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...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/152097 |
| _version_ | 1855524214061137920 |
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| author | Dowling, Thomas P. F. Song, Peilin Jong, Mark C. de Merbold, Lutz Wooster, Martin J. Huang, Jingfeng Zhang, Yongqiang |
| author_browse | Dowling, Thomas P. F. Huang, Jingfeng Jong, Mark C. de Merbold, Lutz Song, Peilin Wooster, Martin J. Zhang, Yongqiang |
| author_facet | Dowling, Thomas P. F. Song, Peilin Jong, Mark C. de Merbold, Lutz Wooster, Martin J. Huang, Jingfeng Zhang, Yongqiang |
| author_sort | Dowling, Thomas P. F. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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. |
| format | Journal Article |
| id | CGSpace152097 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1520972025-12-08T10:29:22Z An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques Dowling, Thomas P. F. Song, Peilin Jong, Mark C. de Merbold, Lutz Wooster, Martin J. Huang, Jingfeng Zhang, Yongqiang data quality temperature knowledge impacts improvement performance techniques datasets satellite accuracy roots errors 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. 2021-09-05 2024-09-11T09:25:59Z 2024-09-11T09:25:59Z Journal Article https://hdl.handle.net/10568/152097 en Open Access MDPI Dowling, T. P. F., Song, P., Jong, M. C. D., Merbold, L., Wooster, M. J., Huang, J., & Zhang, Y. (2021). An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques. Remote Sensing, 13(17), 3522. https://doi.org/10.3390/rs13173522 |
| spellingShingle | data quality temperature knowledge impacts improvement performance techniques datasets satellite accuracy roots errors Dowling, Thomas P. F. Song, Peilin Jong, Mark C. de Merbold, Lutz Wooster, Martin J. Huang, Jingfeng Zhang, Yongqiang An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques |
| 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 | data quality temperature knowledge impacts improvement performance techniques datasets satellite accuracy roots errors |
| url | https://hdl.handle.net/10568/152097 |
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