An Improved Annual Temperature Cycle Model With the Consideration of Vegetation Change

doi:10.1109/LGRS.2022.3145380

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	An Improved Annual Temperature Cycle Model With the Consideration of Vegetation Change
	Zhao, Wei 2; Yang, Yujia 1,2; Yang, Mengjiao 1,2
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2022
卷号	19 页码:5
关键词	Land surface temperature Vegetation mapping Land surface Surface topography Surface fitting Temperature sensors Fitting Annual temperature cycle (ATC) land surface temperature (LST) moderate resolution imaging spectroradiometer (MODIS) vegetation cover
ISSN号	1545-598X
DOI	10.1109/LGRS.2022.3145380
通讯作者	Zhao, Wei(zhaow@imde.ac.cn)
英文摘要	Land surface temperature (LST) is an important parameter in land surface processes with strong relationship between surface energy and water exchange. To effectively capture the surface thermal dynamics, the annual temperature cycle model is a good option by depicting the annual variation as a constant term plus a sine function. However, this type model suffers from the assumption of constant surface thermal property which is hardly satisfied due to the changes in vegetation cover. To well address this issue, the normalized difference vegetation index (NDVI) is introduced as an indicator to characterize the variation in surface thermal property and added to the original form to propose an improved version. Through comparison between the fitting effects of the proposed model with the original one, the improvement shows good performance in suppressing the annual maximum temperature and elevating the annual minimum temperature with the increase in vegetation cover. The difference in the annual maximum and minimum temperature between the estimates from the proposed model and the original model shows good linear regression with NDVI difference when compared with the annual mean value, with the speed of -3.22 and 4.84, respectively. In addition, the fitting accuracy is also improved with a slight increase in the coefficient of determination (0.002) and a decrease in the root mean squared error (0.018 K). The application of the proposed model also provides reasonable distribution of the annual temperature parameters in the southwest of Europe and part of North Africa, confirming its potential effect in thermal dynamic monitoring.
资助项目	National Key Research and Development Program of China[2020YFA0608702] ; National Natural Science Foundation of China[42071349] ; Sichuan Science and Technology Program[2020JDJQ0003] ; Chinese Academy of Sciences Light of West China Program
WOS关键词	LAND-SURFACE TEMPERATURE ; MODIS LST ; VARIABILITY
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000753457800006
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Sichuan Science and Technology Program ; Chinese Academy of Sciences Light of West China Program
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/56446]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者	Zhao, Wei
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Wei,Yang, Yujia,Yang, Mengjiao. An Improved Annual Temperature Cycle Model With the Consideration of Vegetation Change[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5.
APA	Zhao, Wei,Yang, Yujia,&Yang, Mengjiao.(2022).An Improved Annual Temperature Cycle Model With the Consideration of Vegetation Change.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5.
MLA	Zhao, Wei,et al."An Improved Annual Temperature Cycle Model With the Consideration of Vegetation Change".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5.