Daily Aerosol Optical Depth from Multiple Earth Observation Data Fusion

The presented methodology aims to create a daily Aerosol Optical Depth (AOD) fusion product by integrating observations and forecasts from various EO data sources. The data used for this purpose are from the Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) sensors on Sentinel−3, the Second Generation Global Imager (SGLI) on the Global Change Observation Mission−Climate (GCOM−C), and the forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Leveraging the strengths of each dataset, the developed algorithm adapts to different formats and resolutions, providing a unified and higher−resolution AOD dataset.The Sentinel−3 AOD product ensures high resolution (4.5 km), the GCOM−C product ensures dataset accuracy, and CAMS forecasts offer predictive insights. The algorithm employs a mathematical averaging technique for coincident pixels, facilitating precise AOD estimates in overlapping regions, while a mosaic technique seamlessly integrates non−coincident areas.This fused dataset enhances spatiotemporal coverage, contributing to a more in−depth understanding of atmospheric composition variations. Emphasizing the importance of complete AOD data, the methodology is versatile and has been validated against Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products, achieving a Pearson coefficient of 0.87.The product was tested on the Italian Po River Basin area for the year 2021. This region represents an area of great interest for the study of air quality and atmospheric dynamics due to its geographic complexity and the significant impact of anthropogenic activities.The final product is an improvement over the reliable SYN−AOD product from Sentinel−3, providing daily data obtainable in less than an hour through an automated algorithm.