Meiling Xing
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Meiling Xing has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bo Li
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
1 shared publication - Jinyu Yang
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
1 shared publication - Tong Li
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
1 shared publication - Wenhao Zhang
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
1 shared publication - Xiaoyang Chen
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
1 shared publication - Zhihua Han
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
1 shared publication
Latest Publications
- Open Access
- 8 December 2025
- Published by MDPI AG in Atmosphere
- Vol. 16 (12) , 1385
Estimation of High-Temporal-Resolution PM2.5 Concentration from 2019 to 2023 Using an Interpretable Deep Learning Model
The FY-4A satellite represents a new generation of geostationary platforms, providing high-temporal-resolution observations over China. However, challenges remain in effectively leveraging the FY-4A satellite data for high-temporal-resolution PM2.5 concentration estimation, particularly regarding the unclear key parameters required for accurate estimation and the limited interpretability of models. This study utilizes an interpretable deep learning framework that integrates FY-4A Top-of-Atmosphere (TOA) reflectance data, meteorological variables, and auxiliary data to estimate surface high-temporal-resolution PM2.5 concentrations from 2019 to 2023. A multicollinearity test was applied to optimize feature selection, while the SHapley Additive exPlanations (SHAP) method was used to enhance model interpretability. The results indicate that parameters such as TOA02, TOA03, TOA04, and boundary layer height (BLH) significantly influence model performance across years. The model demonstrates strong predictive ability in the Beijing–Tianjin–Hebei (BTH) region, achieving an average R2 of 0.83. Root mean square error (RMSE) values remained below 15 µg/m3, aligning well with ground-based monitoring data. These findings demonstrate that combining high temporal satellite data with interpretable deep learning provides a reliable approach for long-term, high-temporal-resolution PM2.5 monitoring in regions.
- 8 December 2025
- Published by MDPI AG in Atmosphere
- Vol. 16 (12) , 1385
Open AccessEstimation of High-Temporal-Resolution PM2.5 Concentration from 2019 to 2023 Using an Interpretable Deep Learning Model
The FY-4A satellite represents a new generation of geostationary platforms, providing high-temporal-resolution observations over China. However, challenges remain in effectively leveraging the FY-4A satellite data for high-temporal-resolution PM2.5 concentration estimation, particularly regarding the unclear key parameters required for accurate estimation and the limited interpretability of models. This study utilizes an interpretable deep learning framework that integrates FY-4A Top-of-Atmosphere (TOA) reflectance data, meteorological variables, and auxiliary data to estimate surface high-temporal-resolution PM2.5 concentrations from 2019 to 2023. A multicollinearity test was applied to optimize feature selection, while the SHapley Additive exPlanations (SHAP) method was used to enhance model interpretability. The results indicate that parameters such as TOA02, TOA03, TOA04, and boundary layer height (BLH) significantly influence model performance across years. The model demonstrates strong predictive ability in the Beijing–Tianjin–Hebei (BTH) region, achieving an average R2 of 0.83. Root mean square error (RMSE) values remained below 15 µg/m3, aligning well with ground-based monitoring data. These findings demonstrate that combining high temporal satellite data with interpretable deep learning provides a reliable approach for long-term, high-temporal-resolution PM2.5 monitoring in regions.