Acta Optica Sinica, Volume. 41, Issue 4, 0401002(2021)
Retrieval of Regional Aerosol Optical Depth Using Deep Learning
Figures & Tables(7)
Fig. 3. Scatter density map of AOD predication value and AERONET site observation value
Fig. 4. Comparison between model predication results and measurement results at different sites
Table 1. Landsat8 OLI data
View tableTable 1. Landsat8 OLI data
Band No. Spectral range /μm Spatial resolution /m B1 (Coastal) 0.43-0.45 30 B2 (Blue) 0.45-0.51 30 B3 (Green) 0.53-0.59 30 B4 (Red) 0.64-0.67 30 B5 (NIR) 0.85-0.88 30 B6 (SWIR 1) 1.57-1.65 30 B7 (SWIR 2) 2.11-2.29 30 B8 (Pan) 0.50-0.68 15 B9 (Cirrus) 1.36-1.38 30
Table 2. Error statistics
View tableTable 2. Error statistics
Position R RM MA EE Europe 0.45 0.083 0.059 73.12 North America 0.53 0.077 0.049 79.36 South America 0.79 0.069 0.048 80.05 Africa 0.70 0.152 0.098 64.77 Oceania 0.73 0.053 0.041 84.58 Asia 0.75 0.142 0.100 61.58
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Tianchen Liang, Lin Sun, Yongji Wang. Retrieval of Regional Aerosol Optical Depth Using Deep Learning[J]. Acta Optica Sinica, 2021, 41(4): 0401002
Paper Information
Category: Atmospheric Optics and Oceanic Optics
Received: Aug. 4, 2020
Accepted: Sep. 25, 2020
Published Online: Feb. 25, 2021
The Author Email: Lin Sun (sunlin6@126.com)
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