气候变化领域集成服务门户(CCPortal): A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature

CCPortal

DOI	10.1016/j.rse.2021.112437
	A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature
	Zhang X.; Zhou J.; Liang S.; Wang D.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	An all-weather land surface temperature (LST) dataset at moderate to high spatial resolutions (e.g. 1 km) has been in urgent need, especially in areas frequently covered in clouds (i.e. the Tibetan Plateau). Merging satellite thermal infrared (TIR) and passive microwave (PMW) observations is a widely-adopted approach to derive such LST datasets, whereas the swath gap of the PMW data leads to considerable data deficiency or low reliability in the merged LST, especially at the low-mid latitudes. Fortunately, reanalyzed data provides the spatiotemporally continuous LST and thus, is promising to be merged with TIR data for reconstructing the all-weather LST without this issue. However, few studies along this direction have been reported. In this context, based on the decomposition model of LST time series, this study proposes a novel reanalysis and thermal infrared remote sensing data merging (RTM) method to reconstruct the 1-km all-weather LST. The method was applied to merge Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Global/China Land Data Assimilation System (GLDAS/CLDAS) data over the Tibetan Plateau and the surrounding area. Results show that the RTM LST has RMSEs of 2.03–3.98 K and coefficients of determination of 0.82–0.93 under all-weather conditions when validated against the ground measured LST. Besides, from comparison between RTM LST and current existing PMW-TIR merged LST, it is found the former LST efficiently outperforms the latter one in terms of accuracy and image quality, especially over the MW swath gap-covered area. In addition, compared to the MODIS-CLDAS merged all-weather LST based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the two LSTs have comparable accuracy while the RTM LST has higher spatial completeness. The method is promising for generating a long-term all-weather LST record at moderate to high spatiotemporal resolutions at large scales, which would be beneficial to associated studies and applications. © 2021 Elsevier Inc.
英文关键词	All-weather; Land surface temperature; Merging; Reanalysis data; Thermal infrared remote sensing
语种	英语
scopus关键词	Atmospheric temperature; Infrared radiation; Land surface temperature; Merging; Radiometers; Surface measurement; Surface properties; All-weather; Data merging; Land surface temperature; Passive microwaves; Reanalysis; Reanalysis data; Remote sensing data; Thermal infrared remote sensing; Thermal-infrared; Tibetan Plateau; Remote sensing; accuracy assessment; Aqua (satellite); data set; decomposition analysis; infrared radiation; land surface; methodology; model validation; MODIS; numerical model; reconstruction; remote sensing; satellite altimetry; satellite data; spatial resolution; surface temperature; Terra (satellite); China; Qinghai-Xizang Plateau
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178850
作者单位	School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, 611731, China; Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States
推荐引用方式 GB/T 7714	Zhang X.,Zhou J.,Liang S.,et al. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature[J],2021,260.
APA	Zhang X.,Zhou J.,Liang S.,&Wang D..(2021).A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature.Remote Sensing of Environment,260.
MLA	Zhang X.,et al."A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature".Remote Sensing of Environment 260(2021).