Preprints
https://doi.org/10.5194/essd-2022-335
https://doi.org/10.5194/essd-2022-335
 
14 Oct 2022
14 Oct 2022
Status: this preprint is currently under review for the journal ESSD.

A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2005

Jia-Hao Li1,2, Zhao-Liang Li3,1, Xiangyang Liu3, and Si-Bo Duan3 Jia-Hao Li et al.
  • 1State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Abstract. Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. In this study, we developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer (AVHRR) Level-1b Global Area Coverage (GAC) data from 1981 to 2005. The GT-LST product was generated using four main processes: (1) GAC data reading, calibration, and pre-processing using open-source Python libraries; (2) cloud detection using the AVHRR-Phase I algorithm; (3) land surface emissivity estimation using an improved method considering annual land cover changes; and (4) LST retrieval based on a nonlinear generalized split-window algorithm. Validation with in situ measurements from Surface Radiation Budget (SURFRAD) sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K, and nighttime LSTs were typically better than daytime LSTs. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference (RMSD) was approximately 3.2 K, a positive bias was obtained for GT-LST, and relatively large RMSDs were obtained during the daytime, spring and summer. Furthermore, we compared our newly generated dataset with a global AVHRR daytime LST product at the selected measurements of SURFRAD sites (i.e., measurements of these two satellite datasets were valid), which revealed similar accuracies for the two datasets. However, GT-LST can additionally provide nighttime LST, which can be combined with daytime observations estimating relatively accurate monthly mean LST under all-sky conditions, with RMSE of 4.1 K. Finally, we compared GT-LST with a regional twice-daily AVHRR LST product over continental Africa in different seasons, with RMSDs ranging from 2.1 to 4.3 K. Considering these advantages, the proposed dataset provides a better data source for a range of research applications. GT-LST is freely available at https://doi.org/10.5281/zenodo.7113080 (1981–2000) (Li et al., 2022a) and https://doi.org/10.5281/zenodo.7134158 (2001–2005) (Li et al., 2022b).

Jia-Hao Li et al.

Status: open (until 16 Dec 2022)

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  • RC1: 'Comment on essd-2022-335', Anonymous Referee #1, 31 Oct 2022 reply

Jia-Hao Li et al.

Data sets

A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2005 (2001–2005) Li, Jia-Hao; Liu, Xiangyang; Li, Zhao-Liang; Duan, Si-Bo https://doi.org/10.5281/zenodo.7134158

A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2005 (1981–2000) Li, Jia-Hao; Liu, Xiangyang; Li, Zhao-Liang; Duan, Si-Bo https://doi.org/10.5281/zenodo.7113080

Jia-Hao Li et al.

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Short summary
We developed the first global historical twice-daily land surface temperature dataset at 0.05° spatial resolution from 1981 to 2005. We believe that our study makes a significant contribution to the literature because, owing to the large amounts of original Level-1b data handling and the complex data processing flow design, there are currently no global twice-daily satellite-derived LST products with sufficient spatial and temporal resolution that include observations prior to the year 2000.