for future applications in earth observation programmes. Acta Astronaut. 2007, 61, 115-120.
13. Storch, T.; Bachmann, M.; Eberle, S.; Habermeyer, M.; Makasy, C.; de Miguel, A.; Mühle, H.; Müller, R. EnMAP ground segment design: An overview and its hyperspectral image processing chain. In Earth Observation of Global Changes (EOGC); Springer-Verlag: Berlin, Germany; Heidelberg, Germany, 2013; pp. 49-62.
14. Guanter, L.; Kaufmann, H.; Segl, K.; Foerster, S.; Rogass, C.; Chabrillat, S.; Kuester, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; et al. The Environmental Mapping and Analysis Program (EnMAP) spaceborne imaging spectroscopy mission for Earth observation. Remote Sens. 2015, submitted.
15. Green, R.; Pavri, B.; Chrien, T. On-orbit radiometric and spectral calibration characteristics of EO-1 Hyperion derived with an underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1194-1203.
16. Cocks, T.; Jensen, T.; Steward, A.; Wilson, I.; Schields, T. The HyMap airborne hyperspectral sensor: The system, calibration and performance. In Proceedings of the First EARSeL Workshop on Imaging Spectroscopy, Zürich, Switzerland, 6-8 October 1998.
17. Bachmann, M.; Habermayer, M.; Holzwarth, S.; Richter, R.; Müller, A. Including quality measures in an automated processing chain for airborne hyperspectral data. In Proceedings of the 5th EARSeL Workshop on Imaging Spectroscopy, Bruges, Belgium, 23-25 April 2007.
18. Guanter, L.; Richter, R.; Moreno, J. Spectral calibration of hyperspectral imagery using atmospheric absorption features. Appl. Opt. 2006, 45, 2360-2370.
19. Richter, R.; Schlapfer, D. ATCOR User Manual. Available online: www.rese.ch/pdf/atcor3_manual.pdf (accessed on 28 May 2015).
20. Berk, A.; Bernstein, L.; Anderson, G.; Acharay, P.; Robertson, D.; Chetwynd, J.; Adler-Golden, S. MODTRAN cloud and multiple scattering upgrades with application to AVIRIS. Remote Sens. Environ. 1998, 65, 367-375.
21. Richter, R. Correction of satellite imagery over mountainous terrain. Appl. Opt. 1998, 37, 4004-4015.
22. Kaufman, Y.; Wald, A.; Remer, L.; Gao, B.-C.; Li, R.-R.; Flynn, L. The MODIS 2.1 μm channel-Correlation with visible reflectance for use in remote sensing of aerosol. IEEE Trans. Geosci. Remote Sens. 1997, 35, 1286-1298.
23. Schläpfer, D.; Borel, C.; Keller, J.; Itten, K. Atmospheric precorrected differential absorption technique to retrieve columnar water vapor. Remote Sens. Environ. 1998, 65, 353-366.
24. Richter, R.; Schläpfer, D. Considerations on water vapor and surface reflectance retrievals for a spaceborne imaging spectrometer. IEEE Trans. Geosci. Remote Sens. 2008, 46, 1958-1966.
25. Richter, R.; Wang, X.; Bachmann, M.; Schläpfer, D. Correction of cirrus effects in Sentinel-2 type of imagery. Int. J. Remote Sens. 2011, 32, 2931-2941.
26. Richter, R.; Schläpfer, D.; Müller, A. Operational atmospheric correction for imaging spectrometers accounting for the smile effect. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1772-1780.
27. Segl, K.; Guanter, L.; Kaufmann, H. Simulation of spatial sensor characteristics in the context of the EnMAP hyperspectral mission. IEEE Trans. Geosci. Remote Sens. 2010, 48, 3046-3054.
28. Guanter, L.; Segl, K.; Kaufmann, H. Simulation of optical remote sensing scenes with application to the EnMAP hyperspectral mission. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2340-2351.
29. European Space Agency. ESA Earth Observation Campaigns Data. Avaliable online: https://earth.esa.int/web/guest/campaigns (accessed on 28 May 2015).
30. Verhoef, W.; Bach, H. Coupled soil-Leaf-Canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sens. Environ. 2007, 109, 166-182.
31. Press, W.; Teukolsky, S.; Vetterling, W.; Flannery, B. Numerical Recipes in C. The Art of Scientific Computing, 2nd ed.; Cambridge University Press: New York, NY, USA, 1992.
32. Garbulsky, M.; Peñuelas, J.; Gamon, J.; Inoue, Y.; Filella, I. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis. Remote Sens. Environ. 2011, 115, 281-297.
33. Suárez, L.; Zarco-Tejada, P.; Sepulcre-Cantó, G.; Pérez-Priego, O.; Miller, J.; Jiménez-Muñoz, J.; Sobrino, J. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sens. Environ. 2008, 112, 560-575.
34. Mõttus, M.; Takala, T.; Stenberg, P.; Knyazikhin, Y.; Yang, B.; Nilson, T. Diffuse sky radiation influences the relationship between canopy PRI and shadow fraction. ISPRS J. Photogramm. Remote Sens. 2015, 105, 54-60.