Machine learning based bias correction for modis aerosol optical depth in Beijing

Mengjuan Wang, Meng Fan, Zhibao Wang, Liangfu Chen, Lu Bai, Yuanlin Chen, Mei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Aerosol refers to suspensions of small solid and liquid particles in the atmosphere. Although the content of aerosol in the atmosphere is small, it plays a crucial role in atmospheric and the climatic processes, making it essential to monitor. In areas with poor aerosol characteristics, satellite-based aerosol optical depth (AOD) values often differ from ground-based AOD values measured by instruments like AERONET. The use of 3km DT, 10km DT and 10km DTB algorithms in Beijing area has led to significant overestimation of AOD values, highlighting the need for improvement. This paper proposes the use of machine learning techniques, specifically support vector regression (SVR) and artificial neural network (ANN), to correct the deviation of AOD data. Our approach leverages ground-based monitoring data, meteorological reanalysis data and satellite products to train the models. Our results show that the ANN model outperforms the SVR model achieving R2, RMSE and Slope values of 0.88, 0.12 and 0.97, respectively, when applied to nearly two decades of data from 2001 to 2019. This study significantly improves the accuracy of MODIS AOD values, reducing overestimation and bringing them closer to ground-based AOD values measured by AERONET. Our findings have important applications in climate research and environmental monitoring.

Original languageEnglish
Title of host publicationProceedings of the 39th International Symposium on Remote Sensing of Environment, ISRSE-39
EditorsO. Altan, F. Sunar, D. Klein
PublisherCopernicus Gesellschaft mbH
Number of pages8
Publication statusPublished - 21 Apr 2023
Externally publishedYes
Event39th International Symposium on Remote Sensing of Environment 2023 - Antalya, Turkey
Duration: 24 Apr 202328 Apr 2023

Publication series

NameInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISSN (Print)1682-1750
ISSN (Electronic)2194-9034


Conference39th International Symposium on Remote Sensing of Environment 2023
Abbreviated titleISRSE-39


  • Aerosol optical depth
  • artificial neural network
  • bias correction
  • machine learning
  • support vector regression


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