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光谱指数用于EO-1 Hyperion 数据估算中国黄河三角洲地区土壤盐分
作者姓名:WENG Yong-Ling  GONG Peng  ZHU Zhi-Liang
作者单位:[1]Department of Surveying and Mapping Engineering, College of Transportation, Southeast University, Nanjing 210096(China) [2]State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of the Chinese Academy ofSciences and Beijing Normal University, Beijing 100101 (China) [3]Department of Environment Science, Policy and Management, University of California, Berkeley, CA 94720 (USA) [4]EROS Data Center, U.S. Geological Survey, Sioux Falls, SD 57198 (USA)
基金项目:*1Supported 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) and the National Natural Science Foundation of China (No.30590370).
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.

关 键 词:黄河三角洲地区  土壤盐分  Hyperion  谱指数  EO  中国  估计  高光谱数据
收稿时间:4 August 2009

A spectral index for estimating soil salinity in the Yellow River Delta region of China using EO-1 Hyperion data
WENG Yong-Ling,GONG Peng,ZHU Zhi-Liang.A spectral index for estimating soil salinity in the Yellow River Delta region of China using EO-1 Hyperion data[J].Pedosphere,2010,20(3):378-388.
Authors:WENG Yong-Ling  GONG Peng and ZHU Zhi-Liang
Institution:Department of Surveying and Mapping Engineering, College of Transportation, Southeast University, Nanjing 210096 (China) ;State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University, Beijing 100101 (China);State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University, Beijing 100101 (China);Department of Environment Science, Policy and Management, University of California, Berkeley, CA 94720 (USA);EROS Data Center, U.S. Geological Survey, Sioux Falls, SD 57198 (USA)
Abstract: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 a 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 (SSC of the soil samples ranging from 0.06 to 12.30 g kg-1) 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.
Keywords:hyperspectral reflectance  soil salt content  spectral absorption features
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