4.7 Article

Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region

Journal

REMOTE SENSING
Volume 15, Issue 8, Pages -

Publisher

MDPI
DOI: 10.3390/rs15082172

Keywords

aerosol optical depth; sentinel-2; Beijing; high-resolution

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A window-based AOD retrieval algorithm is proposed to ignore unreliable/non-Lambertian pixels and retrieve AOD from Sentinel-2 images in Beijing region. The algorithm shows good agreement with ground-based measured AOD and exhibits comparable spatial distributions to MAIAC algorithm AOD products. The very little noise and high spatial continuity of retrieval AOD suggest its potential for improving AOD quality.
The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality.

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