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CemOA
2014
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Prise en compte de structures spatiales pour l'assimilation variationnelle de données de télédétection. Exemple sur un modèle simple de croissance de végétation

Authors: Lauvernet, C.; Le Dimet, F.X.; Baret, F.;

Prise en compte de structures spatiales pour l'assimilation variationnelle de données de télédétection. Exemple sur un modèle simple de croissance de végétation

Abstract

Information contained in time series of image data should be explicitly exploited in data assimilation methods instead of operating over single pixels. This study proposes to adapt a variational data assimilation method of LAI (Leaf Area Index) images in a crop model. The method assumes that the parameters are governed spatially at some levels (cultivar, field, and pixel), while some of them are assumed to be stable temporally over the whole image. Such constraints help at reducing the size of the inverse problem, transforming the usual assimilation scheme into simultaneous pixel patterns. DA with constraints is applied to the semi-mechanistic model BONSAÏ and evaluated by twin experiments both on the quality of LAI prediction and on parameter estimates. Sensitivity to the observations frequency is also evaluated. The constraints improve the method's robustness and estimates when the number of observations available decreases, compared to the conventional method. / En assimilation de données, une série temporelle de données-image devrait être traitée explicitement pour en extraire toute l'information. Cette étude propose d'adapter une méthode d'assimilation variationnelle d'images de LAI (Leaf Area Index) dans un modèle de végétation, afin d'intégrer l'information liée à l'aspect spatial des données. Pour cela, on considère que les paramètres sont contrôlés spatialement à certains niveaux: variété, parcelle, pixel, ou stables temporellement sur l'ensemble de l'image. Ces contraintes réduisent la taille du problème inverse, en transformant le schéma d'assimilation habituel à des ensembles de pixels simultanés. La méthode est appliquée sur le modèle semi-mécaniste BONSAÏ et évaluée sur la qualité de prédiction du LAI et d'estimation des paramètres d'entrée par expériences jumelles, ainsi que sur sa sensibilité à la fréquence des observations. Les contraintes spatio-temporelles améliorent la robustesse et les estimations lorsque la quantité d'observations disponibles diminue, par rapport à la méthode classique, où chaque pixel.date est considéré indépendamment des autres.

Keywords

ASSIMILATION DE DONNEES VARIATIONNELLE, COUVERT VEGETAL, MODELISATION DU COUVERT VEGETAL, CANOPY, METHODE VARIATIONNELLE, CONTRAINTES SPATIALES, TELEDETECTION, REMOTE SENSING

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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