IGSNRR OpenIR
Downscaling Land Surface Temperatures Using a Random Forest Regression Model With Multitype Predictor Variables
Wu, Hua1,2,3; Li, Wan1,2
2019
Source PublicationIEEE ACCESS
ISSN2169-3536
Volume7Pages:21904-21916
Corresponding AuthorWu, Hua(wuhua@igsnrr.ac.cn)
AbstractIn this paper, a random forest regression model with multitype predictor variables (MTVRF) was utilized with four kinds of input variables, including surface reflectance, spectral indices, terrain factors, and land cover types, to establish the nonlinear relationship between land surface temperature (LSTs) and other land surface parameters. The main objective of this paper is to analyze the superiority of MTVRF model in multivariable regression wherever on the simple or complex underlying surface and further to demonstrate the robustness of random forest (RF) regression downscaling model trained in one study area while being applied to another area. The spatial resolution of the Moderate Resolution Imaging Spectroradiometer LST product was downscaled by MTVRF from 990 to 90 m. A comparison with two other downscaling methods, such as the basic RF model and the thermal sharpening algorithm, was also made. By computing the mean error, the determination coefficient (R-2), and the root mean square error (RMSE) between the downscaled and referenced LSTs, the MTVRF model achieved a satisfied performance. Further satisfactory results were also obtained for the MTVRF to downscale LSTs for different land covers and evaluate the training model in various regions. The RMSE of the MTVRF model trained on study area B and evaluated on study area A was 3.13k, while the RMSE trained on study area A and evaluated on study area B was 2.11k; this shows the MTVRF model trained in a specific region is thought to be robust enough to downscale LSTs under other various surface conditions.
KeywordLand surface temperature downscaling random forest thermal remote sensing thermal sharpening robustness
DOI10.1109/ACCESS.2019.2896241
WOS KeywordURBAN HEAT-ISLAND ; ENERGY FLUXES ; SPATIAL ENHANCEMENT ; DISAGGREGATION ; ALGORITHM ; COVER ; INDEX ; ASTER ; MODULATION ; RESOLUTION
Indexed BySCI
Language英语
Funding ProjectChinese Academy of Sciences through the Strategic Priority Research Program[XDA20030302] ; National Key R&D Program of China[2018YFB0504800] ; National Natural Science Foundation of China[41871267] ; National Natural Science Foundation of China[41571352]
Funding OrganizationChinese Academy of Sciences through the Strategic Priority Research Program ; National Key R&D Program of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000460652500001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/49332
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Hua
Affiliation1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Wu, Hua,Li, Wan. Downscaling Land Surface Temperatures Using a Random Forest Regression Model With Multitype Predictor Variables[J]. IEEE ACCESS,2019,7:21904-21916.
APA Wu, Hua,&Li, Wan.(2019).Downscaling Land Surface Temperatures Using a Random Forest Regression Model With Multitype Predictor Variables.IEEE ACCESS,7,21904-21916.
MLA Wu, Hua,et al."Downscaling Land Surface Temperatures Using a Random Forest Regression Model With Multitype Predictor Variables".IEEE ACCESS 7(2019):21904-21916.
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