Near-Real-Time Estimation of Hourly All-Weather Land Surface Temperature by Fusing Reanalysis Data and Geostationary Satellite Thermal Infrared Data

文献类型： 外文期刊

第一作者： Ding, Lirong

作者： Ding, Lirong;Ding, Lirong;Li, Zhao-Liang;Zhou, Ji;Ma, Jin;Wang, Ziwei;Wang, Wei;Tang, Wenbin;Li, Zhao-Liang;Zhu, Xinming

作者机构：

关键词： Land surface temperature; Clouds; Geostationary satellites; Interpolation; Land surface; MODIS; Estimation; All-weather (AW); geostationary satellite; high temporal resolution; land surface temperature (LST); near real time (NRT)

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.2； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2023 年 61 卷

页码：

收录情况： SCI

摘要： It is urgently needed to obtain the hourly near-real-time all-weather land surface temperature (NRT-AW LST) for immediately monitoring the disaster and environmental changes. Nevertheless, studies on estimating hourly NRT-AW LST are in the preliminary stage. In this study, we proposed a Spatio-TEmporal Fusion (STEF) method for fusing the reanalysis dataset derived from the China Land Surface Data Assimilation System (CLDAS) and thermal infrared (TIR) data derived from the Chinese Fengyun-4A (FY-4A) geostationary satellite to estimate the hourly NRT-AW LST with 0.04(degrees) resolution. The STEF method can produce NRT-AW LST without relying on the data after the target moment. STEF is tested in the Tibetan Plateau (TP). Validation results on DOY 215-366 of 2020 indicate that STEF has good accuracy: root-mean-square errors (RMSEs) and mean bias error (MBEs) under clear-sky, cloudy-sky, and AW conditions vary from 2.74 K (-1.06 K) to 3.77 K (0.14 K), from 3.31 K (-1.40 K) to 4.46 K (-0.22 K), and from 3.10 K (-1.11 K) to 3.87 K (-0.22 K), respectively. The STEF method can improve the accuracies of FY-4A LST, and RMSEs are reduced by about 0.77-1.82 K. The NRT-AW LSTs estimated by STEF have better accuracies than CLDAS LSTs under AW conditions. The SETF also exhibited similar results in 2021. We believe that the proposed STEF method can meet the requirements of NRT-AW LST estimation and contribute to improving the timeliness of regional monitoring and related parameter estimations.

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