Mapping PM2.5 concentration from the top-of-atmosphere reflectance of Himawari-8 via an ensemble stacking model

Direct estimation of PM2.5 concentration using the satellite top-of-atmosphere (TOA) reflectance has become a research hotspot for satellite sensing monitoring of PM2.5. However, the optimal feature parameter selection in current studies on PM2.5 estimation based on TOA reflectance is still unclear, and the model construction is mostly based on a single model, and the accuracy is limited. Therefore, this study used various combinations of full-spectrum data, angle information and meteorological data from the Himawari-8 satellite containing TOA reflectance to explore the optimal feature parameter selection for estimating PM2.5, and removed the band data with multicollinearity in the full-spectrum data by calculating the variance inflation factor (VIF) of the full-spectrum satellite data. Concurrently, based on the ensemble stacking algorithm, a PM2.5 ensemble stacking estimatiion model (PMISES) with a double-layer structure and multiple machine learning models was constructed. The results indicated that the PM2.5 concentration estimation by satellite full-spectrun data after the multicollinearity test combined with angle information and meteorological data could improve the model accuracy. Compared with using satellite full-spectral data alone, the model R2 increased by 0.1 and RMSE decreased by 7 μg/m3. The combination was determined to be the optimal parameter combination for estimating PM2.5. The PMISES model developed in this study demonstrates superior accuracy when compared to other standalone models (RF, LightGBM, XGBoost). The R2 reaches 0.87 and the RMSE is 17.09 μg/m3. The model was utilized for the estimation and analysis of PM2.5 concentration in the Beijing-Tianjin-Hebei (BTH) region, and the outcomes exhibited consistent concordance with ground-based monitoring data. Obviously, the optimal PM2.5 concentration estimation method proposed in this study is reliable and can provide a valuable reference for PM2.5 concentration monitoring in BTH region.