气候变化领域集成服务门户(CCPortal): An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation

CCPortal

DOI	10.1016/j.rse.2020.111979
	An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation
	Liu X.; Tang B.-H.; Li Z.-L.; Zhou C.; Wu W.; Rasmussen M.O.
发表日期	2020
ISSN	00344257
卷号	248
英文摘要	This paper proposed an improved method for separating soil and vegetation component temperatures from one pixel land surface temperature (LST) using multi-pixel and multi-temporal data. The two main features of the method are (1) the use of a diurnal temperature cycle (DTC) model to describe component temperatures and (2) the application of a spatial weighting matrix to consider the spatial correlation of component temperatures. The proposed method was evaluated using an extensive simulated dataset with five component temperature types, three LST errors and 69 fractional vegetation cover (FVC) types, and field measurements with a high temporal frequency. Due to the time extendibility of DTC model, the possibility for retrieving component temperatures at any time was analyzed. Correspondingly, the schemes for selecting the best observations for four representative periods, i.e., 10:00–12:00, 09:00–18:00, 18:00–03:00 (on the next day) and 09:00–03:00, were determined. The validations showed satisfactory accuracies, and it was found that the errors were significantly influenced by the original LST retrieval error. In addition, the difference between the ideal temperature pattern from the DTC model and the actual temperature variation also affected the accuracy of the temporally extended component temperatures. Furthermore, sensitivity analyses indicated that the separation accuracy was independent of the uncertainty of the component emissivity but was influenced by FVC. Specifically, the retrieval accuracy was sensitive to the size and variation of FVC, and the latter had a more significant influence, but the result was less sensitive to the retrieval error and angular effect of FVC. Considering its accuracy, operability and robustness, the proposed method is effective for separating soil and vegetation component temperatures. © 2020 Elsevier Inc.
英文关键词	Component temperature separation; Diurnal temperature cycle (DTC); Land surface temperature (LST); Spatial weight
语种	英语
scopus关键词	Errors; Pixels; Sensitivity analysis; Separation; Soils; Temperature distribution; Uncertainty analysis; Vegetation; Component temperatures; Diurnal temperature cycles; Fractional vegetation cover; High temporal frequency; Multi-temporal data; Spatial correlations; Temperature patterns; Vegetation components; Land surface temperature; accuracy assessment; correlation; data set; diurnal variation; land surface; numerical method; numerical model; pixel; satellite data; separation; spatial analysis; surface temperature; vegetation cover; vegetation dynamics
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/179189
作者单位	State Key Laboratory of Resources and Environment Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China; Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; DHI GRAS A/S, Hørsholm, 2970, Denmark
推荐引用方式 GB/T 7714	Liu X.,Tang B.-H.,Li Z.-L.,et al. An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation[J],2020,248.
APA	Liu X.,Tang B.-H.,Li Z.-L.,Zhou C.,Wu W.,&Rasmussen M.O..(2020).An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation.Remote Sensing of Environment,248.
MLA	Liu X.,et al."An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation".Remote Sensing of Environment 248(2020).