文献类型： 外文期刊

作者： Li, Na ¹ ; Xu, Jianhui ³ ; Li, Xu ³ ; Qin, Boxiong ⁴ ; Wang, Yunpeng ¹ ; Fu, Dongjie ⁶ ; Zhong, Kaiwen ³ ; Qin, Zhihao ³ ;

作者机构： 1.Chinese Acad Sci, Guangzhou Inst Geochem, State Key Lab Organ Geochem, Guangzhou 510640, Peoples R China

2.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China

3.Guangdong Acad Sci, Guangdong Engn Technol Res Ctr Remote Sensing Big, Guangzhou Inst Geog, Guangdong Open Lab Geospatial Informat Technol & A, Guangzhou 510070, Peoples R China

4.Chinese Acad Sci, Guangdong Prov Key Lab Appl Bot, South China Bot Garden, Guangzhou 510650, Peoples R China

5.Chinese Acad Sci, Key Lab Vegetat Restorat & Management Degraded Eco, South China Bot Garden, Guangzhou 510650, Peoples R China

6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

7.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China

8.Chinese Acad Agr Sci, Inst Agroresources & Reg Planning, Lab Agr Ecol, Beijing 100081, Peoples R China

关键词： Land surface temperature; Spatial resolution; Land surface; Grasslands; Sensors; Remote sensing; MODIS; Atmospheric measurements; Landsat; Vegetation mapping; Land surface temperature (LST); retrieval algorithms; Sustainable Development Goals Science Satellite 1 (SDGSAT-1); validation

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.6； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2025 年 63 卷

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is a crucial parameter influencing Earth-atmosphere interactions and energy balance processes. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1) was recently launched to support the realization of the United Nations Sustainable Development Goals (SDGs), which provides worldwide three-spectrum wide-swath, high-resolution, and high-sensitivity thermal infrared (TIR) images. The objective of this study is to develop a modified three-channel split-window algorithm incorporating atmospheric water vapor content (W-TCSW) for LST retrieval from SDGSAT-1 images. This algorithm was developed from the existing split-window (SW) form. The parameters of the algorithm were determined based on the MODerate resolution atmospheric TRANsmission (MODTRAN) simulation results of 946 Thermodynamic Initial-Guess Retrieval (TIGR) atmospheric profiles. The W-TCSW algorithm was comprehensively compared with the SW and three-channel SW (TCSW) algorithms. The retrieval results of the three algorithms were validated with simulated datasets and in situ measurements from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites in China and the Surface Radiation Budget Network (SURFRAD) sites in USA. The SDGSAT-1 data retrieved by the W-TCSW algorithm was also intercompared with Landsat and ECOSTRESS LST products. The W-TCSW algorithm demonstrated the highest accuracy among the three retrieval algorithms (SW, TCSW, and W-TCSW). The influences of atmospheric water vapor content (AWVC) and land surface emissivity (LSE) as well as land use and land cover (LULC) on retrieval algorithms were discussed in a long-term time series. This study introduces a novel LST retrieval algorithm considering AWVC for SDGSAT-1 images and elucidates comprehensive validation and comparative assessment, expanding the application of high-spatial resolution TIR remote sensing data.