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- W3140483423 abstract "土壤盐渍化问题是制约干旱半干旱区植被生长最主要的生态环境地质问题,也是影响绿洲农业生产的障碍性问题。而将遥感与近感技术相结合,是当前评价、监测及预报土壤盐渍化程度的先进方法。以新疆塔里木盆地北缘的渭干河-库车河三角洲绿洲为例,以遥感数据和解译后的电磁感应数据为基础数据源,利用解译后的数据结合GIS和地统计学知识以及野外实测所得到的土壤电导率和盐分资料,分别采用泛克里格(Universal Kriging)、光谱指数回归(Spectral Index Regression)和回归残差泛克里格(Regression-Universal Kriging)3种方法研究了该地区两个关键季节(干季和湿季)土壤盐分的空间变异特征。研究结果表明:研究区的土壤浸提液电导率EC<sub>1:5</sub>和土壤盐分呈现显著相关,可以用EC<sub>1:5</sub>来代替土壤的全盐量进行分析;电磁感应仪(EM38)所测各季节土壤表观电导率与EC<sub>1:5</sub>的相关系数均达到1%显著水平,以表观电导率垂直读数(EM<sub>V</sub>) 和水平读数(EM<sub>H</sub>)为自变量的多元回归模型拟合效果较好;研究区各季节的表层土壤电导率的空间分布均表现为强相关性,说明土壤采样点间的内部结构性良好,采用能够充分考虑到干旱区表层土壤电导率空间变异的尺度依赖性的球状套合模型,能够更好的拟合土壤表观电导率的空间结构;经过精度比较,回归残差泛克里格法为最优预测方法,这表明将遥感和电磁感应技术相结合,能够有效的提高预测与评估土壤盐分空间分布的精度,为精确地进行土壤盐分预测以及土壤次生盐渍化的防控提供了一定的依据。;Soil salinization is one of the most critical eco-environmental problems in arid and semiarid regions, which constrains vegetation growth and, therefore, exerts crop yield and agricultural production. Integrated satellite remote sensing and near sensing technology based on electromagnetic induction instruments is an advanced method for monitoring and forecasting soil salinization. In this contribution, remote sensing and electromagnetic induction EM38 and its mobile sensing system are used to evaluate soil salinity at the delta oasis between the Weigan River and the Kuqa River in the north rim of the Tarim basin. Spatio-temporal variation of soil salinity in response to dry and wet seasons is analyzed by using GIS and geo-spatial statistics. Spatial variability of soil salinity is analyzed by Universal Kriging, Spectral Index Regression and Regression-Universal Kriging. Our results show that apparent soil electrical conductivity (EC<sub>1:5</sub>) is significantly correlated with salinity, therefore, EC<sub>1:5</sub> can be used as a surrogate for salt content. The correlation coefficient between apparent electrical conductivity (EM38) and EC<sub>1:5</sub> is significance at <em>P</em><0.01, indicates EC<sub>1:5</sub> can be estimated with multiple regression equation using EM<sub>V</sub> and EM<sub>H</sub> as independent variables. Soil electrical conductivity in dry and wet seasons in the study region shows strong spatial autocorrelation. Considering the scale dependency of spatial variation, the nested spherical models are fitted for semi-variance of top soil (0-10cm). Regression-Universal Kriging is identified as the best method in terms of the accuracy of predicting spatial variability of soil salinity. We conclude that this study may provide a theoretical reference for rapid and accurate assessment of soil salinity and prevention of soil re-salinization." @default.
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- W3140483423 date "2013-01-01" @default.
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- W3140483423 title "Monitoring spatial variability of soil salinity in dry and wet seasons in the North Tarim Basin using remote sensing and electromagnetic induction instruments" @default.
- W3140483423 doi "https://doi.org/10.5846/stxb201205230766" @default.
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