热带地理 ›› 2019, Vol. 39 ›› Issue (5): 770-779.doi: 10.13284/j.cnki.rddl.003169

• 论文 • 上一篇    下一篇

喀斯特地区春季土壤水分空间插值方法对比

孙智妍, 周秋文(), 张思琪, 韦小茶, 马龙生   

  1. 贵州师范大学 地理与环境科学学院,贵阳 550025
  • 收稿日期:2019-01-05 修回日期:2019-07-12 出版日期:2019-09-10 发布日期:2019-11-08
  • 通讯作者: 周秋文 E-mail:zouqiuwen@163.com
  • 作者简介:孙智妍(1992—),女,辽宁沈阳人,硕士研究生,主要从事喀斯特生态水文过程研究,(E-mail)sunzhiyan139@163.com;
  • 基金资助:
    国家自然科学基金(41761003);地理与环境生态大学生创新训练中心(2016DC3);贵州省科技支撑计划项目(黔科合支撑[2017] 2855)

Comparing and Analyzing Different Spatial Interpolation Methods for Soil-Moisture Estimation in Karst Areas

Sun Zhiyan, Zhou Qiuwen(), Zhang Siqi, Wei Xiaocha, Ma Longsheng   

  1. School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550025, China
  • Received:2019-01-05 Revised:2019-07-12 Online:2019-09-10 Published:2019-11-08
  • Contact: Zhou Qiuwen E-mail:zouqiuwen@163.com

摘要:

以杨眉河小流域为研究区,通过土壤水分采样,选取辅助变量,采用普通克里金、协同克里金、回归克里金3种地统计学方法对土壤水分数据进行空间插值。结果表明:1)回归克里金对研究区土壤水分估算误差最小,其次为协克里金,普通克里金的误差最大;2)普通克里金生成的土壤水分表面最为平滑,而回归克里金最大程度反映了研究区实际的土壤水分空间变化;3)对于协同克里金,以湿度指数(WI)样点数据作为辅助变量的估算误差小于将WI栅格数据作为辅助变量的估算误差。总之,在可获得有效辅助变量的条件下,回归克里金对研究区土壤水分估算的效果优于协同克里金与普通克里金。

关键词: 克里金插值, 回归克里金, 土壤水分, 喀斯特

Abstract:

Most precipitation seeps underground and is difficult to capture because of the poor water-holding capacity of soils in karst areas and dual aspects of the hydrogeological structure formed by karstification. Therefore, soil-moisture estimation is critical for karst ecological restoration in karst area and for agricultural development. Using the Yangmei River basin as the research area, environmental factors, such as Normalized Differential Vegetation Index, Wetness Index (WI), altitude, slope, and relative height, were extracted from remote-sensing imagery and Digital-Elevation-Model data. On the basis of correlation analysis, WI was chosen as an auxiliary variable. Spatial soil-moisture interpolation by four methods, namely ordinary kriging, cokriging with WI vector point as auxiliary, cokriging with WI raster, and kriging combined with regression, were applied to soil-moisture sampling and WI raster data. Root Mean Square Error of predicted and measured values and unary linear regression between predicted and measured values were used to estimate the accuracy of different interpolation methods. Results showed the following: 1) From correlation analysis between soil moisture and other environmental factors, we found a significant negative correlation between WI, which is associated with near surface humidity condition, and soil moisture (P=0.01) but no significant correlation between other environmental factors and soil moisture was found. According to the outcome of stepwise regression, WI was chosen and other environmental factors were removed. 2) Kriging combined with regression had the smallest error of estimation in soil moisture in the study area; cokriging had a larger error, and common kriging had the largest error. When interpolating soil-moisture samples by cokriging, using WI vector-point data was more efficient than WI raster data as the auxiliary variable. 3) The soil-moisture surface generated by ordinary kriging was the smoothest, but kriging combined with regression reflected the soil-moisture spatial pattern for the study area to the greatest extent. However, in general, the spatial variation of soil-moisture interpolation results generated by the four methods was all lower than the actual condition as observed. 4) When fewer soil-moisture sample points were used for interpolation, errors of average value and spatial variation results of the four interpolation methods were larger. However, no matter what number of soil-moisture sample points were used, average values of soil-moisture interpolation results from kriging combined with regression were lower than those of ordinary kriging and cokriging, and spatial variations of soil-moisture interpolation results were higher than those of ordinary kriging and cokriging. In conclusion, the accuracy of kriging combined with regression is better than ordinary kriging and cokriging applied in soil-moisture interpolation in karst areas, which were consistent with the result of other research comparing the accuracy of different interpolation methods. To improve the accuracy of the soil-moisture interpolation results, we would need more soil-moisture sample points and higher resolution remote-sensing imagery to extract more-detailed information on environmental factors, particularly those more significantly correlated with soil moisture.

Key words: Kriging interpolation, Kriging combined with Regression, soil moisture, karst

中图分类号: 

  • S152.7+5