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| Titel |
Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements |
| VerfasserIn |
J. Timmermans, W. Verhoef, C. Tol, Z. Su |
| 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 ; 13, no. 7 ; Nr. 13, no. 7 (2009-07-21), S.1249-1260 |
| Datensatznummer |
250011943
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| Publikation (Nr.) |
 copernicus.org/hess-13-1249-2009.pdf |
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| Zusammenfassung |
| Evapotranspiration is usually estimated in remote sensing from single
temperature value representing both soil and vegetation. This surface
temperature is an aggregate over multiple canopy components. The temperature
of the individual components can differ significantly, introducing errors in
the evapotranspiration estimations. The temperature aggregate has a high
level of directionality. An inversion method is presented in this paper to
retrieve four canopy component temperatures from directional brightness
temperatures. The Bayesian method uses both a priori information and sensor
characteristics to solve the ill-posed inversion problem. The method is
tested using two case studies: 1) a sensitivity analysis, using a large
forward simulated dataset, and 2) in a reality study, using two datasets of
two field campaigns. The results of the sensitivity analysis show that the
Bayesian approach is able to retrieve the four component temperatures from
directional brightness temperatures with good success rates using
multi-directional sensors (Srspectra≈0.3, Srgonio≈0.3,
and SrAATSR≈0.5),
and no improvement using
mono-angular sensors (Sr≈1). The results of the experimental
study show that the approach gives good results for high LAI values
(RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K,
RMSEbarley=0.67 K); but for low LAI values the results were
unsatisfactory (RMSEyoung maize=2.85 K). This discrepancy was found
to originate from the presence of the metallic construction of the setup. As
these disturbances, were only present for two crops and were not present in
the sensitivity analysis, which had a low LAI, it is concluded that using
masked thermal images will eliminate this discrepancy. |
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