Abstract
Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. In this study, we developed an aerosol optical depth (AOD) retrieval algorithm for the GF-4 satellite. AOD retrieval was realized based on the pre-calculated surface reflectance database and 6S radiative transfer model. We customized the unique aerosol type according to the long time series aerosol parameters provided by the Aerosol Robotic Network (AERONET) site. The solar zenith angle, relative azimuth angle, and satellite zenith angle of the GF-4 panchromatic multispectral sensor image were calculated pixel-by-pixel. Our 1 km AOD retrievals were validated against AERONET Version 3 measurements and compared with MOD04 C6 AOD products at different resolutions. The results showed that our GF-4 AOD algorithm had a good robustness in both bright urban areas and dark rural areas. A total of 71.33% of the AOD retrievals fell within the expected errors of ±(0.05% + 20%); root-mean-square error (RMSE) and mean absolute error (MAE) were 0.922 and 0.122, respectively. The accuracy of GF-4 AOD in rural areas was slightly higher than that in urban areas. In comparison with MOD04 products, the accuracy of GF-4 AOD was much higher than that of MOD04 3 km and 10 km dark target AOD, but slightly worse than that of MOD04 10 km deep blue AOD. For different values of land surface reflectance (LSR), the accuracy of GF-4 AOD gradually deteriorated with an increase in the LSR. These results have theoretical and practical significance for aerosol research and can improve retrieval algorithms using the GF-4 satellite.
Highlights
Atmospheric aerosols are dispersed particles suspended in air, and include primary aerosols and secondary aerosols
In our Gaofen 4 (GF-4) Aerosol optical depth (AOD) algorithm, 71.33% of the points were within EE, 17.88% of the sample points were higher than the EE boundary, and relative mean bias (RMB) = 1.05. r reached 0.922, whereas root-mean-square error (RMSE) and mean absolute error (MAE) were 0.122 and 0.089, respectively
The results show that the new algorithm works well in both rural areas and bright urban areas, but the overall accuracy in urban areas (e.g., r = 0.895, RMSE = 0.143, and within EE = 69.01%) is slightly worse than that in rural areas (e.g., r = 0.943, RMSE = 0.097, and within EE = 73.84%)
Summary
Atmospheric aerosols are dispersed particles suspended in air, and include primary aerosols (emitted into the atmosphere directly in the form of particles) and secondary aerosols (converted from primary pollutants in the atmosphere). Aerosol particles affect the transmission of solar radiation through the scattering and absorption of electromagnetic waves, impacting the energy balance of environments on all scales; they are an influential factor in climate change as well [1,3,4,5]. Aerosol optical depth (AOD) is an important parameter used to quantitatively describe aerosols, represented by the integral of the extinction in solar radiation over a transport path due to aerosol absorption and scattering [6].
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