资源科学 ›› 2016, Vol. 38 ›› Issue (4): 704-713.doi: 10.18402/resci.2016.04.12

• 土地资源 • 上一篇    下一篇

黄河三角洲土壤含盐量空间预测方法研究

吴春生1,2(), 黄翀1,3(), 刘高焕1, 刘庆生1   

  1. 1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 中国科学院地理科学与资源研究所中国科学院生态系统网络观测与模拟重点实验室,北京 100101
  • 收稿日期:2015-10-19 修回日期:2016-01-08 出版日期:2016-04-25 发布日期:2016-04-25
  • 作者简介:

    作者简介:吴春生,男,山东菏泽人,博士生,主要研究生态GIS和遥感应用。E-mail:wuchsh0118@163.com

  • 基金资助:
    国家自然科学基金项目(41471335;41271407);国家科技支撑计划项目 (2013BAD05B03)

Spatial prediction of soil salinity in the Yellow River Delta based on geographically weighted regression

WU Chunsheng1,2(), HUANG Chong1,3(), LIU Gaohuan1, LIU Qingsheng1   

  1. 1. State Key Lab. of Resources and Environment Information System,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 Ecosystem Network Observation and Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
  • Received:2015-10-19 Revised:2016-01-08 Online:2016-04-25 Published:2016-04-25

摘要:

土壤盐分对农业发展和土地生产力有很大影响,土壤盐碱化会降低耕地质量并造成土地退化,及时了解区域内土壤盐分含量及空间分布很有必要。地理加权回归是一种局部回归预测方法,其利用主变量与环境要素的相关关系,根据空间位置和距离特点,实现主变量的空间扩展。本研究目的即是探索地理加权回归在土壤盐分空间插值中的可用性,并与多元线性回归和协同克里格作对比来检验其精度。地理加权回归模型构建所选择的环境变量包括NDVI,高程和距河流距离。研究结果显示,地理加权回归在土壤盐分空间扩展中效果较好,精度优于其他两种方法(均方根误差为0.305,相关系数为0.649,决定系数为0.421),该方法降低了协同克里格插值的平滑效应,又比多元线性回归结果具有更多的空间细节展示,故本研究认为地理加权回归是一种较好的土壤盐分插值方法。

关键词: 土壤含盐量, 地理加权回归, 环境变量, 黄河三角洲

Abstract:

The content and spatial distribution of soil salinity is closely related to agriculture development and land productivity at a regional scale. It is essential to determine the content and spatial distribution of soil salinity in a timely manner as soil salinization could cause land degradation and influence human lives. Geographically weighted regression (GWR)is a local regression interpolation method that can achieve spatial extension of the dependent variable based on the relationships between the dependent variable and environmental variables and the spatial distances between sample points and predicted locations. GWR has been successfully applied to studies on some soil properties,such as soil organic matter. This study aimed to explore the feasibility of GWR in predicting soil salinity through comparisons with multiple linear regression (MLR)and Cokriging. Environmental factors,including the normalized difference vegetation index (NDVI),elevation and the distances of sample points from the rivers,were selected as auxiliary variables for GWR. The result generated by GWR showed a strong regularity in the spatial distribution of soil salinity,which has an increasing trend from coastal to inland areas,and the values of soil salinity near rivers were smaller than other regions. When compared to the map produced by Cokriging,GWR weakened the smoothing effect and many details became apparent. These findings indicate that GWR is applicable to predicting soil salinity. The prediction accuracy was higher than those of MLR and Cokriging. The RMSE,correlation coefficient,regression coefficient and adjust coefficient were 0.305,0.649,0.572 and 0.421,respectively. In addition,the prediction map generated by GWR reduced the smoothing effect compared to that of Cokriging and showed more spatial details than that of MLR.

Key words: soil salinity, geographically weighted regression, environmental variables, Yellow River Delta