Name
Title | ||
|---|---|---|
Improving Spatial Coverage Of Satellite Aerosol Classification Using A Random Forest Model |
Abstract | ||
|---|---|---|
The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13071268 | REMOTE SENSING |
Keywords | DocType | Volume |
aerosol classification, aerosol remote sensing, space-borne remote sensing, aerosol type, machine learning, TROPOMI, MODIS, AERONET, AOD | Journal | 13 |
Issue | Citations | PageRank |
7 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wonei Choi | 1 | 1 | 2.81 |
| Hanlim Lee | 2 | 13 | 6.22 |
| Daewon Kim | 3 | 0 | 0.68 |
| Serin Kim | 4 | 0 | 0.34 |