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土壤盐渍化是造成干旱区土地荒漠化及生态恶化的重要原因,及时获取大尺度高精度土壤盐渍信息是防治工作的基础。选取新疆塔里木盆地北缘渭干河—库车河流域三角洲绿洲为研究区,利用Lansat-TM数据与野外实测数据分析盐渍化土壤与修改型土壤调整植被指数(MSAVI)、湿度指数(WI)之间的关系,在此基础上提出了MSAVI-WI特征空间概念,构建了土壤盐渍化遥感监测指数模型(MWI)。结果表明:MWI与土壤表层含盐量相关性较高,其相关性为0.844,精度高于土壤盐渍监测常用的盐分指数与实测数据的相关性。MWI能较好的反映盐渍化土壤地表植被及土壤水分的组合变化,具有明确的生物物理意义,并且特征参量简单,理论上易于理解,实践上易于实现,MWI模型的构建有利于干旱区大尺度土壤盐渍化定量监测与评价工作的开展。
Soil salinization is one of the important causes of land desertification and ecological deterioration in arid areas. It is the basis for prevention and control to timely obtain large-scale and high-precision soil salinity information. Using the Lansat-TM data and field data, we analyzed the relationship between salinity soil and modified soil adjusted vegetation index (MSAVI), humidity index (WI) On the basis of this, the concept of MSAVI-WI feature space was proposed and a model of soil salinization remote sensing monitoring index (MWI) was constructed. The results showed that there was a high correlation between MWI and soil salinity, and the correlation coefficient was 0.844. The accuracy of MWI was higher than that of salt index commonly used in soil salinization monitoring and the measured data. MWI can better reflect the change of the combination of surface vegetation and soil moisture in salinized soils, and has clear biophysical significance. In addition, MWI can be easily understood theoretically and easily in theory, and MWI model is conducive to large Scale soil salinization quantitative monitoring and evaluation work carried out.