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Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes

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Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes

dc.contributor.author	Mateo-Sanchis, Anna
dc.contributor.author	Muñoz Marí, Jordi
dc.contributor.author	Campos Taberner, Manuel
dc.contributor.author	García Haro, Francisco Javier
dc.contributor.author	Camps-Valls, Gustau
dc.date.accessioned	2020-12-12T10:48:19Z
dc.date.available	2020-12-13T05:45:07Z
dc.date.issued	2018	es_ES
dc.identifier.citation	A. Mateo-Sanchis, J. Muñoz-Marí, M. Campos-Taberner, J. García-Haro and G. Camps-Valls, "Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes," IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 4039-4042, doi: 10.1109/IGARSS.2018.8519254.	es_ES
dc.identifier.uri	https://hdl.handle.net/10550/76651
dc.description.abstract	In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.	es_ES
dc.description.abstract	In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.	en_US
dc.language.iso	en	es_ES
dc.relation.ispartofseries	2018 IEEE International Geoscience and Remote Sensing Symposium;
dc.subject	Time series	es_ES
dc.subject	remote sensing	es_ES
dc.subject	machine learning	es_ES
dc.subject	gaussian processes	es_ES
dc.title	Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes	es_ES
dc.type	lecture	es_ES
dc.subject.unesco	UNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIO	es_ES
dc.identifier.doi	10.1109/IGARSS.2018.8519254	es_ES
dc.accrualmethod	-	es_ES
dc.embargo.terms	0 days	es_ES
dc.relation.projectID	CICYT TIN2015-64210-R	es_ES