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中文摘要：
为了能够及时、精准、动态地监测盐渍土水分和盐分含量变化，以新疆玛纳斯河流域绿洲农田为研究对象，应用高光谱分析技术，采用偏最小二乘回归方法（PLSR）分析土壤反射光谱特征值与水分、盐分含量间的关系，建立盐渍化土壤水、盐含量的高光谱预测模型，并对模型的稳定性和预测能力进行检验。结果表明：12种数据变换中分别采用CR、（1gR）’能够有效提高土壤盐分、含水率预测模型精度。水分预测模型中土壤盐分含量小于等于8．19dS／m时，Rcal2均大于0．79，外部验证Rval2均大于0．64，RMSEP间差异不显著，预测精度较好；土壤盐分含量大于等于10．25dS／m时，外部验证Rval2不足0．45，预测精度较差。土壤盐分预测模型中当含水率小于15％时，预测Rcal2均大于0．77，外部验证Rval2大于0．64，RMSEP小于4．3，预测精度较好，土壤含水率大于15％时，模型预测精度较差。结果表明土壤中水分、盐分含量较大时，对水盐预测模型的估算精度均会产生影响。
英文摘要：
Taking farmland of oasis in Xinjiang Manas as the example, in order to timely, accurately and dynamically monitor water and salinity of saline soils, the partial-least squares regression （PLSR） for model was applied to model the moisture and salt content of different moistures and salt soils based on hyperspectral analysis technique, the stability and predictive ability of the model was validated. The results show that the prediction precision of soil salinity and moisture were effectively improved through continuum removal （CR） and the logarithm of first order differential （lgR）＇ in 12 kinds of data transformation. The prediction models were better when soil salt content was less than 8.19 dS/m, R2cal were greater than 0.79, REval were greater than 0.64, with no significant difference between RMSEP. The prediction precision was poor when soil salt content was greater than 10 dS/m with R2v~l less than 0.45 in the moisture prediction models. The better prediction accuracy when the moisture is less than 15% , R2cal were greater than 0.77, R2val were greater than 0.64,with RMSEP less than 4.6. The model prediction accuracy was poor when soil moisture greater than 15%. It was concluded that the large soil moisture, salt content will have a significant impact on salt-water prediction model.
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