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Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
- Source :
- 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 4039-4042
- Publication Year :
- 2020
-
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.<br />Comment: 4 pages, 3 figures
Details
- Database :
- arXiv
- Journal :
- 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 4039-4042
- Publication Type :
- Report
- Accession number :
- edsarx.2012.05912
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/IGARSS.2018.8519254