针对目前半干旱区存在的土壤盐渍化问题, 以典型半旱区封丘县为研究区, 将遥感与磁感应电导率仪(EM38)技术相结合对典型半干旱区土壤盐分空间变异性进行了研究。 结合田间采样对EM38测量数据进行解译, 并对解译后的数据进行经典数据统计特征分析, 建立了EM38解译后的土壤电导率与光谱指数(土壤指数、 植被指数)之间的回归模型。 利用该模型绘制了研究区土壤盐分的空间分布图。 结果表明, 受内在和外在因子的共同作用, 研究区各土层含盐量均具有中等的变异强度。 采用植被指数与土壤指数和EM38测量相结合的方法, 较好地绘制了研究区土壤盐分的空间变化图。 盐渍化土壤主要集中在研究区北部与南部, 具有明显的趋势效应, 并且在整个剖面盐分表现为表层最高, 随着深度增加盐分先减小后增大的趋势。

Taking Feng-qiu County as a case of soil salinization widely existing in the semiarid region, the spatial variability of soil salinity was investigated by using remote sensing and EM (electromagnetic induction) technologies in the present study. Descriptive statistics was applied to soil salinity data interpreted from EM38 measurements using field sampling method. Spectral indices (soil index and plant index) were derived from 25-resolution Landsat TM image taken in April 2005, and proved to be significantly correlated with soil salinity interpreted by EM38 readings. Regression models were further established between the interpreted soil electrical conductivity and spectral indices (soil index and plant index), and spatial distribution patterns across the study area were finally mapped based on the above regression models. Results indicated that soil salinity at each soil layer is from 0.259 to 0.572 and exhibits the moderate spatial variability owing to compound impact of intrinsic and extrinsic factors. Spatial distribution maps of soil salinity were obtained with the application of plant index, soil index and EM38 measurements. It was shown that soil salinization, mainly located in the north and south of the study area, exhibited obvious trend effect. Salinity at surface soil was the greatest and showed the trend of a decrease at subsoil layer and then an increase at deep layer in the whole soil profile. The accuracy of the predictions was tested using 40 soil sampled points. The root mean square error (RMSE) of calibration for soil salinity in each layer was 0.094, 0.052, 0.071 and 0.067 ds·m-1 respectively, showing that the precision is ideal. The change trends of RMSE were the same as soil salinity in soil profile. The trends indicated that soil salinity had effect on the salinity prediction by spectral indices, and showed better accuracy at low soil salinity.