Fusing AVHRR LST With Geostationary SEVIRI LST to Create a Long-Term Daily Maximum LST Time Series Over Europe

Longtime series of satellite-derived land surface temperature (LST) data are an indispensable resource to study climate and global change. The advanced very high-resolution radiometer (AVHRR) provides thermal infrared measurements, which can be translated into LST, for 40 years with high temporal resolution. However, the time series suffers from discontinuities caused by the orbit drift of the carrying National Oceanic and Atmospheric Administration (NOAA) satellites. In this article, a new orbit drift correction (ODC) method is proposed, which uses geostationary Spinning enhanced visible and infrared imager LST to create an AVHRR daily maximum LST time series over Europe. The orbit drift corrected AVHRR LST is validated in three ways: First, observations from the same day but different NOAA platforms are intercompared; second, the orbit drift corrected time series is compared to the climate change initiative (CCI) LST product by the European Space Agency; and third, the orbit drift corrected time series is compared to air temperature (T2m) measurements. The cross-platform comparison and the comparison to CCI LST shows that the ODC method successfully minimized the discontinuities between the NOAA platforms and also spurious trends within the individual platforms. The mean absolute difference (MAD) for the cross-platform comparison is improved from 4.13 to 1.86 K, while the MAD for the comparison with CCI LST is improved from 2.39 to 2.04 K. The comparison with T2m shows both a high correlation of the anomalies and a high accordance of the trends and climatological patterns. Furthermore, it is shown that the ODC works consistently for all land cover types, however results are better in Central than in Southern Europe. This is explained by the higher daily LST amplitude and the consequently higher impacts of the orbit drift on the LST in lower latitudes. The resulting long-term LST dataset with its unique temporal coverage and resolution enables numerous studies regarding climate and global change including land atmosphere interactions and phenomena like heat waves and droughts.

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Publication Details

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2024-12-10
DOI
https://doi.org/10.1109/jstars.2024.3514734
Citations
5
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
1.88
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article

Fusing AVHRR LST With Geostationary SEVIRI LST to Create a Long-Term Daily Maximum LST Time Series Over Europe

José A. Sobrino, Martin Bachmann, A.J. Dietz, Philipp Reiners et al.
5 citations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Meteorological Phenomena and Simulations
1.88
article

Fusing AVHRR LST With Geostationary SEVIRI LST to Create a Long-Term Daily Maximum LST Time Series Over Europe

José A. Sobrino, Martin Bachmann, A.J. Dietz, Philipp Reiners, Claudia Kuenzer, Stefanie Holzwarth
article en
5 citations

Abstract

Longtime series of satellite-derived land surface temperature (LST) data are an indispensable resource to study climate and global change. The advanced very high-resolution radiometer (AVHRR) provides thermal infrared measurements, which can be translated into LST, for 40 years with high temporal resolution. However, the time series suffers from discontinuities caused by the orbit drift of the carrying National Oceanic and Atmospheric Administration (NOAA) satellites. In this article, a new orbit drift correction (ODC) method is proposed, which uses geostationary Spinning enhanced visible and infrared imager LST to create an AVHRR daily maximum LST time series over Europe. The orbit drift corrected AVHRR LST is validated in three ways: First, observations from the same day but different NOAA platforms are intercompared; second, the orbit drift corrected time series is compared to the climate change initiative (CCI) LST product by the European Space Agency; and third, the orbit drift corrected time series is compared to air temperature (T2m) measurements. The cross-platform comparison and the comparison to CCI LST shows that the ODC method successfully minimized the discontinuities between the NOAA platforms and also spurious trends within the individual platforms. The mean absolute difference (MAD) for the cross-platform comparison is improved from 4.13 to 1.86 K, while the MAD for the comparison with CCI LST is improved from 2.39 to 2.04 K. The comparison with T2m shows both a high correlation of the anomalies and a high accordance of the trends and climatological patterns. Furthermore, it is shown that the ODC works consistently for all land cover types, however results are better in Central than in Southern Europe. This is explained by the higher daily LST amplitude and the consequently higher impacts of the orbit drift on the LST in lower latitudes. The resulting long-term LST dataset with its unique temporal coverage and resolution enables numerous studies regarding climate and global change including land atmosphere interactions and phenomena like heat waves and droughts.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Universitat de València (ES), Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Climate action
Openalex Percentile: Top 15%
Meteorological Phenomena and Simulations
1.88
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