基于GIS的“一带一路”地区气温插值方法比较研究

doi:10.12082/dqxxkx.2020.200060

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (4): 867-876.doi: 10.12082/dqxxkx.2020.200060

基于GIS的“一带一路”地区气温插值方法比较研究

杨艳昭^1,^2,^3,^*(), 郎婷婷^1,², 张超^1,², 贾琨^1,²

^1. 中国科学院地理科学与资源研究所,北京 100101
^2. 中国科学院大学,北京 100049
^3. 自然资源部资源环境承载力评价重点实验室,北京 101149

收稿日期:2020-02-08 修回日期:2020-03-18 出版日期:2020-04-25 发布日期:2020-06-10
通讯作者: 杨艳昭 E-mail:yangyz@igsnrr.ac.cn
作者简介:杨艳昭（1977— ）,女,辽宁朝阳人,研究员,博士,主要从事资源开发与区域发展研究工作。
基金资助:
中国科学院战略性先导科技专项(XDA20010201)

Comparative Study of Different Temperature Interpolation Methods in the Belt and Road Regions based on GIS

YANG Yanzhao^1,^2,^3,^*(), LANG Tingting^1,², ZHANG Chao^1,², JIA Kun^1,²

^1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
^2. University of Chinese Academy of Sciences, Beijing 100049, China
^3. Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 101149, China

Received:2020-02-08 Revised:2020-03-18 Online:2020-04-25 Published:2020-06-10
Contact: YANG Yanzhao E-mail:yangyz@igsnrr.ac.cn
Supported by:
Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20010201)

摘要/Abstract

摘要：

“一带一路”倡议是新时期中国为加强对外开放提出的全球化合作倡议,资源环境的优化配置对全球化发展意义重大。气温作为重要的基础数据和输入要素,对其进行空间化处理是实现大尺度区域资源环境优化配置的前提。本文基于地理信息技术（GIS）,运用距离平方反比法（IDS）、协同克里格法（CK）、回归距离平方反比法（RIDS）和回归协同克里格法（RCK）,对“一带一路”地区1980—2017年的2679个气象站点的月平均气温和年平均气温数据进行插值,获得了“一带一路”地区10 km分辨率的气温空间分布数据。交叉验证结果表明：① IDS、CK、RIDS和RCK插值法在整体上均较好地展示了“一带一路”地区气温的地理空间分布规律,4种插值方法的月均气温的均方根误差分别在1.93~2.43、1.78~2.14、1.31~2.23和1.23~1.92 ℃之间;年均气温的均方根误差分别为1.94、1.83、1.37和1.27 ℃;② 在“一带一路”地区,加入协变量分析的CK插值精度整体优于IDS,并且削弱了IDS的极值现象;③ RIDS和RCK对年均气温的插值精度分别较IDS和CK提高了29.4%和30.6%,表明加入地理要素并进行残差修正的插值精度得到了进一步提高。总体来看,RCK插值法对气温数据的插值精度最高,可以考虑将此方法作为“一带一路”地区温度等气象要素的插值方法。

关键词: "一带一路", 资源环境, 气温插值, GIS, 距离平方反比法, 协同克里格法, 回归, 残差修正

Abstract:

The Belt and Road initiative was a globalization cooperation initiative put forward by China to strengthen the opening-up in the new era. With the development of globalization, it is of great significance to optimize the allocation of resources and environment. As an important reference dataset and input factor, the result of temperature interpolation is the basis for optimal allocation of regional resources and environment in large scale study area. Here, taking the Belt and Road (BR) regions as the study area, the monthly and annual mean temperature data in 2679 meteorological stations from 1980 to 2017 were interpolated based on Geographic Information Technology (GIS), using Inverse Distance Squared (IDS), CoKriging (CK), Regression-IDS (RIDS) and Regression-CK (RCK) interpolation methods. The 10 km map of spatial interpolation were generated using aforementioned four methods. The results showed: (1) In the BR regions, the geographical distribution of temperature were better displayed by IDS, CK, RIDS and RCK. The Mean Square Root Error (RMSE) of monthly mean temperature were 1.93~2.43 ℃, 1.78~2.14 ℃, 1.31~2.23 ℃ and 1.23~1.92 ℃, IDS, CK, RIDS and RCK, respectively. And the RMSE of annual mean temperature were 1.94 ℃, 1.83 ℃, 1.37 ℃ and 1.27 ℃, IDS, CK, RIDS and RCK, respectively. (2) The accuracy of CK interpolation with covariates was better than that of IDS, and the peak values produced by IDS were corrected. (3) After considering the impact of terrain, the accuracy of interpolation in temperature based on Residual correction were improved by 29.4% and 30.6%, RIDS compared to IDS and RCK compared to CK, respectively. In summary, The Regression-CK performed better than other three methods in this study area and it can be considered as temperature and climate data interpolation methods in the BR regions.

Key words: the Belt and Road, resources and environment, temperature interpolation, GIS, Inverse Distance Squared, CoKriging, regression, residual correction

杨艳昭, 郎婷婷, 张超, 贾琨. 基于GIS的“一带一路”地区气温插值方法比较研究[J]. 地球信息科学学报, 2020, 22(4): 867-876.DOI:10.12082/dqxxkx.2020.200060

YANG Yanzhao, LANG Tingting, ZHANG Chao, JIA Kun. Comparative Study of Different Temperature Interpolation Methods in the Belt and Road Regions based on GIS[J]. Journal of Geo-information Science, 2020, 22(4): 867-876.DOI:10.12082/dqxxkx.2020.200060

图/表 6

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参考文献 38

[1]	刘慧, 刘卫东 . “一带一路”建设与我国区域发展战略的关系研究[J]. 中国科学院院刊, 2017(4):18-25.
	[ Liu H, Liu W D . Study on relationship between the Belt and Road initiative and regional development strategies of China[J]. Bulletin of Chinese Academy of Sciences, 2017(4):18-25. ]
[2]	刘卫东 . “一带一路”:引领包容性全球化[J]. 中国科学院院刊, 2017,32(4):331-339.
	[ Liu W D . Inclusive globalization: New philosophy of China's Belt and Road Initiative[J]. Bulletin of Chinese Academy of Sciences, 2017,32(4):331-339. ]
[3]	刘清杰 . “一带一路”沿线国家资源分析[J]. 经济研究参考, 2017(15):70-104.
	[ Liu Q J . The analysis of resource on the Belt and Road countries[J]. Review of Economic Research, 2017(15):70-104. ]
[4]	胡伟, 刘壮, 邓超 . “一带一路”空间信息走廊建设的思考[J]. 工业经济论坛, 2015(5):132-140.
	[ Hu W, Liu Z, Deng C . A study on the 'One Belt and One Road' spatial information corridor[J]. Industrial Economy Review, 2015(5):132-140. ]
[5]	Mia M. Bennett . Is a pixel worth 1000 words? Critical remote sensing and China's Belt and Road Initiative[J]. Political Geography, 2020,78:102-127.
[6]	Zhao Y B, Liu X F, Wang S J , et al. Energy relations between China and the countries along the Belt and Road: An analysis of the distribution of energy resources and interdependence relationships[J]. Renewable and Sustainable Energy Reviews, 2019,107:133-144.
[7]	Yang Y, Fan M D . Analysis of the spatial-temporal differences and fairness of the regional energy ecological footprint of the Silk Road Economic Belt (China Section)[J]. Journal of Cleaner Production, 2019,215:1246-1261.
[8]	何鹏, 蹇东南, 李晓 , 等. 复杂地形影响下四川省逐月气温空间插值方法研究[J]. 生态与农村环境学报, 2019,35(6):801-808.
	[ He P, Qian D N, Li X , et al. Study on Interpolation model of monthly temperature in Sichuan Province under the influence of complex topography[J]. Journal of Ecology and Rural Environment, 2019,35(6):801-808. ]
[9]	阎洪 . 气候时空数据的样条插值与应用[J]. 地理与地理信息科学, 2003,19(5):27-31.
	[ Yan H . Spline interpolation of spatial-temporal climate data for China[J]. Geography and Geo-information Science, 2003,19(5):27-31. ]
[10]	唐焰, 封志明, 杨艳昭 . 基于栅格尺度的中国人居环境气候适宜性评价[J]. 资源科学, 2008,30(5):648-653.
	[ Tang Y, Feng Z M, Yang Y Z . Evaluation of climate suitability for human settlement in China[J]. Resources Science, 2008,30(5):648-653. ]
[11]	王志芳 . 中国建设“一带一路”面临的气候安全风险[J]. 国际政治研究, 2015(4):56-74.
	[ Wang Z F . Climate security risks associated with China's "the Belt and Road" Initiative[J]. The Journal of International Studies, 2015(4):56-74. ]
[12]	韩权, 江东, 付晶莹 , 等. “一带一路”沿线撒哈拉以南非洲地区能矿资源投资风险指数研究[J]. 科技导报, 2018,36(3):108-115.
	[ Han Q, Jiang D, Fu J Y , et al. On the risk index of energy resources investment in sub-Saharan Africa along “the Belt and Road Initiative”[J]. Science Technology review, 2018,36(3):108-115. ]
[13]	吴绍洪, 刘路路, 刘燕华 , 等. “一带一路”陆域地理格局与环境变化风险[J]. 地理学报, 2018,73(7):1214-1225.
	[ Wu S H, Liu L L, Liu Y H , et al. Geographical patterns and environmental change risks in terrestrial areas of the Belt and Road[J]. Acta Geographica Sinica, 2018,73(7):1214-1225. ]
[14]	王新鸿, 邱实, 姜小光 , 等. 高光谱热红外数据反演地表温度与比辐射率方法研究[J]. 干旱区地理, 2010(3):107-114.
	[ Wang X H, Qiu S, Jiang X G , et al. Land surface temperature and emissivity retrieval from hyperspectral thermal infrared data[J]. Arid Land Geography, 2010(3):107-114. ]
[15]	封志明, 杨艳昭, 丁晓强 , 等. 气象要素空间插值方法优化[J]. 地理研究, 2004,23(3):357-364.
	[ Feng Z M, Yang Y Z, Ding X Q , et al. Optimization of the spatial interpolation methods for climate resources[J]. Geographical Research, 2004,23(3):357-364. ]
[16]	修思玉, 吕泓辰, 康世伦 , 等. 地理信息系统中不同空间插值方法的比较研究[J]. 森林工程, 2014,30(5):110-113.
	[ Xiu S Y, Lv H C, Kang S L , et al. Comparative study on different spatial interpolation methods in GIS[J]. Forest Engineering, 2014,30(5):110-113. ]
[17]	毛萍, 黄东晓, 王芋华 , 等. 全球变化领域研究现状与趋势的大数据分析[J]. 中国科学院大学学报, 2017,34(4):441-451.
	[ Mao P, Huang D X, Wang Y H , et al. Big data analysis of status and trends of global change research[J]. Journal of University of Chinese Academy of Sciences, 2017,34(4):441-451. ]
[18]	Boer E P J, Beurs K M D, Hartkamp A D . Kriging and thin plate splines for mapping climate variables[J]. International Journal of Applied Earth Observation and Geoinformation, 2001,3(2):146-154.
[19]	Marquínez J, Lastra J, Garcia P . Estimation models for precipitation in mountainous regions: The use of GIS and multivariate analysis[J]. Journal of Hydrology, 2003,270:1-11. doi: 10.1016/S0022-1694(02)00110-5
[20]	林忠辉, 莫兴国, 李宏轩 , 等. 中国陆地区域气象要素的空间插值[J]. 地理学报, 2002,57(1):47-56. doi: 10.11821/xb200201006
	[ Lin Z H, Mo X G, Li H X , et al. Comparison of three spatial interpolation methods for climate variables in China[J]. Acta Geographica Sinica, 2002,57(1):47-56. ] doi: 10.11821/xb200201006
[21]	彭彬, 周艳莲, 高苹 , 等. 气温插值中不同空间插值方法的适用性分析——以江苏省为例[J]. 地球信息科学学报, 2011,13(4):539-548. doi: 10.3724/SP.J.1047.2011.00539
	[ Peng B, Zhou Y L, Gao P , et al. Suitability assessment of different interpolation methods in the gridding process of station collected air temperature: A case study in Jiangsu Province, China[J]. Journal of Geo-information Science, 2011,13(4):539-548. ] doi: 10.3724/SP.J.1047.2011.00539
[22]	段平, 盛业华, 李佳 , 等. 自适应的IDW插值方法及其在气温场中的应用[J]. 地理研究, 2014,33(8):1417-1426. doi: 10.11821/dlyj201408003
	[ Duan P, Sheng Y H, Li J , et al. Adaptive IDW interpolation method and its application in the temperature field[J]. Geographical Research, 2014,33(8):1417-1426. ] doi: 10.11821/dlyj201408003
[23]	Hussain I, Spock G, Pilz J , et al. Spatial-temporal interpolation of precipitation during monsoon periods in Pakistan[J]. Advances in Water Resources, 2010,33(8):880-886. doi: 10.1016/j.advwatres.2010.04.018
[24]	Bhowmik A K, Cabral P . Statistical evaluation of spatial interpolation methods for small-sampled region: A case study of temperature change phenomenon in Bangladesh[J]. Lecture Notes in Computer Science, 2011,6782:44-59.
[25]	Brinckmann S, Krähenmann S, Bissolli P . High-resolution daily gridded datasets of air temperature and wind speed for Europe[J]. International Journal of Pharmaceutics, 2015,8(2):649-702.
[26]	李佳霖, 樊子德, 邓敏 . 基于空间异质分区的残差IDW插值方法[J]. 地理与地理信息科学, 2015,31(5):29-33.
	[ Li J L, Fan Z D, Deng M . Residual inverse distance weighting spatial interpolation method based on spatial heterogeneity sub-region[J]. Geography and Geo-information Science, 2015,31(5):29-33. ]
[27]	李框宇, 周梅, 陈玖英 , 等. 一种适用于气温空间插值的改进梯度距离平方反比法[J]. 2019,36(4):491-497.
	[ Li K Y, Zhou M, Chen J Y , et al. An approach of improved gradient plus inverse distance squared for spatial interpolation of temperature[J]. Journal of University of Chinese Academy of Sciences, 2019,36(4):491-497. ]
[28]	李巍 . 降雨量空间插值方法比较研究[J]. 安徽农业科学, 2014,42(12):3667-3669.
	[ Li W . Study on interpolation methods of rainfall[J]. Journal of Anhui Agricultural Sciences, 2014,42(12):3667-3669. ]
[29]	彭思岭 . 气象要素时空插值方法研究[D]. 长沙:中南大学, 2011: 3-10.
	[ Peng S L . Developments of Spatio-temporal interpolation methods for meteorological elements[D]. Changsha: Central South University, 2011: 3-10. ]
[30]	李波, 黄敬峰, 金志凤 , 等. 基于GIS的浙江省年降水量空间分布推算方法研究[J]. 浙江大学学报(理学版), 2010,37(2):121-126.
	[ Li B. Huang J F. Jin Z F , et al. Methods for calculating precipitation spatial distribution of Zhejiang Province based on GIS[J]. Journal of Zhejiang University(Science Edition), 2010,37(2):121-126. ]
[31]	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):246-251.
	[ Zhang G F, Yang L R, Qu M K , et al. Interpolation of daily mean temperature by using geographically weighted regression-Kriging[J]. Chinese Journal of Applied Ecology, 2015,26(5):246-251. ]
[32]	张国峰, 白蕤, 李伟光 , 等. 海南岛日最低、最高气温空间插值研究[J]. 中国农学通报, 2017,33(19):101-109.
	[ Zhang G F, Bai R, Li W G , et al. Spatial interpolation of daily minimum temperature and daily maximum temperature in Hainan island[J]. Chinese Agricultural Science Bulletin, 2017,33(19):101-109. ]
[33]	李金洁, 王爱慧 . 基于西南地区台站降雨资料空间插值方法的比较[J]. 气候与环境研究, 2019,24(1):50-60.
	[ Li J J, Wang A H . Comparison of spatial interpolation methods based on monthly precipitation observation data of station in southwest China[J]. Climatic and Environment Research, 2019,24(1):50-60. ]
[34]	邹嘉龄, 刘卫东 . 2001-2013年中国与“一带一路”沿线国家贸易网络分析[J]. 地理科学, 2016,36(11):1629-1636.
	[ Zou J L, Liu W D . Trade Network of China and Countries Along "Belt and Road Initiative" Areas from 2001 to 2013[J]. Scientia Geographica Sinica, 2016,36(11):1629-1636. ]
[35]	郝林钢 . “一带一路”分区自然地理特征及水资源分析[D]. 2019: 20-25.
	[ Hao L G . Physiographic Characteristics and Water Resources analysis of the regions among the Belt and Road[D], Henan: Zhengzhou University, 2019: 20-25. ]
[36]	杨凤海, 王帅, 刘晓庆 , 等. 基于ArcGIS的近10年黑龙江省旬平均气温插值与建库[J]. 黑龙江农业科学, 2009(5):126-130.
	[ Yang F H, Wang S, Liu X Q , et al. The spatial interpolation and GeoDatabase foundation of average 10-day air temperature of Heilongjiang Province in recent 10 years based on ArcGIS[J]. Heilongjiang Agricultural Sciences, 2009(5):126-130. ]
[37]	马轩龙, 李春娥, 陈全功 . 基于GIS的气象要素空间插值方法研究[J]. 草业科学, 2008,25(11):13-19.
	[ Ma X L, Li C E, Chen Q G . Study on the method of GIS based spatial interpolation of climate factors in China[J]. Pratacultural Science, 2008,25(11):13-19. ]
[38]	孙钰森, 王维芳, 李国春 . 基于地理加权回归克里格模型的帽儿山地区森林碳储量空间分布[J]. 应用生态学报, 2019,30(5):223-231.
	[ Sun Y S, Wang W F, Li G C . Spatial distribution of forest carbon storage in Maoershan region,Northeast China based on geographically weighted regression kriging model[J]. Chinese Journal of Applied Ecology, 2019,30(5):223-231. ]

月份	相关系数（λ）	相关系数（φ）	相关系数（h）	复相关系数	回归方程序号
1	-0.115^**	-0.877^**	-0.152^**	0.951	Z₁=46.648-0.127λ-0.913φ-0.005h	（11）
2	-0.085^**	-0.896^**	-0.131^**	0.957	Z₂=45.946-0.112λ-0.886φ-0.004h	（12）
3	-0.052^**	-0.910^**	-0.123^**	0.959	Z₃=44.328-0.088λ-0.777φ-0.004h	（13）
4	-0.008^**	-0.904^**	-0.120^**	0.941	Z₄=42.320-0.060λ-0.635φ-0.003h	（14）
5	0.007	-0.869^**	-0.140^**	0.905	Z₅=40.040-0.043λ-0.484φ-0.003h	（15）
6	0.035	-0.803^**	-0.164^**	0.837	Z₆=37.136-0.026λ-0.349φ-0.002h	（16）
7	0.020	-0.746^**	-0.180^**	0.786	Z₇=36.040-0.023λ-0.282φ-0.002h	（17）
8	-0.009	-0.794^**	-0.171^**	0.839	Z₈=37.403-0.031λ-0.324φ-0.002h	（18）
9	0.001	-0.872^**	-0.174^**	0.915	Z₉=38.679-0.040λ-0.434φ-0.003h	（19）
10	-0.038^**	-0.899^**	-0.162^**	0.950	Z₁₀=41.693-0.064λ-0.594φ-0.004h	（20）
11	-0.094^**	-0.888^**	-0.147^**	0.954	Z₁₁=45.833-0.102λ-0.785φ-0.004h	（21）
12	-0.115^**	-0.875^**	-0.156^**	0.949	Z₁₂=46.378-0.122λ-0.874φ-0.005h	（22）
年平均	-0.055^**	-0.901^**	-0.153^**	0.956	Z_年均=41.883-0.070λ-0.612φ-0.003h	（23）

月份	IDS		RIDS		CK		RCK
月份	ME	RSME	ME	RMSE	ME	RMSE	ME	RMSE
1	0.026	2.30	-0.078	2.23	0.007	2.14	-0.009	1.92
2	0.038	2.30	-0.059	1.98	0.006	2.05	-0.007	1.70
3	0.030	2.30	-0.048	1.67	0.004	2.04	-0.004	1.52
4	0.003	2.19	-0.019	1.60	0.002	2.07	-0.001	1.51
5	-0.013	2.13	0.000	1.58	0.003	2.09	-0.002	1.51
6	-0.025	2.15	0.014	1.67	0.007	2.17	-0.004	1.62
7	-0.029	2.12	0.019	1.67	0.003	2.11	-0.006	1.66
8	-0.021	2.00	0.010	1.50	0.003	1.99	-0.017	1.59
9	-0.014	1.93	0.000	1.31	0.002	1.89	-0.003	1.28
10	0.019	1.97	-0.036	1.32	0.004	1.78	-0.003	1.23
11	-0.048	2.21	-0.068	1.73	0.007	1.85	-0.007	1.50
12	0.062	2.43	-0.086	2.14	0.016	2.04	-0.010	1.84
年均气温	0.013	1.94	-0.028	1.37	0.003	1.83	-0.004	1.27

基于GIS的“一带一路”地区气温插值方法比较研究

Comparative Study of Different Temperature Interpolation Methods in the Belt and Road Regions based on GIS

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