A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data

Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.

不同土质下土壤含盐量的高光谱定量反演技术研究

通过内蒙古河套灌区义长灌域野外38个测点的光谱试验，提取反射光谱的盐分指数和吸收光谱的特征参数，研究砂土、砂壤土、壤土土壤合盐量的高光谱定量反演技术。结果表明，针对不同土质，提取最佳盐分指数的组成波段和回归建模的精度都不同；壤土提取最佳盐分指数的组成波段为95lnm和952nm，回归方程精度最高（R＾2adjust为0．88）；砂壤土提取最佳盐分指数的组成波段为670nm和67lnm，回归方程精度最低（R＾2adjust为0．55）；若不考虑土质因素，提取最佳盐分指数的组成波段为651nm和653nm，回归方程的R＾2adjust为0．58。350～593nm波段范围内．吸收峰位置、不对称性与不同土质土壤含盐量的相关性相对较好，但反演不同土质含盐量的精度一般（R＾2adjust最大为0．22），这说明可见光-近红外波段范围含盐量光谱的吸收特征不明显。