An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques
Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3522 |
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| author | Thomas P. F. Dowling Peilin Song Mark C. De Jong Lutz Merbold Martin J. Wooster Jingfeng Huang Yongqiang Zhang |
| author_facet | Thomas P. F. Dowling Peilin Song Mark C. De Jong Lutz Merbold Martin J. Wooster Jingfeng Huang Yongqiang Zhang |
| author_sort | Thomas P. F. Dowling |
| collection | DOAJ |
| description | Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets. |
| first_indexed | 2024-03-10T08:05:03Z |
| format | Article |
| id | doaj.art-8655bf57a342467ab9b078a49fe0538e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:05:03Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8655bf57a342467ab9b078a49fe0538e2023-11-22T11:10:14ZengMDPI AGRemote Sensing2072-42922021-09-011317352210.3390/rs13173522An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave TechniquesThomas P. F. Dowling0Peilin Song1Mark C. De Jong2Lutz Merbold3Martin J. Wooster4Jingfeng Huang5Yongqiang Zhang6National Centre for Earth Observation (NCEO), Department of Geography, King’s College London, London WC2B 4BG, UKKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, The Chinese Academy of Sciences, Beijing 100101, ChinaNational Centre for Earth Observation (NCEO), Department of Geography, King’s College London, London WC2B 4BG, UKMazingira Centre, International Livestock Research Institute (ILRI), Nairobi P.O. Box 30709, KenyaNational Centre for Earth Observation (NCEO), Department of Geography, King’s College London, London WC2B 4BG, UKInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, The Chinese Academy of Sciences, Beijing 100101, ChinaSatellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.https://www.mdpi.com/2072-4292/13/17/3522cloud gap-fillingland surface temperaturethermal infraredpassive microwaveKenya |
| spellingShingle | Thomas P. F. Dowling Peilin Song Mark C. De Jong Lutz Merbold Martin J. Wooster Jingfeng Huang Yongqiang Zhang An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques Remote Sensing cloud gap-filling land surface temperature thermal infrared passive microwave Kenya |
| title | An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques |
| title_full | An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques |
| title_fullStr | An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques |
| title_full_unstemmed | An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques |
| title_short | An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques |
| title_sort | improved cloud gap filling method for longwave infrared land surface temperatures through introducing passive microwave techniques |
| topic | cloud gap-filling land surface temperature thermal infrared passive microwave Kenya |
| url | https://www.mdpi.com/2072-4292/13/17/3522 |
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