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

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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

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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

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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

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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.

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