摘要
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.
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 2 052 and 2 203 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.
基金
Supported by the Open Foundation of State Key Laboratory of Remote Sensing Science,the Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University (No.2009KFJJ002)
the National Natural Science Foundation of China (No.30590370)
