Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods
The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing chal...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/6/655 |
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| author | Huifang Wang Min Wang Pan Jiang Fanshu Ma Yanhu Gao Xinchen Gu Qingzu Luan |
| author_facet | Huifang Wang Min Wang Pan Jiang Fanshu Ma Yanhu Gao Xinchen Gu Qingzu Luan |
| author_sort | Huifang Wang |
| collection | DOAJ |
| description | The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. This study aims to develop a high-precision method to fill spatial AOD missing values and generate daily full-coverage AOD products for the Beijing–Tianjin–Hebei region in 2021 by integrating multi-dimensional data, including meteorological models, multi-source remote sensing, surface conditions, and nighttime light parameters, and applying machine learning methods. A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R<sup>2</sup>) of 0.93. Seasonal evaluation further indicated that the model’s simulation was best in winter. Variable importance analysis identified relative humidity (RH) as the most critical factor influencing model results. The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing–Tianjin–Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. This research lays the foundation for establishing a long-term, 1 km resolution, daily spatially continuous AOD product for the Beijing–Tianjin–Hebei region and beyond, providing more robust data support for addressing regional and larger-scale environmental challenges. |
| format | Article |
| id | doaj-art-01d4fd567e484321a9e5607bcbe71ca5 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-01d4fd567e484321a9e5607bcbe71ca52025-08-20T03:26:10ZengMDPI AGAtmosphere2073-44332025-05-0116665510.3390/atmos16060655Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning MethodsHuifang Wang0Min Wang1Pan Jiang2Fanshu Ma3Yanhu Gao4Xinchen Gu5Qingzu Luan6Beijng Municipal Climate Center, Beijing 100089, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, ChinaBeijng Municipal Climate Center, Beijing 100089, ChinaBeijng Municipal Climate Center, Beijing 100089, ChinaSchool of Architectural Engineering, Tianjin University, Tianjin 300072, ChinaBeijng Municipal Climate Center, Beijing 100089, ChinaThe satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. This study aims to develop a high-precision method to fill spatial AOD missing values and generate daily full-coverage AOD products for the Beijing–Tianjin–Hebei region in 2021 by integrating multi-dimensional data, including meteorological models, multi-source remote sensing, surface conditions, and nighttime light parameters, and applying machine learning methods. A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R<sup>2</sup>) of 0.93. Seasonal evaluation further indicated that the model’s simulation was best in winter. Variable importance analysis identified relative humidity (RH) as the most critical factor influencing model results. The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing–Tianjin–Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. This research lays the foundation for establishing a long-term, 1 km resolution, daily spatially continuous AOD product for the Beijing–Tianjin–Hebei region and beyond, providing more robust data support for addressing regional and larger-scale environmental challenges.https://www.mdpi.com/2073-4433/16/6/655FY-3D/4AAODHi-8AHIModisHGALSSmachine learning algorithms |
| spellingShingle | Huifang Wang Min Wang Pan Jiang Fanshu Ma Yanhu Gao Xinchen Gu Qingzu Luan Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods Atmosphere FY-3D/4A AODHi-8 AHI Modis HGALSS machine learning algorithms |
| title | Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods |
| title_full | Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods |
| title_fullStr | Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods |
| title_full_unstemmed | Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods |
| title_short | Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods |
| title_sort | research on time series interpolation and reconstruction of multi source remote sensing aod product data using machine learning methods |
| topic | FY-3D/4A AODHi-8 AHI Modis HGALSS machine learning algorithms |
| url | https://www.mdpi.com/2073-4433/16/6/655 |
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