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Normalization of the temporal effect on the MODIS land surface temperature product using random forest regression
Zhao, Wei1; Wu, Hua2; Yin, Gaofei3; Duan, Si-Bo4
Corresponding AuthorZhao, Wei(zhaow@imde.ac.cn) ; Duan, Si-Bo(duansibo@caas.cn)
2019-06-01
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
Volume152Pages:109-118
SubtypeArticle
Contribution Rank1
AbstractInformation about land surface temperature (LST) acquired from remote sensing satellite observations is very important to monitor surface energy and water exchange processes at the land-atmosphere interface. However, the wide-view of the popularly used polar-orbiting satellites (Terra and Aqua) face the challenge from the temporal effect on their LST products induced by the big temporal differences along the scan line. To generate a time-consistent LST product, a practical normalization method is proposed in this study based on random forest regression for LST observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board Terra satellite. A linking model is constructed to express LST as a function of various surface variables including vegetation indices, leaf area index, surface albedo, water index, solar radiation factor, and surface elevation. Under the assumption that the temporal effect is induced primarily by the differences in incident solar radiation, the temporal effect normalization is conducted by deriving a temporally consistent solar radiation factor which is used to drive the linking model and obtain the normalized LST. The proposed method is applied to the central Iberian Peninsula on the day of year 170 and 181, 2015. Results show that the areas with positive complement in solar radiation factor generally exhibit a positive increase in LST. An obvious improvement can be observed in the spatial pattern of the normalized LST data with the disappearance of the temperature boundary due to the big difference of satellite observation time. The Meteosat Second Generation (MSG) LST data which have the observations at the same local solar time is used for quantitative validation. The evaluation shows that the normalized LST data is more coincident with the MSG LST data than the original MODIS LST data, with significant improvements in the root mean squared deviation and bias with the MSG LST data (1.23 K and 1.66 K, respectively). Unlike previous normalization methods, the proposed method is conducted based on only satellite observations without other ancillary data. Therefore, the method demonstrates good potential for normalizing the temporal effect of the wide-view polar-orbiting satellite observations.
KeywordLand surface temperature Temporal effect MODIS Random forest regression Normalization
DOI10.1016/j.physa.2019.04.201
Indexed BySCI
WOS KeywordSOLAR-RADIATION ; DIURNAL CYCLES ; MSG-SEVIRI ; DISAGGREGATION ; DIFFUSE ; COVER ; CLIMATOLOGY ; VALIDATION
Language英语
Funding ProjectNational Natural Science Foundation of China[41771409] ; Hundred Young Talents Program of the Institute of Mountain Hazards and Environment, CAS[SDSQB-2015-02] ; Youth Innovation Promotion Association CAS[2016333] ; CAS Light of West China Program ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS[SDS-135-1708]
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000469158200009
Funding OrganizationNational Natural Science Foundation of China ; Hundred Young Talents Program of the Institute of Mountain Hazards and Environment, CAS ; Youth Innovation Promotion Association CAS ; CAS Light of West China Program ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/26418
Collection数字山地与遥感应用中心
Corresponding AuthorZhao, Wei; Duan, Si-Bo
Affiliation1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China;
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
3.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Sichuan, Peoples R China;
4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Zhao, Wei,Wu, Hua,Yin, Gaofei,et al. Normalization of the temporal effect on the MODIS land surface temperature product using random forest regression[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2019,152:109-118.
APA Zhao, Wei,Wu, Hua,Yin, Gaofei,&Duan, Si-Bo.(2019).Normalization of the temporal effect on the MODIS land surface temperature product using random forest regression.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,152,109-118.
MLA Zhao, Wei,et al."Normalization of the temporal effect on the MODIS land surface temperature product using random forest regression".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 152(2019):109-118.
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