Spatiotemporal estimation of daily PM₂.₅ and PM₁₀ in Thailand using MODIS and VIIRS aerosol optical depth

Accurate estimation of particulate matter (PM) concentrations is essential for air quality management, environmental monitoring, and public health research, particularly in regions with limited ground-based monitoring networks. This study evaluated satellite-derived Aerosol Optical Depth (AOD) products from MODIS and VIIRS for estimating daily PM₂.₅ and PM₁₀ concentrations across Thailand during 2012–2025. Daily observations from air quality monitoring stations were integrated with AOD, digital elevation model (DEM), land surface temperature (LST), normalized difference vegetation index (NDVI), meteorological variables, week of year (WOY), and year. Random Forest (RF) models were developed and evaluated using training, validation, and five-fold cross-validation datasets. Log-linear regression analysis showed significant positive relationships between AOD and PM concentrations (p < 0.05), with MODIS AOD explaining 10.8% and 9.1% of the variability in PM₂.₅ and PM₁₀, respectively, compared with 4.8% and 4.1% for VIIRS AOD. RF models substantially improved predictive performance. Validation R2 values reached 82% and 81% for PM₂.₅ and 77% and 76% for PM₁₀ using MODIS and VIIRS AOD, respectively. WOY, relative humidity, AOD, and topographic factors were the most influential predictors. Overall, MODIS and VIIRS provided comparable and reliable estimates of PM₂.₅ and PM₁₀, supporting satellite-based air quality monitoring in Thailand.