6533b86ffe1ef96bd12cdd05
RESEARCH PRODUCT
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
Jordi Muñoz-maríAnna Mateo-sanchisGustau Camps-vallsJavier Garcia-haroManuel Campos-tabernersubject
FOS: Computer and information sciencesComputer Science - Machine Learning010504 meteorology & atmospheric sciences0211 other engineering and technologiesFOS: Physical sciencesMachine Learning (stat.ML)02 engineering and technology01 natural sciencesQuantitative Biology - Quantitative MethodsMachine Learning (cs.LG)Data modelingsymbols.namesakeStatistics - Machine LearningApplied mathematicsTime seriesGaussian processQuantitative Methods (q-bio.QM)021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsSeries (mathematics)Linear modelProbability and statisticsMissing dataFOS: Biological sciencesPhysics - Data Analysis Statistics and ProbabilitysymbolsFocus (optics)Data Analysis Statistics and Probability (physics.data-an)description
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-07-01 |