Aerosol Retrieval Algorithm for Sentinel-2 Images Over Complex Urban Areas

Abstract

High-resolution aerosol retrieval is of great significance for understanding the impact of aerosols on air pollution and climate change. In this study, an algorithm for aerosol retrieval at a spatial resolution of 60 m over complex urban areas is developed using Sentinel-2 images. The proposed algorithm has two assumptions: 1) the blue–red surface reflectance ratio does not change temporally in a single season and 2) surface reflectance over bright areas is invariant over three months. Then, the aerosol optical depth (AOD) is retrieved from the surface reflectance correlations with a combination of temporal signatures over the vegetated areas and bright areas. The aerosol robotic network (AERONET) measurements in Beijing and its surrounding from 2017 to 2019 are collected and used to validate the retrieved 60-m Sentinel-2 AODs; 77% of the retrieved Sentinel-2 AODs fall within the expected error (EE), and the correlation coefficient is of 0.927. MODerate resolution Imaging Spectroradiometer (MODIS) AOD products at 1- and 10-km resolutions (MCD19A2 and MOD04_L2, respectively) are acquired to compare with the retrieved Sentinel-2 AODs. Comparison results show that the retrieved Sentinel-2 AODs are superior to the MOD04_L2 dark target (DT) and MCD19A2 AODs, and slightly better than the MOD04_L2 deep blue (DB), and DT and DB combined (DTBC) AODs. The validation and comparison results indicate that the proposed algorithm is able to describe aerosol distributions at high resolution continuously. However, further work is needed to apply the proposed algorithm on a global scale.