Reply on RC1

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).