Downscaling Land Surface Temperatures Using a Random Forest Regression Model With Multitype Predictor Variables
文献类型：期刊论文
| 作者 | Wu, Hua1,2,3 ; Li, Wan1,2
|
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2019 |
| 卷号 | 7页码:21904-21916 |
| 关键词 | Land surface temperature downscaling random forest thermal remote sensing thermal sharpening robustness |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2019.2896241 |
| 通讯作者 | Wu, Hua(wuhua@igsnrr.ac.cn) |
| 英文摘要 | In 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. |
| WOS关键词 | URBAN HEAT-ISLAND ; ENERGY FLUXES ; SPATIAL ENHANCEMENT ; DISAGGREGATION ; ALGORITHM ; COVER ; INDEX ; ASTER ; MODULATION ; RESOLUTION |
| 资助项目 | Chinese 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] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000460652500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Chinese Academy of Sciences through the Strategic Priority Research Program ; National Key R&D Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/49331]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wu, Hua |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。