Correction of atmospheric influence on hyperspectral EO-1 Hyperion data for the red edge position estimation

1Lyalko, VI, 1Sakhatsky, OI, 1Shportiuk, ZM, 1Sibirtseva, ON
1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine
Kosm. nauka tehnol. 2009, 15 ;(3):32-41
Publication Language: Ukrainian
We investigated the influence of atmospheric correction of the Hyperion data using dark object substraction on the Red Edge Position (REP) of spectral reflectance. The comparison of REP-images which were constructed without atmospheric correction and after it was made with the application of images classification to estimate the improvement of accuracy of land cover mapping with the use of the Red Edge Position. It is found that the atmospheric correction of satellite data shows the increase of the contrasts of REP values and the improvement of land cover mapping accuracy by classification.
Keywords: atmospheric correction, hyperspectral data, spectral reflectance
1. Lyalko V. I., Sakhatskyi O. I., Shportyuk Z. M., Sybirtseva O. M. Using red-earth indexes and water indexes for hyperspectral data EO-1 "Hyperion" for ground-based classification. In: 7th Ukrainian Conference on Space Research: Abstracts, NCUIKS, Evpatoria, September 3-8, 2007, 176 (In-t kosmich. doslidzhen' NANU—NKAU, Kyiv, 2007) [in Ukrainian].
2. Lyalko V. I., Shportyuk Z. M., Sakhatskyi O. I., Sybirtseva O. M. The use of red edge indices and water indices from hyperspectral data from EO-1 Hyperion for land cover classification. Kosm. nauka tehnol., 14 (3), 55—68 (2008) [in Ukrainian].
3. Baret F., Jacquemoud S., Guyot G., Leprieur C. Modeled Analysis of the Biophysical Nature of Spectral Shifts and Comparison with Information Content of Broad Bands. Remote Sens. Environ., 41 (2/3), 133—142 (1992).
4. Buschmann C. Fernerkundung von Pflanzen. Naturwissenschaften, 80, 439—453 (1993).
5. Buschmann C., Nagel E. Reflexionsspektren von Blatern und Nadeln als Basis fur die physiologische Beurteilung von Baumschaden. PEF-Report Nr. 90, 165 s. (Kernforschungszentrum, Karlsruhe, 1992).
6. Buschmann C., Nagel E. In vivo spectroscopy and internal optics of leaves as basis for the remote sensing of vegetation. Int. J. Remote Sens., 14, 711— 722 (1993).
7. Chavez P. S. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ., 24, 459— 479 (1993).
8. Cheng A. F., Dominique D. L. Radiative transfer models for light scattering from planetary surfaces. J. Geophys. Res., 105, 9477—9482 (2000).
9. Clevers J., Bartholomeus H., Mücher C., de Wit A. Land cover classification with the Medium Resolution Imaging Spectrometer (MERIS). In: New Strategies for European Remote Sensing, Ed. by Oluic, 687—694 (Millpress, Rotterdam, 2005).
10. Collins W., Chang S.-H., Raines G., et al. Airborne Biogeophysical Mapping of Hidden Mineral Deposits. Economic Geol., 4 (78), 737— 749 (1983).
11. Feng J., Rivard B, Sánchez – Azofeifa A. The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping. Remote Sens. Environ., 85, 221—231 (2003).
12. Goetz A., Rock B., Rowan L. Remote Sensing for Exploration: An Overview. Economic Geol., 78 (4), 573—590 (1983).
13. Griffin M. K., Hsu S. M., Burke H. K., et. al. Examples of EO-1 Hyperion Data Analysis. Lincoln Laboratory J., 15 (2), 271—296 (2005).
14. Horler D. N. H., Dockray M., Barber J. The red edge of plant leaf reflectance. Int. J. Remote Sens., 4, 273—288 (1983).
15. Lin Li, Susan L. Ustin, Mui Lay. Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico. Remote Sens. Environ., 94, 1—16 (2004).
16. Moran M. S., Jackson P. D., Slater P. N., Teillet P. M. Evalution of Simplified Procedures for Retrieval of Land Surface Reflectance Factor from Satellite Sensor Output. Remote Sens. Environ., 41, 169—184 (1992).
17. Pearlman, J. S, Barry P. S, Segal C. C., et al. Hyperion, a Space Borne Imaging Spectrometer. IEEE Trans. Geosci. Remote Sens., 41 (6), 1160— 1173 (2003).
18. Pons X., Solé-Sugrañes L. A Simple Radiometric Correction Model to Improve Automatic Mapping of Vegetation from Multispectral Satellite Data. Remote Sens. Environ., 48 (2), 191—203 (1994).
19. Rock B. N., Hoshizaki T., Miller J. R. Comparison of the in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sens. Environ., 24, 109—127 (1988).
20. Shportyuk Z. M., Sakhatsky A. I., Sibirtseva O. N. Land cover classification in Ukrainian Carpathians using the MERIS Terrestrial Chlorophyl Index and Red Edge Position from Envisat Meris data. In: Remote Sensing: From Pixels to Processes: Proc. of Mid-Term Symposium ISPRS, Enschede, the Netherlands (8—11 May 2006).
21. Yang C., Vidal A. Combination of digital elevation models with SPOT-1 HRV multispectral imagery for reflectance factor mapping. Remote Sens. Environ., 32, 35—45 (1990).
22. Zarco-Tejada P. J., Miller J. R., et. al. Optical indices as bioindicators of forest condition from hyperspectral CASI data. In: Proc. 19th Symp. European Association of Remote Sens. Laboratories (EARSeL), Valladolid (Spain) (1999).

23. Zarco-Tejada P. J., Miller J. R. Land cover mapping of BOREAS using red edge spectral parameters from CASI imagery. J. Geophys. Res., 104D (22), 27921—27933 (1999).