大气与环境光学学报2025，Vol.20Issue(5)：637-651,15.DOI:10.3969/j.issn.1673-6141.2025.05.007
基于高分五号的地表温度反演方法与应用
Surface temperature retrieval methods and applications based on Chinese Gaofen-5 satellite
摘要
Abstract
Surface temperature is a crucial parameter in the studies of urban environmental monitoring,geothermal anomalies,global climate change,and other topics.The full-spectrum spectral imager on board Gaofen-5(GF-5)satellite has a high spatial resolution imaging capability of 40 m in the thermal infrared band,which can offer detailed information on the spatial distribution and variation of surface temperature.In this paper,two split-window algorithms,two-channel and four-channel,are used to invert GF-5 data to obtain the surface temperature of Tianjin,China,in March 2019,and the accuracy of the inversion results is evaluated using ASTER surface temperature product at the same time.The results show that the optimal root-mean-square error of the two-channel split-window algorithm for surface temperature is 1.19 K,while that of the four-channel split-window algorithm is 2.31 K.And the comparison of different channel combinations shows that the B9/B10 combination of the two-channel split-window algorithm has the highest inversion accuracy.In addition,to verify the application capability of GF-5 high-resolution thermal infrared data in monitoring the heat island effect,a comparative analysis of GF-5 and ASTER surface temperature and heat island effect is conducted in the region surrounding Yadian Reservoir of Tianjin.The results show that the high-resolution surface temperature data of GF-5 can play an important role in urban thermal environment monitoring and urban planning.关键词
高分五号/热红外/地表温度/劈窗算法/城市热岛效应Key words
Gaofen-5/thermal infrared/land surface temperature/split-window algorithm/urban heat island effect分类
天文与地球科学引用本文复制引用
贾志扬,张文豪,占玉林,张丽丽,付雅帅,马宇,邴芳飞..基于高分五号的地表温度反演方法与应用[J].大气与环境光学学报,2025,20(5):637-651,15.基金项目
高分辨率对地观测系统重大专项(30-Y30F06-9003-20/22),国家自然科学基金(41907192),国家科技重大专项(67-Y50G04-9001-22/23),河北省自然科学基金(D2020409003,D2022103002),河北省高等学校科学技术研究项目(ZD2021303),河北省全职引进高端人才科研项目(2020HBQZYC002),北华航天工业学院博士科研启动基金(BKY-2021-31),2023年省级在读研究生创新能力培养资助项目(CXZZSS2023166),北华航天工业学院研究生创新资助项目(YKY-2022-54) （30-Y30F06-9003-20/22）
