# A Perspective on Gaussian Processes for Earth Observation

arXiv: https://arxiv.org/abs/2007.01238 · HTML: https://mlsanity.com/paper/2007.01238/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward](https://mlsanity.com/paper/2305.08413/) (2023)
- [Probabilistic Machine Learning for Noisy Labels in Earth Observation](https://mlsanity.com/paper/2504.03478/) (2025)
- [Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion](https://mlsanity.com/paper/2104.10638/) (2021)
- [Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments](https://mlsanity.com/paper/2106.00842/) (2021)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [Inference over radiative transfer models using variational and expectation maximization methods](https://mlsanity.com/paper/2204.03346/) (2022)
- [On the Generalization of Representation Uncertainty in Earth Observation](https://mlsanity.com/paper/2503.07082/) (2025)
- [Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions](https://mlsanity.com/paper/2104.08134/) (2021)

## Abstract (arXiv)

Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error propagation. Despite great advances in forward and inverse modelling, GP models still have to face important challenges that are revised in this perspective paper. GP models should evolve towards data-driven physics-aware models that respect signal characteristics, be consistent with elementary laws of physics, and move from pure regression to observational causal inference.