Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula
Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps tha...
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MDPI AG
2024-11-01
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| author | Youjeong Youn Seoyeon Kim Seung Hee Kim Yangwon Lee |
| author_facet | Youjeong Youn Seoyeon Kim Seung Hee Kim Yangwon Lee |
| author_sort | Youjeong Youn |
| collection | DOAJ |
| description | Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea. |
| format | Article |
| id | doaj-art-16fdf15beaa843bcba07e3db270281e6 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-16fdf15beaa843bcba07e3db270281e62025-08-20T02:50:40ZengMDPI AGRemote Sensing2072-42922024-11-011623440010.3390/rs16234400Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean PeninsulaYoujeong Youn0Seoyeon Kim1Seung Hee Kim2Yangwon Lee3Major of Geomatics Engineering, Division of Earth Environmental System Sciences, Pukyong National University, Busan 48513, Republic of KoreaMajor of Geomatics Engineering, Division of Earth Environmental System Sciences, Pukyong National University, Busan 48513, Republic of KoreaInstitute for Earth, Computing, Human and Observing (ECHO), Chapman University, Orange, CA 92866, USAMajor of Geomatics Engineering, Division of Earth Environmental System Sciences, Pukyong National University, Busan 48513, Republic of KoreaGiven the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea.https://www.mdpi.com/2072-4292/16/23/4400aerosol optical depth (AOD)Himawari-8gap-fillingmachine learning |
| spellingShingle | Youjeong Youn Seoyeon Kim Seung Hee Kim Yangwon Lee Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula Remote Sensing aerosol optical depth (AOD) Himawari-8 gap-filling machine learning |
| title | Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula |
| title_full | Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula |
| title_fullStr | Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula |
| title_full_unstemmed | Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula |
| title_short | Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula |
| title_sort | spatial gap filling of himawari 8 hourly aod products using machine learning with model based aod and meteorological data a focus on the korean peninsula |
| topic | aerosol optical depth (AOD) Himawari-8 gap-filling machine learning |
| url | https://www.mdpi.com/2072-4292/16/23/4400 |
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