King's College London

Research portal

An improved cloud gap-filling method for longwave infrared land surface temperatures through introducing passive microwave techniques

Research output: Contribution to journalArticlepeer-review

Thomas P.F. Dowling, Peilin Song, Mark C. De Jong, Lutz Merbold, Martin J. Wooster, Jingfeng Huang, Yongqiang Zhang

Original languageEnglish
Article number3522
Issue number17
PublishedSep 2021

Bibliographical note

Funding Information: Funding: This work arose from a collaboration funded by the Science and Technology Facilities Council (UK) Newton Fund (STFC) Grant Ref: ST/N006712/1, and National Natural Science Foundation of China (NSFC) Grant Ref: 42001304, 61661136004. Funding Information: Acknowledgments: The authors wish to thank NASA-JPL for providing AMSR-2, MODIS, and DEM datasets free of charge. Thanks is also extended to Simon Hook and Gerardo Rivera of NASA-JPL for the loan of some of the radiometers used at the ILRI Kapiti Research Station as part of the ECOSTESS validation effort. The ILRI Kapiti Research Station site was funded by the UK Space Agency IPP project ‘PRISE’ under the Global Challenge Research Fund (GCRF), and by National Capability funding from NERC provided via the National Centre for Earth Observation (NCEO). Thanks also to our ILRI collaborators: Sonja Leitner, Illona Gluecks and all at ILRI Kapiti Research Station for their tireless efforts in keeping the validation station running. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


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.

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454