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以宁夏平罗县为研究对象,将Unispec-SC便携式光谱仪测得的盐渍化光谱数据和实验室测得的土壤含盐量数据作为基础数据源。运用高光谱数据处理方法,分析不同盐渍化地区植被的光谱特征曲线;对实测植被、土壤光谱曲线进行对数、均方根和一阶微分等变换,筛选与土壤含盐量相关性最好的变换形式和特征波段构造盐分指数SI及多种植被指数,利用多元非线性回归分析建立土壤盐渍化遥感监测模型。结果表明:土壤、植被光谱一阶微分变换与土壤含盐量响应敏感;协同盐分指数SI和植被指数MSAVI构造的土壤盐渍化指数模型,模拟值和实测值相关系数达到0.758 9,模拟效果很好,实现快速提取该区域的土壤盐渍化信息。
Taking Pingluo County of Ningxia as the research object, the data of salinization measured by Unispec-SC portable spectrometer and the soil salinity measured by laboratory were taken as the basic data source. The hyperspectral data processing method was used to analyze the spectral characteristics of the vegetation in different salinization areas. The logarithm, root mean square and first-order differential transformations of the measured vegetation and soil spectral curves were used. The correlation between the screening and soil salt content was the best The transformation form and characteristic band constructed salt index (SI) and multiple vegetation indices, and established the remote sensing monitoring model of soil salinization using multivariate nonlinear regression analysis. The results showed that the first-order differential transformation of soil and vegetation was sensitive to the response of soil salinity; the correlation coefficient of simulated and measured values was 0.758 9, with the synergistic salinity index (SI) and vegetation index (MSAVI) Well, to quickly extract soil salinization information for this area.