关键词: 克立格法, 半变异函数, 土壤水分, 距离反比法

Abstract:

In situ observations of soil moisture are invaluable means in developing land surface parameterization and studying pattern of climate change. However, the existing observations have been done only at point scale. Hence as to how to get regional soil moisture is of especially important. In this paper, geostatistical method (Kriging) is used to estimate soil moisture unknown at site A based on known soil moisture data around A. The data set of soil humidity in the top 1 m of 102 agrometeorological stations over China in 1987 is used for the estimation. In order to test how well the method works, we estimate one station's soil moisture as unknown by using other station's data. The observational data from that station is then taken as the true value. We gave the RMSE of Kriging interpolation method. and compared it with inverse distance square method The accuracy of the estimation is not high in terms of average relative error and standard deviation index For further analysis ,we took fperiod which had the maximal samples as an example.The average relative error of both methods was 0.26, the standard deviation of Kriging was 8.77 , the standard deviation of 5.17. The better results of the latter method was maybe due to itshomogenization over all the data with difference between the maximum and the minimum observed soil moisture being 42.85 that by geostatistical method being 31.25 and that by inverse interpolation method being 24.09. It is suggested that the combination of the two estimation methodsmay give better results. The inverse distance interpolation method is suitable for data with general variation characteristicswhile the geostatistical method is good for regional variable tendency. The average range calculated in this paper, around 500 km, is in agreement with the result of Entin et al. (2000) from 49 stations and 11 year records and Liu et al. (2001) with 99 stations with 3 year records.

Key words: Kriging, semivariogram, soil humidity, inverse distance square