A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique

Xinyu Yu, Man Sing Wong (Corresponding Author), Majid Nazeer, Zhengqiang Li, Yin Tung Coco Kwok

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The ubiquitous missing values in the satellite-derived aerosol optical depth (AOD) products have always been a challenge for spatial and temporal analysis. To address this concern, we propose a novel data-driven model to attain the full-coverage daily AOD dataset with 0.01° spatial resolution in the Guangdong-Hong Kong-Macao Greater Bay Area (hereafter GBA) from 2010 to 2021. Firstly, the missing values of top-of-atmosphere (TOA) reflectance and surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) caused by cloud contamination, were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF). Subsequently, a new model was developed for the estimation of AOD which integrates the geographical and temporal encodings into the Light Gradient Boosting Machine (LightGBM) with the inputs of reconstructed TOA/surface reflectance and other influencing variables like meteorological and geographical factors. Results showed that the derived gap-free AOD dataset outperforms the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product and agrees well with the ground-based observations, achieving an index of agreement (IOA) of 0.88, R of 0.84, root mean square error (RMSE) of 0.19 and mean absolute error (MAE) of 0.14. Moreover, the derived AOD dataset presents consistent temporal patterns with in-situ measurements, but with more spatial details than other gapless AOD datasets, i.e., Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, this study has developed a promising meteorological framework for the estimation of full-coverage AOD, which can also be applied over other regions. The derived long-term full-coverage daily AOD dataset can also be used for other applications related to climate change, air quality and ecosystem assessment.
Original languageEnglish
Article number120216
JournalAtmospheric Environment
Volume318
Issue number120216
DOIs
Publication statusPublished - 1 Feb 2024

Keywords

  • Aerosol optical depth
  • Data interpolating empirical orthogonal functions
  • Full coverage
  • Light gradient boosting machine

ASJC Scopus subject areas

  • General Environmental Science
  • Atmospheric Science

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