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| Titel |
Monitoring vegetation using DOAS satellite observations |
| VerfasserIn |
E. Eigemeier, S. Beirle, T. Marbach, U. Platt, T. Wagner |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250027087
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| Zusammenfassung |
| Vegetation-cycles are of general interest for many applications. Be it for harvest-predictions, global monitoring of climate-change or as input to atmospheric models. From novel spectrally resolving UV/vis satellite instruments (like GOME of SCIAMACHY) the spectral signatures of different types of vegetation can be identified and analysed. Although the spatial resolution of GOME and SCIAMACHY observations is much coarser than those of conventional satellite instruments for vegetation monitoring, our data sets on different vegetation types add new and useful information, not obtainable from other sources.
Common Vegetation Indices use the fact that the difference between Red and Near Infrared reflection is higher than in any other material on Earth’s surface. This gives a very high degree of confidence for vegetation-detection. The spectrally resolving data from GOME and SCIAMACHY provide the chance to concentrate on finer spectral features throughout the Red and Near Infrared spectrum. We look at these using a technique known as Differential Optical Absorption Spectroscopy (DOAS). Although originally developed to retrieve information on trace gases, it can also be used to gain information on vegetation. Another advantage is that this method automatically corrects for changes in the atmosphere. This renders the vegetation-information easily comparable over long time-spans.
In addition, high-frequency-structures from vegetation also effect the retrieval of tropospheric trace-gases and aerosols.
To optimize vegetation monitoring with DOAS we produce spectrally resolved reference spectra from different vegetation types. We investigate how well we will be able to distinguish vegetation types from space. This will also be valuable for monitoring global vegetation-cycles over long time spans. Preliminary results will be presented here. |
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