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

arXiv: https://arxiv.org/abs/2012.05912 · HTML: https://mlsanity.com/paper/2012.05912/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Probabilistic Machine Learning for Noisy Labels in Earth Observation](https://mlsanity.com/paper/2504.03478/) (2025)
- [On the Generalization of Representation Uncertainty in Earth Observation](https://mlsanity.com/paper/2503.07082/) (2025)

## Abstract (arXiv)

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.