标题: Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data

作者: Ma, J (Ma, Jun); Shen, HF (Shen, Huanfeng); Wu, PH (Wu, Penghai); Wu, JA (Wu, Jingan); Gao, ML (Gao, Meiling); Meng, CL (Meng, Chunlei)

来源出版物: REMOTE SENSING OF ENVIRONMENT 卷: 278 文献号: 113083 DOI: 10.1016/j.rse.2022.113083 出版年: SEP 1 2022

摘要: Land surface temperature (LST) is a key parameter when monitoring land surface processes. However, cloud contamination and the tradeoff between the spatial and temporal resolutions greatly impede the access to highquality thermal infrared (TIR) remote sensing data. Despite the massive efforts made to solve these dilemmas, it is still difficult to generate LST estimates with concurrent spatial completeness and a high spatio-temporal resolution. Land surface models (LSMs) can be used to simulate gapless LST with a high temporal resolution, but this usually comes with a low spatial resolution. In this paper, we present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and halfhourly temporal resolution. The global linear model (GloLM) model and the diurnal land surface temperature cycle (DTC) model are respectively performed as preprocessing steps for sensor and temporal normalization between the different LST data. The Landsat LST, Moderate Resolution Imaging Spectroradiometer (MODIS) LST, and Community Land Model Version 5.0 (CLM 5.0)-simulated LST are then fused using a filter-based spatiotemporal integrated fusion model. Evaluations were implemented in an urban-dominated region (the city of Wuhan in China) and a natural-dominated region (the Heihe River Basin in China), in terms of accuracy, spatial variability, and diurnal temporal dynamics. Results indicate that the fused LST under all-weather conditions is highly consistent with actual Landsat LST data (in situ LST measurements), in terms of a Pearson correlation coefficient of 0.94 (0.96-0.99), a mean absolute error of 0.71-0.98 K (0.82-3.34 K), and a root-mean-square error of 0.97-1.26 K (1.09-4.36 K). The generated diurnal Landsat-like LSTs under all weather conditions are able to support diurnal dynamic studies that are the most relevant to human activities, such as the study of urban heat islands (UHIs) and water resource management at the field scale.

作者关键词: Land surface temperature; Thermal infrared remote sensing; Land surface model; Data fusion; Normalization

地址: [Ma, Jun; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wu, Penghai] Anhui Univ, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei 230601, Peoples R China.

[Wu, Jingan] Sun Yat sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China.

[Gao, Meiling] Changan Univ, Coll Geol Engn & Geomatics, Xian 710054, Peoples R China.

[Meng, Chunlei] China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China.

通讯作者地址: Shen, HF (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: shenhf@whu.edu.cn

影响因子：13.85