Bayesian Latent Variable Co-kriging Model in Remote Sensing for Observations with Quality Flagged

TL;DR

This paper introduces a Bayesian latent variable co-kriging model that effectively integrates quality flags in remote sensing data analysis, significantly enhancing prediction accuracy for large datasets like AIRS temperature observations.

Contribution

The paper develops a novel separable Gaussian process co-kriging framework with an efficient MCMC algorithm that incorporates data quality flags into remote sensing data modeling.

Findings

Incorporating quality flags improves prediction accuracy.

The model efficiently handles large datasets via covariance decomposition.

Application to AIRS data demonstrates substantial performance gains.

Abstract

Remote sensing data products often include quality flags that inform users whether the associated observations are of good, acceptable or unreliable qualities. However, such information on data fidelity is not considered in remote sensing data analyses. Motivated by observations from the Atmospheric Infrared Sounder (AIRS) instrument on board NASA's Aqua satellite, we propose a latent variable co-kriging model with separable Gaussian processes to analyze large quality-flagged remote sensing data sets together with their associated quality information. We augment the posterior distribution by an imputation mechanism to decompose large covariance matrices into separate computationally efficient components taking advantage of their input structure. Within the augmented posterior, we develop a Markov chain Monte Carlo (MCMC) procedure that mostly consists of direct simulations from conditional distributions. In addition, we propose a computationally efficient recursive prediction procedure. We apply the proposed method to air temperature data from the AIRS instrument. We show that incorporating quality flag information in our proposed model substantially improves the prediction performance compared to models that do not account for quality flags.