中文摘要：

植被对土壤光谱的干扰是目前土壤盐渍化遥感监测的重要限制因素之一,探索消除稀疏植被覆盖区植被对光谱影响的方法,对提高土壤含盐量遥感反演精度具有重要意义。本文通过对189组不同植被覆盖度且不同盐渍化程度种植微区野外实测地表可见-近红外反射光谱进行分析,比较并评价了基于原始光谱和盲源分离（blind source separation,BSS）后光谱预测土壤含盐量的结果。结果表明：地表植被覆盖严重影响基于可见-近红外反射光谱的土壤含盐量反演精度。盲源分离方法,尤其是基于方程z=tanh（y）的独立分量分析（independent components analysis,ICA）算法,可有效分解植被和土壤的混合光谱,并提高植被覆盖下基于可见-近红外反射光谱的土壤含盐量反演精度。该方法为植被覆盖区大尺度土壤盐渍化遥感监测提供了方法指导。

英文摘要：

The technology of image spectroscopy has been widely used in soil attribute mapping in the past few decades. However,vegetation cover seriously affects the acquisition of soil spectral information,leading to misestimating of soil attributes by visible and near-infrared（vis-NIR）spectroscopy. The traditional solution dealt with vegetation cover interfering soil spectra by masking out the areas with high vegetation coverage,thus resulting in absence of soil information for these areas. Some researchers also tried to use vegetation indices to estimate soil attributes,with results showing that the general applicability and transferability of these vegetation indices was limited by study areas and crop varieties. Therefore,how to remove the influence of vegetation on soil spectrum has become a crucial issue in estimating soil components,such as salt content over partially vegetated surfaces. The residual spectral unmixing method was previously used to separate different components of a mixed spectrum,however,the percentage of each component had to be known as a prerequisite. Recently blind source separation（BSS）,a method previously often used in signal separation analysis,has successfully been applied to separating soil spectral information from vegetation spectral information. In order to verify the effectiveness of BSS,in the Huanghai Raw Seed Growing Farm in Dongtai of Jiangsu,was selected as an experiment site,with its field delineated into plots diversified in soil salt content by amending the soil with salt and vegetated sparsely by seeding in different densities The experiment eventually had a total of 50 plots,5 levels in soil salt content and 10 in sowing density. Then spectra,photos and soil samples of each plot were collected regularly until the soil surface was fully covered by vegetation. A total of 189 groups of field spectral reflectance of the plots various in vegetation coverage,soil salinity and growing season,were analyzed for influences of vegetation on estimation of soil salt content,and ef