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| Titel |
Comparison of Landsat and MODIS for assessing surface properties of snow and ice |
| VerfasserIn |
Stef Lhermitte, Nicole P. M. van Lipzig |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250096512
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| Publikation (Nr.) |
 EGU/EGU2014-12018.pdf |
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| Zusammenfassung |
| Assessment of the spatio-temporal variations in snow and ice properties provides valuable
input for a variety of climatological, hydrological, glaciological applications ranging from
energy and mass budget calculations to distributed snowmelt modelling. Within this context a
variety of retrieval methods has been developed to assess surface properties from
multi-spectral Landsat and MODIS data. These methods range from spectral index
calculations and unmixing methods to combined remote sensing and radiative transfer
approaches.
This study provides a quantitative analysis of the trade-offs between the state-of-the-art
retrieval methodologies applied on Landsat and MODIS data. Within this context,
spatio-temporal patterns of surface properties (e.g., snow cover fraction, albedo, grain size,
impurity load, ponding melt water, snow/ice classification) are derived from Landsat and
MODIS reflectance data over two study areas covering parts of the Greenland Ice Sheet and
the Chilean Andes from 2000 to present. The retrieved properties are subsequently compared
and validated based on reference in-situ measurements.
Analysis of the differences in derived surface properties from Landsat and MODIS
reveals the importance of understanding the spatial and temporal scales at which variations
occur. Large spatial variability within a MODIS pixel complicates the performance of
retrieval methods for MODIS time series, especially for surface properties not related to snow
cover fractions. Large temporal variability, on the other hand, constrains the validity of time
series of Landsat retrievals and also has a large impact on the use of multi-day composite
MODIS data. |
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