气候变化领域集成服务门户(CCPortal): Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method

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DOI	10.1016/j.jag.2019.101984
	Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method
	Neinavaz E.; Skidmore A.K.; Darvishzadeh R.
发表日期	2020
ISSN	15698432
卷号	85
英文摘要	Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVITHM), especially for agriculture and forest ecosystems. To apply NDVITHM, knowledge of the proportion of vegetation cover (PV) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the PV on the estimation of LSE and LST when using NDVITHM. In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The PV was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate PV and to compute LSE and LST. The results showed that the prediction accuracy of PV improved using an artificial neural network (R2 CV = 0.64, RMSECV = 0.05) over classic vegetation indices (R2 CV = 0.42, RMSECV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated PV affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST. © 2019 Elsevier B.V.
英文关键词	Artificial neural network; Land surface emissivity; Land surface temperature; Landsat-8; Proportion of vegetation cover; Thermal infrared remote sensing; Vegetation index
语种	英语
scopus关键词	accuracy assessment; artificial neural network; emissivity; land surface; Landsat; NDVI; prediction; satellite imagery; surface temperature; vegetation cover; Bavaria; Bavarian Forest National Park; Germany
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156436
作者单位	Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, Enschede, 7500 AE, Netherlands; Department of Environmental Science, Macquarie University, Sydney, NSW 2109, Australia
推荐引用方式 GB/T 7714	Neinavaz E.,Skidmore A.K.,Darvishzadeh R.. Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method[J],2020,85.
APA	Neinavaz E.,Skidmore A.K.,&Darvishzadeh R..(2020).Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method.International Journal of Applied Earth Observation and Geoinformation,85.
MLA	Neinavaz E.,et al."Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method".International Journal of Applied Earth Observation and Geoinformation 85(2020).