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| Titel |
Accurate LAI retrieval method based on PROBA/CHRIS data |
| VerfasserIn |
W. J. Fan, X. R. Xu, X. C. Liu, B. Y. Yan, Y. K. Cui |
| 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 ; 14, no. 8 ; Nr. 14, no. 8 (2010-08-10), S.1499-1507 |
| Datensatznummer |
250012396
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| Publikation (Nr.) |
 copernicus.org/hess-14-1499-2010.pdf |
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| Zusammenfassung |
| Leaf area index (LAI) is one of the key structural variables in terrestrial
vegetation ecosystems. Remote sensing offers an opportunity to accurately
derive LAI at regional scales. The anisotropy of canopy reflectance,
variations in background characteristics, and variability in atmospheric
conditions constitute three factors that can strongly constrain the accuracy
of retrieved LAI. Based on a hybrid canopy reflectance model, a new
hyperspectral directional second derivative method (DSD) is proposed in this
paper. This method can estimate LAI accurately through analyzing the canopy
anisotropy. The effect of the background can also be effectively removed.
With the aid of a widely-accepted atmospheric model, the influence of
atmospheric conditions can be minimized as well. Thus the inversion
precision and the dynamic range can be markedly improved, which has been
proved by numerical simulations. As the derivative method is very sensitive
to random noise, we put forward an innovative filtering approach, by which
the data can be de-noised in spectral and spatial dimensions synchronously.
It shows that the filtering method can remove random noise effectively;
therefore, the method can be applied to hyperspectral images. The study
region was situated in Zhangye, Gansu Province, China; hyperspectral and
multi-angular images of the study region were acquired via the Compact
High-Resolution Imaging Spectrometer/Project for On-Board Autonomy
(CHRIS/PROBA), on 4 June 2008. After the pre-processing
procedures, the DSD method was applied, and the retrieved LAI was validated
by ground reference data at 11 sites. Results show that the new LAI
inversion method is accurate and effective with the aid of the innovative
filtering method. |
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