Evaluating the accuracy of Gaussian approximations in VSWIR imaging spectroscopy retrievals

by   Kelvin M. Leung, et al.

The joint retrieval of surface reflectances and atmospheric parameters in VSWIR imaging spectroscopy is a computationally challenging high-dimensional problem. Using NASA's Surface Biology and Geology mission as the motivational context, the uncertainty associated with the retrievals is crucial for further application of the retrieved results for environmental applications. Although Markov chain Monte Carlo (MCMC) is a Bayesian method ideal for uncertainty quantification, the full-dimensional implementation of MCMC for the retrieval is computationally intractable. In this work, we developed a block Metropolis MCMC algorithm for the high-dimensional VSWIR surface reflectance retrieval that leverages the structure of the forward radiative transfer model to enable tractable fully Bayesian computation. We use the posterior distribution from this MCMC algorithm to assess the limitations of optimal estimation, the state-of-the-art Bayesian algorithm in operational retrievals which is more computationally efficient but uses a Gaussian approximation to characterize the posterior. Analyzing the differences in the posterior computed by each method, the MCMC algorithm was shown to give more physically sensible results and reveals the non-Gaussian structure of the posterior, specifically in the atmospheric aerosol optical depth parameter and the low-wavelength surface reflectances.


IID Sampling from Doubly Intractable Distributions

Intractable posterior distributions of parameters with intractable norma...

Surface transfer coefficients estimation for heat conduction problem using the Bayesian framework

This work deals with an inverse two-dimensional nonlinear heat conductio...

Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods

Uncertainty quantification is a critical missing component in radio inte...

Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model

The performance of land surface models (LSMs) strongly depends on their ...

A Closest Point Proposal for MCMC-based Probabilistic Surface Registration

In this paper, we propose a non-rigid surface registration algorithm tha...

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions

Discovering interaction effects on a response of interest is a fundament...

Please sign up or login with your details

Forgot password? Click here to reset