Porównanie wyników korekcji atmosferycznej danych satelitarnych CHRIS/Proba przeprowadzonych w oprogramowaniach BEAM/Visat oraz ATCOR

• Autorzy: Gawuć L. , Osińska-Skotak K.

Warianty tytułu

EN

Intercomparison of BEAM/Visat and ATCOR atmospheric correction methods performed on CHRIS/Proba satellite data

• Języki publikacji: PL

Abstrakty

EN

A comparison of output of two absolute atmospheric correction methods (ATCOR by R. Richter, 1996, and an algorithm by L. Guanter et al., 2005, implemented in the BEAM/Visat framework) is presented. Analyses are based on satellite data acquired by CHRIS (Compact High Resolution Imaging Spectrometer) sensor onboard the PROBA (Project for On-Board Autonomy) satellite. For comparison, a set of in situ spectral measurements obtained by the Norwegian NIVA Institute was taken as reference data. The area of study was the Vistula Lagoon in Northern Poland. All analyses presented here are based on comparison of results of atmospheric correction methods with in situ reference data. Alterations between ground and satellite spectral measurements can be caused by changes of humidity or solar zenith angle, as well as fluctuations of water masses, aerosols and air masses, all of which phenomena occur with time passage. In order to minimize the influence of this element, a set of simultaneous ground and satellite measurements was analyzed. Observations were collected on the same day, 18th August 2008. The best atmospheric correction was obtained in ATCOR with a ground model calibration, and the mean relative difference in spectral reflectance between the results obtained with this method and the reference data was 0,18%. The drawback of this method is that it requires results from in situ spectral measurements to reinforce the reflectance derivation, while such data is usually unavailable. Hence, only methods independent of ancillary data are treated as authoritative. In this case, the output of two methods – ATCOR without ground model calibration and an algorithm by L. Guanter et al., (2005) implemented in BEAM/Visat framework – were compared against the reference data. The comparison yields 2,30% and 2,10% reflectance mean difference between ATCOR, an algorithm by L. Guanter et al., (2005) and the reference data, correspondingly. This leads to conclusion that an algorithm by L. Guanter et al., (2005), provided better results in our case.

Słowa kluczowe

PL

korekcja atmosferyczna; ATCOR; CHRIS/PROBA; BEAM/Visat; dane superspektralne

EN

atmospheric correction; ATCOR; CHRIS/PROBA; BEAM/Visat; superspectral data

• Wydawca: Polskie Towarzystwo Geograficzne, Oddział Teledetekcji i Geoinformatyki
• Czasopismo: Teledetekcja Środowiska
• Rocznik: 2012
• Tom: T. 47
• Strony: 33--42
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

Gawuć L.

• Wydział Inżynierii Środowiska Politechniki Warszawskiej

autor

Osińska-Skotak K.

• Zakład Fotogrametrii, Teledetekcji i SIP na Wydziale Geodezji i Kartografii i Politechniki Warszawskiej

Bibliografia

• 1. Cracknell A. P., Hayes L., 2007, Introduction to remote sensing, second edition, Boca Raton, CRC Press, Taylor&Francis Group, str. 159–202
• 2. Cutter M., 2004, Review of aspects associated with the CHRIS calibration, Proceedings of 2nd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Włochy
• 3. Cutter M., Johns L., 2005, CHRIS data products – latest issue, Proceedings of 3rd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Włochy
• 4. Garcia J.C., Moreno J., 2004, Removal of noises in CHRIS/Proba images: Application to the SPARC Campaign data, Proceedings of 2nd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Włochy
• 5. Głowienka E., 2008, Porównanie metod korekcji atmosferycznej dla danych z sensorów hiperspektralnych, Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 18, str. 121–130
• 6. Gómez-Chova L., Alonso L., Guanter L., Camps-Valls G., Calpe J., Moreno J., 2005, Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images, Applied Optics, vol. 47, no. 28
• 7. Guanter L., Alonso L., Moreno J., 2005, A Method for the Surface Reflectance Retrieval From PROBA/CHRIS Data Over Land: Application to ESA SPARC Campaigns, IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 12
• 8. Jensen R., Introductory Digital Image Processing – A Remote Sensing Perspective, Prentice Hall, Upper Saddle River, New Jersey, second edition
• 9. Osińska-Skotak K., 2007, Znaczenie korekcji atmosferycznej w procesie przetwarzania zdjęć satelitarnych, Archiwum Fotogrametrii, Kartografii i Teledetekcji, vol. 17b, str. 577–590
• 10. Richter R., 1996, Spatially adaptive fast atmospheric correction algorithm, International Journal of Remote Sensing, vol. 17, str. 56–64
• 11. Sharma A.R., Badarinath K.V.S., Roy P.S., 2009, Comparison of ground reflectance measurement with satellite derived atmospherically corrected reflectance: A case study over semi-arid landscape, Advances in Space Research, vol. 43.l str. 56–64
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• 13. Tachiiri K., 2005, Calculating NDVI for NOAA/AVHRR data after atmospheric correction for extensive images using 6S code: A case study in the Marsabit District, Kenya, ISPRS Journal of Photogrammetry & Remote Sensing, vol. 59, str. 103–114
• 14. Wu J, Wang D, Bauer M. E., 2005, Image-based atmospheric correction of QuickBird imagery of Minnesota Cropland, Remote Sensing of Environment, vol. 99. str. 315–325