IGSNRR OpenIR
An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes
Quan, Jinling1,2; Zhan, Wenfeng3; Ma, Ting1,2; Du, Yunyan1,2; Guo, Zheng4; Qin, Bangyong5
2018-03-01
Source PublicationREMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
Volume206Pages:403-423
Corresponding AuthorQuan, Jinling(quanjl@lreis.ac.cn)
AbstractThe trade-off between spatial and temporal resolutions in remote sensing has greatly limited the availability of concurrently high spatiotemporal land surface temperature (LST) data for wide applications. Although many efforts have been made to resolve this dilemma, most have difficulties in generating diurnal fine-resolution LSTs with high spatial details for landscapes with significant heterogeneity and land cover type change. This study proposes an integrated framework to BLEnd Spatiotemporal Temperatures (termed BLEST) of Landsat, MODIS and a geostationary satellite (FY-2F) to one hour interval and 100 m resolution, where (1) a linear temperature mixing model with conversion coefficients is combined to better characterize heterogeneous landscapes and generate more accurate predictions for small and linear objects; (2) residuals are downscaled by a thin plate spline interpolator and restored to the primary fine-resolution estimations to include information about land cover type change; and (3) separate operations at annual and diurnal scales with nonlinear temperature modeling are designed to neutralize the hybrid impacts of large scale gap and land cover type change. BLEST was tested on both simulated data and actual satellite data at annual, diurnal and combined scales, and evaluations were conducted with the simulated/actual fine-resolution data, in-situ data, and with three popular fusion methods, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the Enhanced STARFM (ESTARFM) and the spatiotemporal integrated temperature fusion model (STITFM). Results show higher accuracy by BLEST with more spatial details and pronounced temporal evolutions, particularly over heterogeneous landscapes and changing land cover types. BLEST is proposed to augment the spatiotemporal fusion system and further support diurnal dynamic studies in land surfaces.
KeywordData fusion Land surface temperature Landsat MODIS Geostationary satellite Heterogeneity
DOI10.1016/j.rse.2017.12.003
WOS KeywordURBAN HEAT-ISLAND ; REFLECTANCE FUSION MODEL ; SATELLITE DATA ; CLEAR-SKY ; SPARSE REPRESENTATION ; SPATIAL-RESOLUTION ; IMAGE FUSION ; TIME-SERIES ; MODIS DATA ; IN-SITU
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41601462] ; National Natural Science Foundation of China[41590845] ; National Natural Science Foundation of China[41421001] ; Major State Basic Research Development Program of China[2015CB954101] ; Key Research Project on Frontier Science, CAS[QYZDY-SSW-DQC007-1] ; Youth Science Funds of LREIS, CAS[O8R8A083YA] ; Key Laboratory of Space Utilization, CAS[LSU-2016-06-03]
Funding OrganizationNational Natural Science Foundation of China ; Major State Basic Research Development Program of China ; Key Research Project on Frontier Science, CAS ; Youth Science Funds of LREIS, CAS ; Key Laboratory of Space Utilization, CAS
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000427342700030
PublisherELSEVIER SCIENCE INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57225
Collection中国科学院地理科学与资源研究所
Corresponding AuthorQuan, Jinling
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China
3.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210046, Jiangsu, Peoples R China
4.Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China
5.Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing 100094, Peoples R China
Recommended Citation
GB/T 7714
Quan, Jinling,Zhan, Wenfeng,Ma, Ting,et al. An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes[J]. REMOTE SENSING OF ENVIRONMENT,2018,206:403-423.
APA Quan, Jinling,Zhan, Wenfeng,Ma, Ting,Du, Yunyan,Guo, Zheng,&Qin, Bangyong.(2018).An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes.REMOTE SENSING OF ENVIRONMENT,206,403-423.
MLA Quan, Jinling,et al."An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes".REMOTE SENSING OF ENVIRONMENT 206(2018):403-423.
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