Monitoring spatial variability of soil salinity in dry and wet seasons in the North Tarim Basin using remote sensing and electromagnetic induction instruments

Abstract

Soil salinization is one of the most critical eco鄄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鄄temporal variation of soil salinity in response to dry and wet seasons is analyzed by using GIS and geo鄄spatial statistics. Spatial variability of soil salinity is analyzed by Universal Kriging, Spectral Index Regression and Regression鄄Universal Kriging. Our results show that apparent soil electrical conductivity ( EC 1颐5 ) is significantly correlated http: / / www.ecologica.cn with salinity, therefore, EC 1颐5 can be used as a surrogate for salt content. The correlation coefficient between apparent electrical conductivity ( EM38 ) and EC 1颐5 is significance at P < 0. 01, indicates EC 1颐5 can be estimated with multiple regression equation using EM V and EM H 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鄄variance of top soil (0-10cm) . Regression鄄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鄄salinization.

Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta

“…This is mainly because the vegetation cover and growth can reflect the status of soil salinity. In addition, the satellite image detects mainly the vegetation coverage information, so it is hard to directly use the spectrum of bare soil to monitor the soil salinity in the vegetation coverage area on a large scale [12][13][14][15]. When constructing soil salinity inversion model based on satellite imagery, most studies use traditional linear regression methods and machine learning algorithms.…”