数字农科院2.0

Land Surface Temperature Retrieval From Landsat 8 Thermal Infrared Data Over Urban Areas Considering Geometry Effect: Method and Application

文献类型: 外文期刊

作者: Ru, Chen;Duan, Si-Bo;Jiang, Xiao-Guang;Li, Zhao-Liang;Jiang, Yazhen;Ren, Huazhong;Leng, Pei;Gao, Maofang

作者机构:

关键词: Land surface temperature;Remote sensing;Urban areas;Earth;Geometry;Artificial satellites;Buildings;Geometry effect;land surface temperature (LST);Landsat 8;local climate zone (LCZ);urban heat island (UHI) intensity (UHII)

期刊名称: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

ISSN: 0196-2892

年卷期: 2021 年

页码:

收录情况: JCR(2021版) ; EI(2021版)

摘要: Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas using in situ LST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

分类号:

  • 相关文献

[1]Enhanced Surface Soil Moisture Retrieval at High Spatial Resolution From the Integration of Satellite Observations and Soil Pedotransfer Functions. Leng, Pei,Li, Zhao-Liang,Liao, Qian-Yu,Geng, Yun-Jing,Yan, Qiu-Yu,Zhang, Xia,Shang, Guo-Fei. 2022

[2]Evaluating Spatial Representativeness Across Multiple Scales for a Comprehensive Ground Validation Network Using Landsat Land Surface Temperature Data and Random Forest. He, Xuanwei,Liu, Xiangyang,Ru, Chen,Deng, Xiangyi,Zhao, Ruoyi,Yu, Wenping. 2025

[3]Optimizing Latent Heat Flux Calculation via Composited Thermal Infrared Temperatures. Jiang, Yazhen,Zhao, Jianing,Wu, Anqi,Si, Menglin,Bian, Zunjian,Tang, Ronglin,Li, Zhao-Liang. 2025

[4]Spatio-Temporal Distribution Characteristics of Global Annual Maximum Land Surface Temperature Derived from MODIS Thermal Infrared Data From 2003 to 2019. Duan, Si-Bo,Huang, Cheng,Liu, Xiangyang,Liu, Meng,Sun, Yingwei,Gao, Caixia. 2022

[5]Retrieval of Land Surface Temperature with Topographic Effect Correction from Landsat 8 Thermal Infrared Data in Mountainous Areas. Xiaolin Zhu,Si Bo Duan,Zhao Liang Li,Wei Zhao,Hua Wu,Pei Leng,Maofang Gao,Xiaoming Zhou. 2021

[6]Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province. Feng, Shanshan,Jiang, Shun,Liu, Xu,Zhang, Lei,Gan, Yangying,Xia, Ning,Wu, Wenbin,Zhou, Canfang. 2024

[7]Reconstruction of land surface temperature under cloudy conditions from Landsat 8 data using annual temperature cycle model. Xiaolin Zhu,Si Bo Duan,Zhao Liang Li,Penghai Wu,Hua Wu,Wei Zhao,Yonggang Qian. 2022

[8]Transfer Learning in Junction With a Light Use Efficiency Model for Estimating Grassland Gross Primary Production. Yu, Ruiyang,Yao, Yunjun,Tang, Qingxin,Zhang, Xueyi,Shao, Changliang,Fisher, Joshua B.,Chen, Jiquan,Zhang, Xiaotong,Li, Yufu,Xu, Jia,Liu, Lu,Xie, Zijing,Ning, Jing,Fan, Jiahui,Zhang, Luna. 2025

[9]An Operational Split-Window Algorithm for Land Surface Temperature Estimation From Chinese FY-3C VIRR Data. Li, Jia-Hao,Li, Zhao-Liang,Fan, Jinlong,Liu, Niantang. 2024

[10]Estimating All-Weather Land Surface Temperature: A Method Considering Cloud Fraction and Energy Balance. Yu, Wenping,Deng, Xiangyi,Xiao, Yao,Huang, Yajun,Zhou, Wei,Liu, Xiangyang. 2025

[11]Ground temperature measurement and emissivity determination to understand the thermal anomaly and its significance on the development of an arid environmental ecosystem in the sand dunes across the Israel-Egypt border. Qin, Z,Berliner, PR,Karnieli, A.

[12]Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan. Abbasi B.,Qin Z.,Du W.,Fan J.,Li S.,Zhao C.. 2022

[13]基于Landsat 8遥感影像的土地利用分类研究——以四川省红原县安曲示范区为例. 王敏,高新华,陈思宇,冯琦胜,梁天刚. 2015

[14]基于Landsat 8TM卫星数据的玉米灌浆期旱情监测. 杨文杰,邹楠,张召星,王克如,李少昆,杨小霞,韩冬生,王玉华,肖春花. 2017

[15]基于Landsat 8和机器学习的塔城地区草地地上生物量估测模型. 杨延晓,曹姗姗,李全胜,张鲜花,孙伟. 2024

[16]3D Indoor Scene Geometry Estimation from a Single Omnidirectional Image: A Comprehensive Survey. Meng, Ming,Zhu, Yonggui,Zhao, Yufei,Li, Zhaoxin,Zhu, Zhe. 2025

[17]A Novel Framework for Exploring the Spatial Characteristics of Leisure Tourism Using Multisource Data: A Case Study of Qingdao, China. Shang, Yiqun,Wen, Caiyun,Bai, Yangchun,Hou, Dongyang. 2022

[18]Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. Ren, Huazhong,Du, Chen,Qin, Qiming,Meng, Jinjie,Liu, Rongyuan,Yan, Guangjian,Li, Zhao-Liang,Li, Zhao-Liang. 2015

[19]Comparison of three empirical methods for water depth mapping with case study of Pratas Island. Chen, Ailian,Zhu, Boqin,Zhu, Boqin. 2015

[20]Evaluation of Radiometric Performance for the Thermal Infrared Sensor Onboard Landsat 8. Ren, Huazhong,Du, Chen,Qin, Qiming,Meng, Jinjie,Liu, Rongyuan,Yan, Guangjian,Li, Zhao-Liang,Li, Zhao-Liang. 2014

作者其他论文 更多>>