Bayesian spatio-temporal disaggregation modeling using a diffusion-SPDE approach: a case study of Aerosol Optical Depth in India

TL;DR

This paper introduces a Bayesian spatio-temporal disaggregation model using a diffusion-SPDE approach to enhance high-resolution estimates of Aerosol Optical Depth in India, aiding climate and health research.

Contribution

It develops a novel diffusion-SPDE based Bayesian model for spatio-temporal disaggregation of AOD, integrating covariates and improving resolution from coarse satellite data.

Findings

Enhanced spatial resolution from 0.75° to 0.25°

Improved temporal resolution from 3 hours to 1 hour

Effective in nowcasting AOD in India

Abstract

Accurate estimation of Aerosol Optical Depth (AOD) is crucial for understanding climate change and its impacts on public health, as aerosols are a measure of air quality conditions. AOD is usually retrieved from satellite imagery at coarse spatial and temporal resolutions. However, producing high-resolution AOD estimates in both space and time can better support evidence-based policies and interventions. We propose a spatio-temporal disaggregation model that assumes a latent spatio--temporal continuous Gaussian process observed through aggregated measurements. The model links discrete observations to the continuous domain and accommodates covariates to improve explanatory power and interpretability. The approach employs Gaussian processes with separable or non-separable covariance structures derived from a diffusion-based spatio-temporal stochastic partial differential equation (SPDE). Bayesian inference is conducted using the INLA-SPDE framework for computational efficiency. Simulation studies and an application to nowcasting AOD at 550 nm in India demonstrate the model's effectiveness, improving spatial resolution from 0.75{\deg} to 0.25{\deg} and temporal resolution from 3 hours to 1 hour.