# Inference over radiative transfer models using variational and expectation maximization methods

arXiv: https://arxiv.org/abs/2204.03346 · HTML: https://mlsanity.com/paper/2204.03346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Machine Learning of Linear Differential Equations using Gaussian Processes](https://mlsanity.com/paper/1701.02440/) (2017)
- [Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks](https://mlsanity.com/paper/1701.04722/) (2017)
- [Marginal likelihood computation for model selection and hypothesis testing: an extensive review](https://mlsanity.com/paper/2005.08334/) (2020)
- [Layered Adaptive Importance Sampling](https://mlsanity.com/paper/1505.04732/) (2015)
- [Physics-Aware Gaussian Processes in Remote Sensing](https://mlsanity.com/paper/2012.07986/) (2020)
- [Active emulation of computer codes with Gaussian processes -- Application to remote sensing](https://mlsanity.com/paper/1912.06552/) (2019)
- [A Perspective on Gaussian Processes for Earth Observation](https://mlsanity.com/paper/2007.01238/) (2020)
- [Joint Gaussian Processes for Biophysical Parameter Retrieval](https://mlsanity.com/paper/1711.05197/) (2017)
- [Metropolis Sampling](https://mlsanity.com/paper/1704.04629/) (2017)
- [Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization](https://mlsanity.com/paper/1712.03353/) (2017)
- [Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models](https://mlsanity.com/paper/2012.04598/) (2020)

## Built on by (most cited first, 1 of 1)

- [Neural Methods for Amortized Inference](https://mlsanity.com/paper/2404.12484/) (2024)

## Abstract (arXiv)

Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to model and understand the Earth system, as well as to estimate the parameters that describe the status of the Earth from satellite observations by inverse modeling. However, performing inference over such simulators is a challenging problem. RTMs are nonlinear, non-differentiable and computationally costly codes, which adds a high level of difficulty in inference. In this paper, we introduce two computational techniques to infer not only point estimates of biophysical parameters but also their joint distribution. One of them is based on a variational autoencoder approach and the second one is based on a Monte Carlo Expectation Maximization (MCEM) scheme. We compare and discuss benefits and drawbacks of each approach. We also provide numerical comparisons in synthetic simulations and the real PROSAIL model, a popular RTM that combines land vegetation leaf and canopy modeling. We analyze the performance of the two approaches for modeling and inferring the distribution of three key biophysical parameters for quantifying the terrestrial biosphere.