Remote Sensing Technology and Application 鈥衡�� 2007, Vol. 22 鈥衡�� Issue (1): 39-44.DOI: 10.11873/j.issn.1004-0323.2007.1.39

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Comparison of Spatial Interpolation Methods of Snow Depth in the West of China

TANG Guo-dong, KE Chang-qing   

  1. (Department of Urban and Resources Sciences,Nanjing University,Nanjing210093,China)
  • Received:2006-05-10 Revised:2006-11-14 Online:2011-10-14 Published:2007-01-01

涓浗瑗块儴鍦板尯绉洩娣卞害鐨勭┖闂存彃鍊兼瘮杈�

鍞愬浗鏍�,鏌暱闈�   

  1. (鍗椾含澶у鍩庡競涓庤祫婧愬绯�,姹熻嫃 鍗椾含銆�210093)
  • 浣滆�呯畝浠�:鍞愬浗鏍�(1981-),鐢�,纭曞+鐢�,涓昏鐮旂┒鏂瑰悜涓哄湴鐞嗕俊鎭郴缁熺殑鐮旂┒鍜屽紑鍙戙��
  • 鍩洪噾璧勫姪:

    鍥藉鑷劧绉戝鍩洪噾(40301013)椤圭洰璧勫姪銆�

Abstract:

The spatial interpolation methods of Inverse Distance Weighted (IDW), Spline and Kriging are utilized for comparison study on spatial interpolation of annual average snow depth from 113 observatories in the west of China (79.05°锝�103.57°E,27.17°锝�48.05°N). The principles of these three methods are different from each other. IDW determines cell values using a linear-weighted combination set of sample points. Spline estimates values using a mathematical function that minimizes overall surface curvature. And ordinary Kriging is a powerful statistical interpolation method which assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. Compared with the unsatisfactory interpolation results of IDW and Spline, the result of ordinary Kriging is more close to the real snow depth distribution and can represents the spatial structure of snow depth distribution better. The main reasons which affect the precision are the small number of observatories and their asymmetric spatial distribution. However, the accuracy of spatial interpolation can be improved through reasonable design of sampling, combining deterministic and stochastic methods, and considering the influencing factors of snow distribution such as the terrain and climate.

Key words: Kriging, IDW, Spline, Snow depth, The west of China

鎽樿锛�

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鍏抽敭璇�: Kriging娉�, 鍙嶈窛绂诲姞鏉冩硶, 鏍锋潯鍑芥暟娉�, 绉洩娣卞害, 涓浗瑗块儴鍦板尯

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