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| Titel |
Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations |
| VerfasserIn |
J. M. Krijger, P. Tol, L. G. Istomina, C. Schlundt, H. Schrijver, I. Aben |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 4, no. 10 ; Nr. 4, no. 10 (2011-10-19), S.2213-2224 |
| Datensatznummer |
250002119
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| Publikation (Nr.) |
 copernicus.org/amt-4-2213-2011.pdf |
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| Zusammenfassung |
| In the ultra-violet, visible and near infra-red wavelength range the presence
of clouds can strongly affect the satellite-based passive remote sensing
observation of constituents in the troposphere, because clouds effectively
shield the lower part of the atmosphere. Therefore, cloud detection
algorithms are of crucial importance in satellite remote sensing. However,
the detection of clouds over snow/ice surfaces is particularly difficult in
the visible wavelengths as both clouds an snow/ice are both white and highly
reflective. The SCIAMACHY Polarisation Measurement Devices (PMD)
Identification of Clouds and Ice/snow method (SPICI) uses the SCIAMACHY
measurements in the wavelength range between 450 nm and 1.6 μm to
make a distinction between clouds and ice/snow covered surfaces, specifically
developed to identify cloud-free SCIAMACHY observations. For this purpose the
on-board SCIAMACHY PMDs are used because they provide higher spatial
resolution compared to the main spectrometer measurements. In this paper we
expand on the original SPICI algorithm (Krijger et al., 2005a) to also adequately
detect clouds over snow-covered forests which is inherently difficult because
of the similar spectral characteristics. Furthermore the SCIAMACHY
measurements suffer from degradation with time. This must be corrected for
adequate performance of SPICI over the full SCIAMACHY time range. Such a
correction is described here. Finally the performance of the new SPICI
algorithm is compared with various other datasets, such as from FRESCO,
MICROS and AATSR, focusing on the algorithm improvements. |
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