A data fusion modeling framework for retrieval of land surface temperature from landsat8 and modis data

doi:10.3390/s20154337

CORC > 兰州理工大学 > 兰州理工大学

	A data fusion modeling framework for retrieval of land surface temperature from landsat8 and modis data
	Zhao, Guohui 1,4; Zhang, Yaonan 1; Tan, Junlei 2; Li, Cong 3,4; Ren, Yanrun 1,4
	2020-08-01
关键词	Atmospheric temperature Climate change Critical current density (superconductivity) Data fusion Image resolution Mean square error Radiometers Surface measurement Surface propertiesComplementary characteristics Environmental change Index of agreements Root mean square errors Spatial and temporal resolutions Spatio-temporal dataset Structural similarity indices (SSIM) Temporal information
卷号	20
期号	15
DOI	10.3390/s20154337
页码	1-23
英文摘要	Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezingthawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixelbased multispatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat8 and MODIS data was implemented to generate the synthetic LST with Landsatlike spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsatderived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65–0.88, the root mean square error (RMSE) yielded a range of 1.6–3.4 °C, and the averaged bias was 0.6 °C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 °C to 1.5 °C with an average value of 1.1 °C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 °C. The validation results that fused LST possesses the spatial pattern of Landsatderived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a highquality LST spatiotemporal dataset for various applications in environment studies. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
会议录	Sensors (Switzerland)
会议录出版者	MDPI AG, Postfach, Basel, CH-4005, Switzerland
语种	英语
ISSN号	14248220
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS记录号	WOS:000559208600001
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/132645]
专题	兰州理工大学
作者单位	1.Science Big Data Center of Cold and Arid Regions, Northwest Institute of EcoEnvironment and ResourcesChinese Academy of Sciences, Lanzhou; 730000, China; 2.Heihe Remote Sensing Experimental Research Station, Northwest Institute of EcoEnvironment and Resources, Chinese Academy of Sciences, Lanzhou; 730000, China; 3.College of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China 4.University of Chinese Academy of Sciences, Beijing; 100049, China;
推荐引用方式 GB/T 7714	Zhao, Guohui,Zhang, Yaonan,Tan, Junlei,et al. A data fusion modeling framework for retrieval of land surface temperature from landsat8 and modis data[C]. 见:.