Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region

NIE Lei; SHU Hong; LIU Yan

doi:10.13203/j.whugis20160433

Volume 43 Issue 10

Oct. 2018

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2018 > 43(10): 1553-1559. > DOI: 10.13203/j.whugis20160433

NIE Lei, SHU Hong, LIU Yan. Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1553-1559. DOI: 10.13203/j.whugis20160433

Citation:

PDF (6042 KB)

Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region

NIE Lei^1,,
SHU Hong^1, ,,
LIU Yan²

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China

Funds:

The National Key Research and Development Plan in 13th Five-year 2017YFB0503604

the National Natural Science Foundation of China 41331175

the Fundamental Research Funds for the Central Universities 2042016kf0176

the Fundamental Research Funds for the Central Universities 2042016kf1035

More Information

Author Bio:
NIE Lei, postgraduate, specializes in remote sensing data assimilation. E-mail: nielei1993@foxmail.com
Corresponding author:
SHU Hong, professor. E-mail:shu_hong@whu.edu.com
Received Date: December 28, 2017
Published Date: October 04, 2018

Graphical Abstract

Abstract

Abstract

Based on the complex topographical features and sparse uneven observation sites in Sichuan Province, terrain factors (slope and aspect) and vegetation index were introduced in this paper. The method of (mixed) geographically weighted regression kriging ((m)GWRK) which took into account the non-stationary of spatial relationship was adopted to study the interpolation method of monthly mean temperature and the precision analysis of the estimation results. In different seasons and different regions, the estimation results of (m)GWRK and regression Kriging (RK) based on global regression were compared. The results indicate that the coefficient of determination(R²) of regression relationship of RK, GWRK and mGWRK are 0.795, 0.922 and 0.911, respectively, and root meansquare error of these three methods are 0.83℃, 0.64℃, 0.55℃, respectively. This implies (m)GWRK is better than RK in ability to interpret the target variable and estimation accuracy. Compared with RK, the improvements of (m)GWRK on estimating monthly average temperature have the characteristics of seasonal and regional differences. The improvement is more significant in winter half year than in summer half year. And in northwest and southwest Sichuan, where topography changes acutely, the improvement is greater than in basin where topography changes gently.

FullText(HTML)

References (21)

References

[1]	鲍艳松, 李紫甜, 王冬梅, 等.基于FY3热红外数据的地表温度反演方法研究[J].农业机械学报, 2014, 45(6):266-274 http://d.old.wanfangdata.com.cn/Periodical/nyjxxb201406041 Bao Yansong, Li Zitian, Wang Dongmei, et al. Retrieval of Land Surface Temperature from FY3 Infrared Imageries[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(6):266-274 http://d.old.wanfangdata.com.cn/Periodical/nyjxxb201406041
[2]	谢云峰, 张树文.基于数字高程模型的复杂地形下的黑龙江平均气温空间插值[J].中国农业气象, 2007, 28(2):205-211 doi: 10.3969/j.issn.1000-6362.2007.02.023 Xie Yunfeng, Zhang Shuwen. Spatial Interpolation of Mean Temperature of Heilongjiang Province Based on Digital Elevation Model[J]. Chinese Journal of Agrometeorology, 2007, 28(2):205-211 doi: 10.3969/j.issn.1000-6362.2007.02.023
[3]	游松财, 李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报, 2005, 20(1):140-144 doi: 10.3321/j.issn:1000-3037.2005.01.020 You Songcai, Li Jun. Study on Error and Its Pervasion of Temperature Estimation[J]. Journal of Natural Resources, 2005, 20(1):140-144 doi: 10.3321/j.issn:1000-3037.2005.01.020
[4]	李莎, 舒红, 徐正全.利用时空Kriging进行气温插值研究[J].武汉大学学报·信息科学版, 2012, 37(2):237-241 http://ch.whu.edu.cn/CN/Y2012/V37/I2/237 Li Sha, Shu Hong, Xu Zhengquan. Interpolation of Temperature Based on Spatial-temporal Kriging[J]. Geomatics and Information Science of Wuhan University, 2012, 37(2):237-241 http://ch.whu.edu.cn/CN/Y2012/V37/I2/237
[5]	蔡迪花, 郭铌, 李崇伟.基于DEM的气温插值方法研究[J].干旱气象, 2009, 27(1):10-17 doi: 10.3969/j.issn.1006-7639.2009.01.002 Cai Dihua, Guo Ni, Li Chongwei. Interpolation of Air Temperature Based on DEM over Eastern Region of Gansu[J]. Journal of Arid Meteorology, 2009, 27(1):10-17 doi: 10.3969/j.issn.1006-7639.2009.01.002
[6]	Szymanowski M, Kryza M. The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland[J]. Pure & Applied Geophysics, 2017, 174(2):1-27 https://core.ac.uk/display/81617041
[7]	许民, 王雁, 周兆叶, 等.长江流域逐月气温空间插值方法的探讨[J].长江流域资源与环境, 2012, 21(3):327-334 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK201200349974 Xu Min, Wang Yan, Zhou Zhaoye, et al. Discussion of Methods of Spatial Interpolation for Monthly Temperature Data in the Yangtze River Basin[J]. Resources & Environment in the Yangtze Basin, 2012, 21(3):327-334 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK201200349974
[8]	覃文忠.地理加权回归基本理论与应用研究[D].上海: 同济大学, 2007 http://cdmd.cnki.com.cn/Article/CDMD-10247-2007222843.htm Tan Wenzhong. The Basic Theoretics and Application Research on Geographically Weighted Regression[D]. Shanghai: Tongji University, 2007 http://cdmd.cnki.com.cn/Article/CDMD-10247-2007222843.htm
[9]	杨顺华, 张海涛, 郭龙, 等.基于回归和地理加权回归Kri-ging的土壤有机质空间插值[J].应用生态学报, 2015, 26(6):1649-1656 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yystxb201506007 Yang Shunhua, Zhang Haitao, Guo Long, et al. Spatial Interpolation of Soil Organic Matter Using Regression Kriging and Geographically Weighted Regression Kriging[J]. Chinese Journal of Applied Ecology, 2015, 26(6):1649-1656 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yystxb201506007
[10]	Kumar S, Lal R, Liu D. A Geographically Weighted Regression Kriging Approach for Mapping Soil Organic Carbon Stock[J]. Geoderma, 2012, 189-190(6):627-634 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ0228292612
[11]	Szymanowski M, Kryza M, Spallek W. Regression-based Air Temperature Spatial Prediction Models:An Example from Poland[J]. Meteorologische Zeitschrift, 2013, 22(5):577-585 doi: 10.1127/0941-2948/2013/0440
[12]	Donkelaar A V, Martin R V, Spurr R J D, et al. High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America[J].Environmental Science & Technology, 2015, 49(17):10482-10491 doi: 10.1021/acs.est.5b02076
[13]	张国峰, 杨立荣, 瞿明凯, 等.基于地理加权回归克里格的日平均气温插值[J].应用生态学报, 2015, 26(5):1531-1536 http://d.old.wanfangdata.com.cn/Periodical/yystxb201505032 Zhang Guofeng, Yang Lirong, Qu Mingkai, et al. Interpolation of Daily Mean Temperature by Using Geographically Weighted Regression-Kriging[J]. Chinese Journal of Applied Ecology, 2015, 26(5):1531-1536 http://d.old.wanfangdata.com.cn/Periodical/yystxb201505032
[14]	马小彬.四川年鉴, 2015[M].成都:四川年鉴社, 2015 Ma Xiaobin. Sichuan Yearbook, 2015[M]. Chengdu:Sichuan Yearbook Press, 2015
[15]	Hengl T, Heuvelink G B M, Rossiter D G. About Regression-Kriging:From Equations to Case Studies[J]. Computers & Geosciences, 2007, 33(10):1301-1315 https://www.sciencedirect.com/science/article/pii/S0098300407001008
[16]	Brunsdon C, Fotheringham S, Charlton M. Geographically Weighted Regression:A Method for Exploring Spatial Nonstationarity[J]. Geographical Analysis, 1996, 28(4):281-298 doi: 10.1111/j.1538-4632.1996.tb00936.x/full
[17]	Brunsdon C, Fotheringham S, Charlton M. Geographically Weighted Regression[J].Journal of the Royal Statistical Society, 1998, 47(3):431-443 http://cdmd.cnki.com.cn/Article/CDMD-10731-1014262511.htm
[18]	Brunsdon C, Fotheringham A S, Charlton M. Some Notes on Parametric Significance Tests for Geographically Weighted Regression[J]. Journal of Regional Science, 1999, 39(3):497-524 doi: 10.1111/0022-4146.00146
[19]	汤国安, 杨昕. ArcGIS地理信息系统空间分析实验教程[M]. 2版.北京:科学出版社, 2012 Tang Guoan, Yang Xin. ArcGIS Geographic Information System Spatial Analysis the Experimental Tutorial[M]. 2nd ed Beijing:Science Press, 2012
[20]	胡丹桂, 舒红, 胡泓达.时空CoKriging的变异函数建模[J].华中师范大学学报(自然科学版), 2015, 49(4):596-602 doi: 10.3969/j.issn.1000-1190.2015.04.024 Hu Dangui, Shu Hong, Hu Hongda.Varlogram Modeling in Space-time CoKriging[J]. Journal of Central China Normal University(Natural Sciences), 2015, 49(4):596-602 doi: 10.3969/j.issn.1000-1190.2015.04.024
[21]	张建平, 刘淑珍.金沙江干热河谷区攀枝花市土地退化初探[J].中国沙漠, 1998, 18(2):149-153 doi: 10.3321/j.issn:1000-694X.1998.02.009 Zhang Jianping, Liu Shuzhen. A Preliminary Study on Land Degradation in Panzhihua City on Jinsha River Day-Hot Valley Area[J]. Journal of Desert Research, 1998, 18(2):149-153 doi: 10.3321/j.issn:1000-694X.1998.02.009

[1]	CAI Xianhua, LIU Kaili, HU Zhuoliang, ZHANG Yuan. An Algorithm for Constructing Road Network Using Block Polygon Topology[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1170-1177. DOI: 10.13203/j.whugis20190348
[2]	YANG Wei, AI Tinghua. Extracting Arterial Road Polygon from OpenStreetMap Data Based on Delaunay Triangulation[J]. Geomatics and Information Science of Wuhan University, 2018, 43(11): 1725-1731. DOI: 10.13203/j.whugis20160294
[3]	ZHANG Hao, WU Fang, GONG Xianyong, XU Junkui, ZHANG Juntao. A Parallel Factor-Based Method of Arterial Two-Lane Roads Recognition[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8): 1123-1130. DOI: 10.13203/j.whugis20150122
[4]	WANG Xiao, QIAN Haizhong, LIU Hailong, HE Haiwei, CHEN Jingnan. A Hierarchical and Iterative Road Network Matching Method by Using Road Classification[J]. Geomatics and Information Science of Wuhan University, 2016, 41(8): 1072-1078. DOI: 10.13203/j.whugis20140441
[5]	LIU Hailong, QIAN Haizhong, WANG Xiao, HE Haiwei. Road Networks Global Matching Method Using Analytical Hierarchy Process[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 644-651. DOI: 10.13203/j.whugis20130350
[6]	LI Fei, LUAN Xuechen, YANG Bisheng, LI Qiuping. Automatic Topology Maintenance Approach for High-level Road Networks[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 729-733. DOI: 10.13203/j.whugis20140115
[7]	LUAN Xuechen, YANG Bisheng, ZHANG Yunfei. Structural Hierarchy Analysis of Streets Based on Complex Network Theory[J]. Geomatics and Information Science of Wuhan University, 2012, 37(6): 728-732.
[8]	LI Qingquan, ZENG Zhe, YANG Bisheng, LIBijun. Betweenness Centrality Analysis for Urban Road Networks[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 37-41.
[9]	ZHU Qing, LI Yuan. Review of Road Network Models[J]. Geomatics and Information Science of Wuhan University, 2007, 32(6): 471-476.
[10]	DENG Hongyan, WU Fang, ZHAI Renjian. A Generalization Model of Road Networks Based on Genetic Algorithm[J]. Geomatics and Information Science of Wuhan University, 2006, 31(2): 164-167.

Cited By

Cited by

Periodical cited type(8)

1.	高奎亮，刘冰，余旭初，余岸竹，孙一帆. 面向高光谱影像分类的网络结构自动搜索方法. 武汉大学学报(信息科学版). 2024(02): 225-235 .
2.	崔林林，仙巍，柳锦宝. 产学研背景下遥感科学与技术专业”遥感原理与应用”实验教学改革探讨. 电脑与信息技术. 2023(01): 92-95 .
3.	李杰，曾超，刘汇慧，李慧芳. 地学大数据背景下遥感课程的多阶段进阶混合式教学模式探索. 测绘通报. 2023(S2): 131-136 .
4.	薛冰，赵冰玉，李京忠. 地理学视角下城市复杂性研究综述——基于近20年文献回顾. 地理科学进展. 2022(01): 157-172 .
5.	桑国庆，唐志光，毛克彪，邓刚，王靖文，李佳. 基于GEE云平台与Sentinel数据的高分辨率水稻种植范围提取——以湖南省为例. 作物学报. 2022(09): 2409-2420 .
6.	徐恩恩，郭颖，陈尔学，李增元，赵磊，刘清旺. 基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测模型. 武汉大学学报(信息科学版). 2022(08): 1298-1308 .
7.	孙玉梅，刘昱豪，边占新，孙亮，陈敬周. 深度学习PaddlePaddle框架支持下的遥感智能视觉平台研究与实现. 测绘通报. 2021(11): 65-69+75 .
8.	陈晓峰. 光学遥感立体测绘技术及其发展趋势研究. 光源与照明. 2021(11): 69-71 .

Other cited types(8)

Get Citation

PDF

XML

Article views (2189) PDF downloads (336) Cited by(16)

Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region

Abstract

References

Related Articles

Cited by

Periodical cited type(8)

Other cited types(8)

Catalog

Related

Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region

Abstract

References

Related Articles

Cited by

Periodical cited type(8)

Other cited types(8)

Catalog

Related

Export File

Citation

Format

Content