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A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021
Earth System Science Data ( IF 11.4 ) Pub Date : 2023-05-31 , DOI: 10.5194/essd-15-2189-2023
Jia-Hao Li, Zhao-Liang Li, Xiangyang Liu, Si-Bo Duan

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 2021. The GT-LST product was generated using four main processes: (1) GAC data reading, calibration, and preprocessing 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; (4) LST retrieval based on a nonlinear generalized split-window algorithm. Validation with in situ measurements from Surface Radiation Budget (SURFRAD) sites and Baseline Surface Radiation Network sites showed that the overall root-mean-square errors (RMSEs) of GT-LST varied from 1.6 to 4.0 K, and nighttime LSTs were typically better than daytime LSTs. Intercomparison with the Moderate Resolution Imaging Spectroradiometer LST products (MYD11A1 and MYD21A1) revealed that the overall root-mean-square difference (RMSD) was approximately 3.0 K. Compared with MYD11A1 LST, GT-LST was overestimated, and relatively large RMSDs were obtained during the daytime, spring, and summer, whereas the significantly smaller positive bias was obtained between GT-LST and MYD21A1 LST. 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, with an RMSE of 2.7 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), https://doi.org/10.5281/zenodo.7134158 (2001–2005) (Li et al., 2022b), and https://doi.org/10.5281/zenodo.7813607 (2006–2021) (J. H. Li et al., 2023).

中文翻译:

1981 年至 2021 年由高级甚高分辨率辐射计观测生成的全球历史每日两次(白天和夜间)地表温度数据集

摘要。地表温度(LST)是监测和评估全球长期气候变化的关键变量。然而,现有的基于卫星的每天两次的 LST 产品只能追溯到 2000 年,这使得很难获得稳健的长期温度变化。在这项研究中,我们使用 1981 年的高级甚高分辨率辐射计 (AVHRR) 1b 级全球区域覆盖 (GAC) 数据,开发了第一个全球历史上每天两次的 LST 数据集 (GT-LST),空间分辨率为 0.05∘到 2021 年。GT-LST 产品使用四个主要过程生成:(1) 使用开源 Python 库读取 GAC 数据、校准和预处理;(2) 使用AVHRR-Phase I算法进行云检测;(3) 考虑年土地覆盖变化的改进方法估算地表发射率;(4) 基于非线性广义分裂窗口算法的LST 检索。地表辐射预算 (SURFRAD) 站点和基线地表辐射网络站点的原位测量验证表明,GT-LST 的总体均方根误差 (RMSE) 在 1.6 到 4.0 K 之间变化,夜间 LST 通常优于白天 LST。与中分辨率成像光谱仪 LST 产品(MYD11A1 和 MYD21A1)的比对显示,整体均方根差(RMSD)约为 3.0 K。与 MYD11A1 LST 相比,GT-LST 被高估,并且在期间获得了相对较大的 RMSD白天、春季和夏季,而在 GT-LST 和 MYD21A1 LST 之间获得了显着较小的正偏差。此外,我们将我们新生成的数据集与 SURFRAD 站点选定测量值的全球 AVHRR 日间地表温度产品进行了比较(即,这两个卫星数据集的测量值是有效的),这表明这两个数据集的精度相似。然而,GT-LST 还可以提供夜间 LST,它可以与白天观测相结合,估计相对准确的月平均 LST,RMSE 为 2.7 K。最后,我们将 GT-LST 与大陆上每天两次的区域 AVHRR LST 产品进行了比较不同季节的非洲,RMSD 范围从 2.1 到 4.3 K。考虑到这些优势,拟议的数据集为一系列研究应用提供了更好的数据源。GT-LST 可在 https://doi.org/10.5281/zenodo.7113080 (1981–2000)(Li 等人,2022a)、https://doi.org/10.5281/zenodo 上免费获得。
更新日期:2023-05-31
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