准噶尔盆地人工林地土壤全盐的高光谱反演

Total Soil Salinity in Artificial Forest in the Junggar Basin Based on Hyperspectrum

随着高光谱遥感技术的快速发展,通过其定量估测土壤化学成分具有很好的可行性。使用ASD Pro Field-Spec3便携式光谱仪,测量准噶尔盆地人工林地风干土壤样品的可见光-近红外光谱,利用土壤反射光谱值预测全盐的含量。首先,通过皮尔森相关系数分析方法,计算土壤全盐与土壤反射光谱之间的相关性,其中土壤光谱值的二阶导数与土壤全盐的相关系数最高为0.806,均方根误差最小为1.508。其次,在基于光谱反射率的基础上,通过多元统计回归分析,表明土壤光谱在1 130 nm、1 430 nm和1 930 nm波段的全盐反演模型预测的效果较好,可以利用这3个波段建立回归方程,对土壤全盐进行反演估算。

With the rapid development of hyperspectral remote sensing, it is feasible to quantitatively investigate the important chemical components in soil. In this study, the values of NIR-Visible spectral reflectance of soil sam- ples collected from the Junggar Basin in Xinjiang, China were measured with an ASD Pro FieldSpec3 hyperspectral meter so as to predict the total soil salinity based on the spectral reflectance of soil, and provide a theoretical basis for monitoring soil salinization. The Pearson correlation coefficient analysis was used to estimate the correlation be- tween total salinity and spectral reflectance of soil. The results showed that there was a good correlation between soil spectral reflectance and total soil salinity. The highest correlation coefficient between second derivative of soil spec- tral reflectance and total soil salinity was 0. 806, and the minimum root-mean-square error was 1. 508. Through the multiple statistics regression analysis based on spectral reflectance, the performance of inversion model of total soil salinity at wavelengths of 1 130 nm, 1 430 nm and 1 930 nm was good, these three wavelengths could be used to develop a regression equation for inversing the values of total soil salinity.