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| Titel |
High-resolution land surface modeling utilizing remote sensing parameters and the Noah UCM: a case study in the Los Angeles Basin |
| VerfasserIn |
P. Vahmani, T. S. Hogue |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 12 ; Nr. 18, no. 12 (2014-12-03), S.4791-4806 |
| Datensatznummer |
250120541
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| Publikation (Nr.) |
 copernicus.org/hess-18-4791-2014.pdf |
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| Zusammenfassung |
| In the current work we investigate the utility of remote-sensing-based
surface parameters in the Noah UCM (urban canopy model) over a highly
developed urban area. Landsat and fused Landsat–MODIS data are utilized to
generate high-resolution (30 m) monthly spatial maps of green vegetation
fraction (GVF), impervious surface area (ISA), albedo, leaf area index
(LAI), and emissivity in the Los Angeles metropolitan area. The gridded
remotely sensed parameter data sets are directly substituted for the
land-use/lookup-table-based values in the Noah-UCM modeling framework. Model
performance in reproducing ET (evapotranspiration) and LST (land surface
temperature) fields is evaluated utilizing Landsat-based LST and ET
estimates from CIMIS (California Irrigation Management Information System)
stations as well as in situ measurements. Our assessment shows that the
large deviations between the spatial distributions and seasonal fluctuations
of the default and measured parameter sets lead to significant errors in the
model predictions of monthly ET fields (RMSE = 22.06 mm month−1). Results
indicate that implemented satellite-derived parameter maps, particularly
GVF, enhance the capability of the Noah UCM to reproduce observed ET patterns over
vegetated areas in the urban domains (RMSE = 11.77 mm month−1). GVF plays the
most significant role in reproducing the observed ET fields, likely due to
the interaction with other parameters in the model. Our analysis also shows
that remotely sensed GVF and ISA improve the model's capability to predict the
LST differences between fully vegetated pixels and highly developed areas. |
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