A generalized method for retrieving global daily mean land surface temperature from polar-orbiting thermal infrared sensor instantaneous observations

文献类型： 外文期刊

第一作者： Li, Jia-Hao

作者： Li, Jia-Hao;Li, Jia-Hao;Li, Zhao-Liang;Li, Zhao-Liang;Liu, Xiangyang;Duan, Si-Bo;Si, Menglin;Shang, Guofei;Zhang, Xia

作者机构：

关键词： daily mean land surface temperature; polar-orbiting thermal infrared sensor; linear regression

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.4； 五年影响因子：3.6 ）

ISSN： 0143-1161

年卷期： 2023 年

页码：

收录情况： SCI

摘要： Daily mean land surface temperature (LST) is a crucial indicator for investigating global long-term climate change. The polar-orbiting thermal infrared sensor (POTIRS) can provide limited instantaneous LSTs of a single day on a global scale. Several studies have developed algorithms to derive daily mean land surface temperature (DMLST) from limited instantaneous daily LST data, but these methods are restricted to using data from a single POTIRS after 2000. This study presents a generalized method for estimating global DMLST utilizing one daytime and one night-time observation from any POTIRS since 1981. The proposed method employs a simple linear regression of two instantaneous LST measurements from different observation times (once during the day and once at night) of POTIRSs based on in situ LST measurements from 227 flux stations operating in diverse climate regions globally. The results demonstrate that this simple linear regression model provides highly accurate estimates of DMLST under all-weather conditions, with a root mean square error (RMSE) value lower than 1.7 K. Additionally, the proposed method was employed to estimate DMLST from instantaneous LST products obtained from various sensors, including Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite, Advanced Very High Resolution Radiometer aboard the polar-orbiting National Oceanic and Atmospheric Administration satellite, and Meteorological Operational satellite. Validation results indicate that the DMLSTs estimated from these POTIRS products are in close agreement with the daily mean in situ LST, with RMSE values varying from 2.2 to 2.4 K. We expect that this generalized method will be useful for generating long-term and high-quality DMLST datasets.

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