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Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran

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Abstract

One of the techniques for estimating the surface particle concentration with a diameter of fewer than 2.5 micrometers (PM2.5) is using aerosol optical depth (AOD) products. Different AOD products are retrieved from various satellite sensors, like MODIS and VIIRS, by various algorithms, such as Deep Blue and Dark Target. Therefore, they don’t have the same accuracy and spatial resolution. Additionally, the weakness of algorithms in AOD retrieval reduces the spatial coverage of products, particularly in cloudy or snowy areas. Consequently, for the first time, the present study investigated the possibility of fusing AOD products from observations of MODIS and VIIRS sensors retrieved by Deep Blue and Dark Target algorithms to estimate PM2.5 more accurately. For this purpose, AOD products were fused by machine learning algorithms using different fusion strategies at two levels: the data level and the decision level. First, the performance of various machine learning algorithms for estimating PM2.5 using AOD data was evaluated. After that, the XGBoost algorithm was selected as the base model for the proposed fusion strategies. Then, AOD products were fused. The fusion results showed that the estimated PM2.5 accuracy at the data level in all three metrics, RMSE, MAE, and R2, was improved (R2= 0.64, MAE= 9.71\(\frac {\mu g}{m^{3}} \), RMSE= 13.51\(\frac {\mu g}{m^{3}} \)). Despite the simplicity and lower computational cost of the data level fusion method, the spatial coverage did not improve considerably due to eliminating poor quality data through the fusion process. Afterward, the fusion of products at the decision level was followed in eleven scenarios. In this way, the best result was obtained by fusing Deep Blue products of MODIS and VIIRS sensors (R2= 0.81, MAE= 7.38\( \frac {\mu g}{m^{3}} \), RMSE= 10.08\( \frac {\mu g}{m^{3}} \)). Moreover, in this scenario, the spatial coverage was improved from 77% to 84%. In addition, the results indicated the significance of the optimal selection of AOD products for fusion to obtain highly accurate PM2.5 estimations.

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Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

The author wishes to express his gratitude to everyone who contributed to this study, particularly Tehran’s AQCC for the ground PM2.5 measurements, NASA for the MAIAC data, and ECMWF for the meteorological data.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Faculty of Civil Engineering and Transportation, University of Isfahan, Azadi square, Isfahan, 8174673441, Iran

    Ali Mirzaei, Hossein Bagheri & Mehran Sattari

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  1. Ali Mirzaei
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  2. Hossein Bagheri
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  3. Mehran Sattari
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Contributions

Ali Mirzaei: Methodology, Investigation, Data curation, Software, Validation, Writing- Original draft preparation, Visualization. Hossein Bagheri: Conceptualization, Methodology, Software, Investigation, Supervision, Validation, Writing- Reviewing and Editing. Mehran Sattari: Conceptualization, Writing- Reviewing and Editing.

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Correspondence to Hossein Bagheri.

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Communicated by: H. Babaie

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Mirzaei, A., Bagheri, H. & Sattari, M. Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran. Earth Sci Inform 16, 753–771 (2023). https://doi.org/10.1007/s12145-022-00912-6

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