| Technology Application |
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Research on Quantitative Remote Sensing of Soil Salinization in the Arid Area Based on Electromagnetic Induction |
| LI Xiao-ming1,2, YANG Jing-song1, YU Mei3, YANG Qi-yong4, LIU Mei-xian1 |
1. Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;
2. Shaanxi Estate Development Service Corporation, Xi’an 710075, China;
3. Yuhua District Water Resources Bureau, Nanjing 210012, China;
4. Institute of Karst Geology, CAGS, Guilin 541004, China |
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Abstract For the sake of quantitative remote sensing study of soil salinization in the typical arid area, Landsat 7 ETM+ image of the typical arid area in South Xinjiang was obtained. The land use type of farmland was extracted by decision tree classification. The correlation between soil salinization, etectromagnetic induction data and spectrum characteristics was analyzed by mobile electromagnetic survey and extraction of spectrum characteristics in farmland. On such a basis, a quantitative inversion model of soil salinization was obtained. Some results have been obtained: the land use classification has a favorable accuracy with a total precision of 93.75% and a Kappa coefficient of 0.9154; multiple regression indicates that there exists significant correlation between soil salinization detected by the mobile electromagnetic survey and DVI (Difference Vegetable Indice), B2 (the value of band 2 of ETM+ images) and RVI (Ratio Vegetable Index), and that the inversion model of soil salinization can be used to identify salinized soils quantitatively. Results from 89 verification points show that, although the quantitative inversion accuracy of remote sensing is a little lower than that of geo-statistics analysis based on electromagnetic induction, the correlation between the inversion values and the measured values is favorable, and the accuracy is acceptable. Thus the means put forward in this paper is an rapid and effective technology for large-scale soil salinization monitoring.
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| Keywords
Spatial statistics
Spatial autocorrelation
Spatial model
Population density
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Issue Date: 07 March 2012
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2012,
Vol. 24
