Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature

doi:10.1016/j.rse.2018.04.006

	Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature
	Lu, Ning 1,6; Liang, Shunlin 2; Huang, Guanghui 3; Qin, Jun 4; Yao, Ling 1; Wang, Dongdong 2; Yang, Kun 5
刊名	REMOTE SENSING OF ENVIRONMENT
	2018-06-15
卷号	211 页码:48-58
关键词	Surface air temperature Land surface temperature Hierarchical Bayesian modeling Space-time estimation
ISSN号	0034-4257
DOI	10.1016/j.rse.2018.04.006
通讯作者	Lu, Ning(ning.robin@gmail.com)
英文摘要	Surface 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.
资助项目	National Natural Science Foundation of China[41371016] ; Yong Talent Fund of Institute of Geographic Sciences and Natural Resources Research, CAS[2015RC203] ; NCEO
WOS关键词	MODIS LST DATA ; LAND ; TRENDS ; ALGORITHM ; MODELS ; FIELDS
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000433650700005
资助机构	National Natural Science Foundation of China ; Yong Talent Fund of Institute of Geographic Sciences and Natural Resources Research, CAS ; NCEO
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/54762]
专题	中国科学院地理科学与资源研究所
通讯作者	Lu, Ning
作者单位	1.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
推荐引用方式 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.