Inference over radiative transfer models using variational and expectation maximization methods
This work addresses the problem of improving parameter estimation from satellite observations for Earth system monitoring, representing an incremental advancement in computational methods for radiative transfer modeling.
The paper tackled the challenge of performing inference over radiative transfer models (RTMs), which are nonlinear, non-differentiable, and computationally costly, by introducing two computational techniques—a variational autoencoder approach and a Monte Carlo Expectation Maximization (MCEM) scheme—to infer joint distributions of biophysical parameters, with numerical comparisons in synthetic simulations and the real PROSAIL model.
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.