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Preprints
https://doi.org/10.5194/essd-2020-143
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-143
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Aug 2020

07 Aug 2020

Review status
A revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

Jin Ma1,2, Ji Zhou1, Frank-Michael Göttsche2, Shunlin Liang3, Shaofei Wang1, and Mingsong Li1 Jin Ma et al.
  • 1School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
  • 2Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe 76344, Germany
  • 3Department of Geographical Sciences, University of Maryland, College Park 20742, USA

Abstract. Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite of a variety of operational satellite LST products. In this study, a global 0.05° × 0.05° historical LST product is generated from NOAA AVHRR data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several Split-Window Algorithms with a Random Forest (RF-SWA); (2) orbital drift corrected (ODC) LST, a drift corrected version of RF-SWA LST; (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapour content of 0.04 and 1.0 g/cm2, respectively and evaluated against the simulation data set, the RF-SWA method has a Mean Bias Error (MBE) of less than 0.10 K and a Standard Deviation (STD) of 1.10 K. To compensate the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in-situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in-situ LST, the RF-SWA LST has a MBE 0.03 K with a range of −1.59 K–2.71 K and STD is 1.18 K with a range of 0.84 K–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 K to 3.01 K and STD is 3.57 K with a range of 2.34 K to 3.69 K, indicating a good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a); https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c); https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

Jin Ma et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Jin Ma et al.

Data sets

GLASS Land Surface Temperature product (1981-2000): instantaneous LST J. Ma, J. Zhou, F.-M. Göttsche, S. Liang, and S. Wang https://doi.org/10.5281/zenodo.3934354

GLASS Land Surface Temperature product (1981-2000): Oribtal Drift Corrected LST M. Jin, J. Zhou, S. Liang, and M. Li https://doi.org/10.5281/zenodo.3936627

GLASS Land Surface Temperature product (1981-2000): monthly averaged LST M. Jin, J. Zhou, and S. Liang https://doi.org/10.5281/zenodo.3936641

Jin Ma et al.

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Short summary
Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from twenty years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Land surface temperature is an important parameter in the research of climate change and many...
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