[Quantitative retrieval of soil salinity using hyperspectral data in the region of inner Mongolia hetao irrigation district].
[Quantitative retrieval of soil salinity using hyperspectral data in the region of inner Mongolia hetao irrigation district].
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发表时间：
2009-05
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通讯作者：
Y. Qu;X. Duan;Hong-yong Gao;Ai-ping Chen;Y. An;Jin-ling Song;Hong-min Zhou;T. He
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作者：
Y. Qu;X. Duan;Hong-yong Gao;Ai-ping Chen;Y. An;Jin-ling Song;Hong-min Zhou;T. He
为研究盐渍化土壤的光谱特性及其与高光谱数据的关系，于7月底至8月初在内蒙古河套灌区采集了田间土壤样品。通过对土壤离子和高光谱反射率的统计分析，建立了偏最小二乘回归模型。利用校正模型中未包含的数据进行的独立验证表明，该模型能较好地预测土壤中总离子（S%）、SO4（2+）、pH和K+ + Na+等主要组分，其决定系数（R2）分别为0.728、0.801、0.715和0.734。上述预测值的预测偏差比（RPD）大于1.6，表明校准后的PLSR模型可以作为土壤盐分反演的工具，且结果准确。将PLSR模型的回归系数按LandSat Thematic Mapper（TM）传感器的可见光波段（蓝、绿色和红）和近红外波段进行聚合，得到了一些显著的响应值，表明本文提出的方法可用于星载平台遥感数据的分析。
In the present paper, to investigate the spectral property of salinized soil and the relationship between the soil salinity and the hyperspectral data, the field soil samples were collected in the region of Hetao irrigation, Neimeng in the northwest China from the end of July to the beginning of August. The partial least squares regression (PLSR) model was established based on the statistical analysis of the soil ions and the reflectance of hyperspectra. The independent validation using data which are not included in the calibration model reveals that the proposed model can predicate the main soil components such as the content of total ions (S%), SO4(2+), PH and K+ + Na+ with higher determination coefficients (R2) Of 0.728, 0.801, 0.715 and 0.734 respectively. And the ratio of prediction to deviation (RPD) of the above predicted value is larger than 1.6, which indicates that the calibrated PLSR model can be used as a tool to retrieve soil salinity with accurate results. When the PLSR model's regression coefficients were aggregated according to the wavelength of visual (blue, green and red) and near infrared bands of LandSat Thematic Mapper(TM) sensor, some significant response values were observed, which indicates that the proposed method in this paper can be used to analyse the remotely sensed data from the space-boarded platform.