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An integrated methodology for assessing soil salinization,a pre-condition for land desertification
Institution:1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Department of Geography, Ghent University, Ghent 9000, Belgium;4. Sino-Belgian Joint Laboratory of Geo-information, Urumqi 830011, China;5. Sino-Belgian Joint Laboratory of Geo-information, Ghent 9000, Belgium;6. Research Institute of Irrigation and Water Problems, Tashkent 100187, Uzbekistan;1. Desert Research Center, Cairo, Egypt;2. Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, USA;3. Ramakrishna Mission Vivekananda University, Kolkata, India;4. Louisiana State University, Baton Rouge, LA, USA
Abstract:Soil salinization is mainly an arid-zone problem leading to land desertification. It reduces soil quality and limits the growing of crops. The control of this problem involves inventorying, mapping, and monitoring soil salinity, which requires cost-effective, rapid, and reliable methods for determining soil salinity in the field, and rapid, specific data-processing methods. This paper shows the usefulness of an integrated methodology involving a hand-held electromagnetic sensor (Geonics-EM38) and the ESAP (Electrical conductivity or salinity, Sampling, Assessment and Prediction) software for assessing, predicting, and mapping soil salinity. The salinity of a 0.45-ha surface-irrigated plot was analysed by reading the EM38 at 161 locations, and by employing the ESAP software for calibrating the sensor, and predicting and mapping soil salinity at multiple depths. To calibrate the EM38 sensor, the electrical conductivity of the saturation extract (ECe) of 57 soil samples taken at 19 points was measured. The multiple linear regression (MLR) calibration model predicted ECe from EM38 readings with R2 ranging from 0.71 to 0.95 for the multiple-depth profile. Furthermore, the MLR calibration model provided field range average estimates of soil salinity. Fifty-seven percent of the field had ECe values above 4 dS m−1. The salinity levels and distribution in the root zone identified areas with inverted profiles, which revealed drainage problems. The integrated method presented is a breakthrough in the ability to accurately and rapidly assess soil salinity in agricultural lands.
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