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Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature
Lu, Ning1,6; Liang, Shunlin2; Huang, Guanghui3; Qin, Jun4; Yao, Ling1; Wang, Dongdong2; Yang, Kun5
2018-06-15
Source PublicationREMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
Volume211Pages:48-58
Corresponding AuthorLu, Ning(ning.robin@gmail.com)
AbstractSurface air temperature (SAT) is a critical metric that is used to assess regional warming and cooling patterns, and maximum and minimum SATs are required to evaluate the model predictions of climate extremes. Since station SAT data are irregularly distributed, land surface temperature (LST) values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data are used to estimate regional SAT by using linear regression methods. The deviations between SAT and LST are largely dependent on space and time, which hampers the estimation of linear regression, especially for the maximum SAT. To obtain accurate regional SAT estimates, a three-stage hierarchical Bayesian (HB) model is proposed that incorporates the MODIS LSTs as model covariates and specifies the deviations with structured dependence of MODIS LST fields. Sampling of model parameters and estimation of SAT values are implemented under the Bayesian paradigm using a Markov Chain Monte Carlo algorithm. Sensitivity analyses involving various model configurations and running processes are discussed to help build a robust HB model. The model's performance is evaluated using station measurements that are not used in the modeling process, with RMSEs of 2.15 K (0.75%) and 1.97 K (0.73%) for monthly maximum and minimum SATs, respectively. The evaluation indicates that HB modeling is an effective method to estimate SAT from MODIS LST. The verified HB model with the covariate inputs of both MODIS daytime and nighttime LSTs is used to reproduce monthly maximum and minimum SATs that are spatially continuous over the Qinghai province in Northwestern China for 2003-2011. From the comparison between MODIS LST and HB-estimated SAT, it is found that the spatial structure and warming patterns of LST and SAT show significant distinctions, implying that they cannot be substituted for one another when assessing the regional warming trends. The spatial heterogeneity of HB model estimation is able to provide thorough insights into regional SAT status changes that could otherwise be biased by station deployment.
KeywordSurface air temperature Land surface temperature Hierarchical Bayesian modeling Space-time estimation
DOI10.1016/j.rse.2018.04.006
WOS KeywordMODIS LST DATA ; LAND ; TRENDS ; ALGORITHM ; MODELS ; FIELDS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41371016] ; Yong Talent Fund of Institute of Geographic Sciences and Natural Resources Research, CAS[2015RC203] ; NCEO
Funding OrganizationNational Natural Science Foundation of China ; Yong Talent Fund of Institute of Geographic Sciences and Natural Resources Research, CAS ; NCEO
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000433650700005
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54763
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Ning
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
3.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Gansu, Peoples R China
4.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100085, Peoples R China
5.Tsinghua Univ, Dept Earth Syst Sci, Beijing 1000084, Peoples R China
6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Lu, Ning,Liang, Shunlin,Huang, Guanghui,et al. Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature[J]. REMOTE SENSING OF ENVIRONMENT,2018,211:48-58.
APA Lu, Ning.,Liang, Shunlin.,Huang, Guanghui.,Qin, Jun.,Yao, Ling.,...&Yang, Kun.(2018).Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature.REMOTE SENSING OF ENVIRONMENT,211,48-58.
MLA Lu, Ning,et al."Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature".REMOTE SENSING OF ENVIRONMENT 211(2018):48-58.
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