Aerosol Optical Depth Retrieval from MODIS Using a Physically Informed Machine Learning Framework

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we develop a physically informed random forest framework for global 1 km land AOD retrieval from MODIS. The framework integrates multispectral TOA reflectance, surface properties, observation geometry, meteorological conditions, topography, and physically informed aerosol–surface features. Long-term Aerosol Robotic Network (AERONET) observations from 2001 to 2017 were collocated with MODIS and ancillary datasets for model development and evaluation. Two physically informed features were introduced to improve retrieval robustness across diverse aerosol and surface conditions, including minimum AOD derived from long-term AERONET observations and time-series clear-sky reflectance (TSCR) in the blue, red, and shortwave-infrared bands derived using the 6S radiative-transfer model. Independent retrieval evaluation for 2013–2014 showed good agreement with AERONET observations, with R = 0.81, MAE = 0.063, RMSE = 0.096, and 74.93% of matched samples falling within the MODIS land expected-error envelope, although increasing underestimation was observed at high aerosol loading (AOD > 1). The proposed retrievals also showed better agreement with AERONET than the MOD04 Dark Target and Deep Blue products. These results demonstrate the value of incorporating physically interpretable aerosol-background and surface-reflectance information into data-driven retrievals for AOD over land surfaces.