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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
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| Mateo-Sanchis, Anna; Muñoz Marí, Jordi; Campos Taberner, Manuel; García Haro, Francisco Javier; Camps-Valls, Gustau | |
| Aquest document és un/a conferència, creat/da en: 2018 | |
| 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.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. | |
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