Miao FU. Improving the accuracy of NO2 concentrations derived from remote sensing using localized factors based on random forest algorithm[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(3): 258
- Journal of Atmospheric and Environmental Optics
- Vol. 18, Issue 3, 258 (2023)
Fig. 1. The comparison of the distributions of NO2 ground-level observed and satellite-derived concentrations. (a) Ground-level observed NO2 concentrations; (b) NASA satellite-derived NO2 concentrations
Fig. 2. The histograms of the cross validation concentrations from the three algorithms.(a) GWR_NO2; (b) MGWR_NO2; (c) RF_NO2
Fig. 3. Cross validation results of the GWR, MGWR and random forest. (a) GWR; (b) MGWR;(c) random forest; (d) random forest 2
Fig. 4. The comparison of the predicted concentrations from the four approaches for Tibet. (a) NASA_NO2; (b) GWR_NO2;(c) MGWR_NO2; (d) RF_NO2
Fig. 5. County-level NO2 concentrations predicted by the random forest algorithm. (a) Nationwide; (b) North China Plain;(c) Yangtze River Delta; (d) Guangdong Province; (e) Urumqi, Xinjiang
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Table 1. Statistical description of observed，NASA and the cross validation concentrations
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Table 2. The comparison of cross validation results of the models
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