MODIS aerosol optical thickness retrieval using deep belief network

Ke Li; YanFang Ming; Chen Jia; GuangZhe Wang

doi:10.1117/12.2667429

10 February 2023 MODIS aerosol optical thickness retrieval using deep belief network

Ke Li, YanFang Ming, Chen Jia, GuangZhe Wang

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522T (2023) https://doi.org/10.1117/12.2667429
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Aerosols are important pollutants affecting the quality of atmospheric environment. With the development of space remote sensing technology, using satellite remote sensing to retrieve aerosol has become an important method. In order to solve the ill-posed problem of aerosol retrieval and to mine the aerosol information of satellite signals to a greater extent, this paper proposes a method based on deep confidence network to realize the aerosol retrieval of MODIS sensor. AERONET station data and satellite data in the Beijing-Tianjin-Hebei region of China were selected, and the sample dataset was constructed according to the reasonable spatio-temporal matching principle. By setting relevant parameters, the model is trained and tested, and the optimal network model is found. The aerosol measured data from independent AERONET sites were selected for accuracy evaluation, and the accuracy reached 97.78%. Compared with the traditional aerosol inversion algorithm, the proposed method achieves high precision and high efficiency of aerosol inversion, and improves the stability and spatio-temporal adaptability of aerosol inversion.

Citation Download Citation

Ke Li, YanFang Ming, Chen Jia, and GuangZhe Wang "MODIS aerosol optical thickness retrieval using deep belief network", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522T (10 February 2023); https://doi.org/10.1117/12.2667429

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KEYWORDS

Aerosols

Education and training

Atmospheric modeling

Data modeling

Reflectivity

Satellites

MODIS

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