Regional estimates of ground level Aerosol using Satellite Remote Sensing and Machine-Learning

The ground-level aerosols are known to have harmful impact on people's health. The Moderate Imaging resolution Spectroradiometer (MODIS) sensors onboard aqua and terra satellites retrieve aerosol optical depth (AOD) at various bands. The comparison between the AOD measured from the satellite MODIS instruments and the ground-based Aerosol Robotic Network (AERONET) system at 550 nm shows that there is a bias between the two data products. In this study we explore the factors that can delineate these extrema, and/or explain them statistically. We use the MODIS 3 km and 10 km resolution AOD products, and develop a machine-learning framework to compare the Aqua and Terra MODIS-retrieved AODs with the ground- based AERONET observations. The analysis uses several measured variables such as the MODIS AOD, surface type, land use, etc. as input in order to train a neural network in regression mode with a special emphasis on biases observed over non vegetative urban surfaces. The result is the estimator of the bias-corrected estimates of AOD. This research is part of our goal to provide air quality information, with special focus on the northeast region of the USA, which can also be useful for developing regional-level decision support tools.