Improvement of spatial-temporal resolution of aerosol profile by using multi-source satellite data over the Persian Gulf

Abstract

Remote Sensing observations have enormous advantages in aerosol studies since aerosols' space and time variation. MODIS and CALIOP are two independent instruments with different design principles that provide aerosol optical depth (AOD) retrievals and scan the same points on the Earth's surface within a 2-min interval. Due to predefined aerosol models and fixed vertical profiles in the MODIS algorithm and AOD CALIOP resolution, MODIS and CALIOP cannot give suitable spatial-temporal coverage in related studies. This paper proposes a method based on Bayesian networks to retrieve the AODs by the synergy of CALIOP and MODIS in two vertical layers, 1.5 and 3 km. We applied the Bayesian network for three days in 2018 over the Persian Gulf. The overall analyses reveal that estimated AOD by the seasonal networks correlates with obtained retrieval CALIOP AODs. The correlation values, 0.94 and 0.84, are obtained for the first layer in the summer and winter. These values for the second layer are 0.88 and 0.82. The observed differences in the estimated AOD with the actual measured AOD values and the overall correlation results demonstrate that the proposed networks are sufficient to provide accurate AODs in the two, 1.5, and 3 km vertical layers. According to the experimental results, the layering MAIAC AOD product becomes more suitable for monitoring and studying aerosol phenomena by applying the proposed networks.