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Estimation of Regional Ground-Level $PM_{2.5}$ Concentrations Directly from Satellite Top-of-Atmosphere Reflectance Using A Hybrid Learning Model
| Content Provider | MDPI |
|---|---|
| Author | Feng, Yu Fan, Shurui Xia, Kewen Wang, Li |
| Copyright Year | 2022 |
| Description | The accurate prediction of $PM_{2.5}$ concentrations is important for environmental protection. The accuracy of the commonly used prediction methods is not high; so, this paper proposes a $PM_{2.5}$ concentration prediction method based on a hybrid learning model. The Top-of-Atmosphere Reflectance (TOAR), $PM_{2.5}$ data decomposed by wavelets, and meteorological data were used as input features to build an integrated prediction model using random forest and LightGBM, which was applied to $PM_{2.5}$ concentration prediction in the Beijing–Tianjin–Hebei region. The practical application showed that the proposed method using TOAR, incorporating wavelet decomposition with meteorological element data, had an improvement of 0.06 in the $R^{2}$ of the model accuracy and a reduction of 2.93 and 1.14 in the root mean square error (RMSE) and mean absolute error (MAE), respectively, over the model using Aerosol Optical Depth (AOD). Our model had a prediction accuracy of $R^{2}$ of 0.91, which was better than the other models. We used this model to estimate and analyze the variation in $PM_{2.5}$ concentrations in the Beijing–Tianjin–Hebei region, and the results were the same as the actual $PM_{2.5}$ concentration distribution trend. Obviously, the proposed model has a high prediction accuracy and can avoid the errors caused by the limitations of the AOD inversion method. |
| Starting Page | 2714 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14112714 |
| Journal | Remote Sensing |
| Issue Number | 11 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-06 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Pm2.5 Estimation Hybrid Learning Model Top-of-atmosphere Reflectance Beijing–tianjin–hebei Region |
| Content Type | Text |
| Resource Type | Article |
