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Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature
Gao, Lun1; Zhan, Wenfeng1,2; Huang, Fan1; Quan, Jinling3; Lu, Xiaoman1; Wang, Fei1; Ju, Weimin1; Zhou, Ji4,5
2017
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume55Issue:1Pages:477-490
Corresponding AuthorZhan, Wenfeng(zhanwenfeng@nju.edu.cn)
AbstractThe past decade has witnessed the disaggregation of remotely sensed land surface temperature (DLST), which aims for the generation of high temporal and spatial resolution land surface temperature (LST) and which has steadily evolved into a relatively independent subfield of thermal remote sensing. Limited by Tobler's first law of geography, DLST methods require a regression between LSTs and scaling factors using image pixels within a globalized or a localized regression window. Recommendations regarding the selection of the regression window have been provided, but they are mainly subjective and based on highly specific examples. In this context, 100 DLST samples with diversified land cover types and climates were employed to assess the global window strategy (GWS) and the local window strategy (LWS). To optimize disaggregation accuracy and computational complexity, the assessments show that the optimal moving-window size (MWS) for the LWS can be estimated by the resolution ratio between pre- and postdisaggregated LSTs. To identify the better strategy between the GWS and the LWS, an indirect criterion based on aggregation-disaggregation (ICAD) was formulated, which determines the better strategy from medium to high resolution according to the associated performances from low to medium resolution. Validations demonstrate that the accuracy predicted by the ICAD achieves 72%, and in cases in which predictions are incorrect, the performances of the GWS and the LWS are similar. Further evidences indicate that the use of historical high-resolution LSTs improves the LWS by using a locally varying MWS. These findings are able to guide researchers in choosing the most suitable regression window for any particular DLST.
KeywordDisaggregation global regression strategy (GWS) land surface temperature (LST) local regression strategy (LWS) moving-window size (MWS) thermal remote sensing
DOI10.1109/TGRS.2016.2608987
WOS KeywordURBAN HEAT-ISLAND ; THERMAL-INFRARED DATA ; WATER TEMPERATURE ; SOIL-MOISTURE ; ASTER DATA ; IMAGERY ; ALGORITHM ; MODIS ; CHINA ; EVAPOTRANSPIRATION
Indexed BySCI
Language英语
Funding ProjectNational 863 Plan Grant[2013AA122801] ; National Natural Science Foundation of China[41301360] ; National Natural Science Foundation of China[41671420] ; Natural Science Foundation of Jiangsu Province[BK20130566] ; Natural Science Foundation of Jiangsu Province[BK20130568] ; DengFeng Program-B of Nanjing University
Funding OrganizationNational 863 Plan Grant ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; DengFeng Program-B of Nanjing University
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000391527900038
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/65053
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhan, Wenfeng
Affiliation1.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Univ Elect Sci & Technol, Sch Resources & Environm, Chengdu 611731, Peoples R China
5.Univ Elect Sci & Technol, Informat Geosci Res Ctr, Chengdu 611731, Peoples R China
Recommended Citation
GB/T 7714
Gao, Lun,Zhan, Wenfeng,Huang, Fan,et al. Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2017,55(1):477-490.
APA Gao, Lun.,Zhan, Wenfeng.,Huang, Fan.,Quan, Jinling.,Lu, Xiaoman.,...&Zhou, Ji.(2017).Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,55(1),477-490.
MLA Gao, Lun,et al."Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 55.1(2017):477-490.
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