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Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape
Fan, Xiao-Mei1; Liu, Hong-Guang2; Liu, Gao-Huan3; Li, Shou-Bo1
2014-12-10
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
Volume35Issue:23Pages:7857-7877
AbstractModerate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) products provide important and reliable time-series data for the examination of global climate change, water cycling, and ecological evolution. In particular, in recently developed remote-sensing evapotranspiration models, such as the Surface Energy Balance Algorithm for Land and the Surface Energy Balance System, LST is a critical parameter that can directly influence the accuracy and integrity of final results. However, clouds and other atmospheric disturbances, which cover a large area throughout most of the year, are read as blank values by these programs, creating a problem. To solve this, a number of algorithms have been proposed to reconstruct LST data, but few can be used to evaluate flat and relatively fragmented landscape regions, such as the Yellow River Delta in China. Here, we conducted an analysis where we considered the LST of a flat area to be mainly influenced by land cover and other environmental elements (e.g. soil moisture). We used maps such as land cover, normalized difference vegetation index, and MODIS band 7 as additional data in the reconstruction model. All of the LST pixels we used were randomly divided into two parts: one part was used to train the model, and the other part was used to validate the calculated results. Three different methods have been developed to reconstruct LST data - linear regression, regression tree (RT) analysis, and artificial neural networks. In comparing these methods, we found that the RT method is able to estimate the LST of MODIS pixels with the greatest accuracy, and that it is both convenient and useful for reconstructing the LST map in flat and fragmented regions.
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
WOS Subject ExtendedRemote Sensing ; Imaging Science & Photographic Technology
WOS KeywordARTIFICIAL NEURAL-NETWORK ; YELLOW-RIVER DELTA ; TIME-SERIES ; DYNAMICS
Indexed BySCI
Language英语
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000345583500003
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68404
Collection中国科学院地理科学与资源研究所
Corresponding AuthorFan, Xiao-Mei
Affiliation1.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing, Nanjing 210044, Jiangsu, Peoples R China
2.Nanjing Agr Univ, Coll Publ Adm, Nanjing 210095, Jiangsu, Peoples R China
3.Chinese Acad Sci, LREIS Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Fan, Xiao-Mei,Liu, Hong-Guang,Liu, Gao-Huan,et al. Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2014,35(23):7857-7877.
APA Fan, Xiao-Mei,Liu, Hong-Guang,Liu, Gao-Huan,&Li, Shou-Bo.(2014).Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape.INTERNATIONAL JOURNAL OF REMOTE SENSING,35(23),7857-7877.
MLA Fan, Xiao-Mei,et al."Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape".INTERNATIONAL JOURNAL OF REMOTE SENSING 35.23(2014):7857-7877.
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