An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

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dc.creatorDowling, Thomas P. F.
dc.creatorSong, Peilin
dc.creatorJong, Mark C. de
dc.creatorMerbold, Lutz
dc.creatorWooster, Martin J.
dc.creatorHuang, Jingfeng
dc.creatorZhang, Yongqiang
dc.date2021-09-05
dc.date2024-09-11T09:25:59Z
dc.date2024-09-11T09:25:59Z
dc.date.accessioned2026-06-27T16:48:02Z
dc.descriptionSatellite-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.
dc.identifierhttps://hdl.handle.net/10568/152097
dc.identifier.urihttp://hdl.handle.net/123456789/133508
dc.languageen
dc.publisherMDPI
dc.rightsOpen Access
dc.sourceDowling, 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
dc.subjectdata
dc.subjectquality
dc.subjecttemperature
dc.subjectknowledge
dc.subjectimpacts
dc.subjectimprovement
dc.subjectperformance
dc.subjecttechniques
dc.subjectdatasets
dc.subjectsatellite
dc.subjectaccuracy
dc.subjectroots
dc.subjecterrors
dc.titleAn Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques
dc.typeJournal Article

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Instituto Internacional de Investigaciones Pecuarias (ILRI)