土壤含盐量与电导率的高光谱反演精度对比研究

探明土壤盐渍化的高光谱遥感监测机理，对改善高光谱遥感监测精度具有重要意义。以南疆地区温宿县、和田县、拜城县的水稻土为研究对象，通过分析土样的高光谱数据和室内测定的盐分与电导率数据，研究了耕作土壤含盐量与电导率的关系，并比较了含盐量和电导率与不同光谱指标的相关性以及二者高光谱反演的精度。结果表明，南疆水稻土的含盐量与电导率的相关性较低，二者之间的关系因地区差异而有较大的变化;含盐量与反射率、一阶微分、连续统去除之间的相关性要优于电导率，特别在一些土壤盐渍化的敏感波段尤为突出;以含盐量建立的多元线性回归、主成分回归、偏最小二乘回归模型的决定系数和相对分析误差均高于电导率。研究表明高光谱信息对土壤含盐量的响应比电导率更敏感，以含盐量为监测指标的高光谱反演精度明显要优于电导率。该结果可为提高土壤盐渍化高光谱遥感监测精度提供理论依据。

Comparative Study on Hyperspectral Inversion Accuracy of Soil Salt Content and Electrical Conductivity

The objective of the present article is to ascertain the mechanism of hyperspectral remote sensing monitoring for soil salinization, which is of great importance for improving the accuracy of hyperspectral remote sensing monitoring. Paddy soils in Wensu，Hetian and Baicheng counties of the southern Xinjiang were selected. Hyperspectral data of soils were obtained. Soil salt content (S_t) an electrical conductivity of 1∶5 soil-to-water extracts (EC_1∶5) were determined. Relationships between S_t and EC_1∶5 were studied. Correlations between hyperspectral indices and S_t, and EC_1∶5 were analyzed. The inversion accuracy of S_t using hyperspectral technique was compared with that of EC_1∶5. Results showed that: significant (p<0.01) relationships were found between S_t and EC_1∶5 for soils in Wensu and Hetian counties, and correlation coefficients were 0.86 and 0.45, respectively; there was no significant relationship between S_t and EC_1∶5 for soils in Baicheng county. Therefore, the correlations between S_t and EC_1∶5 varied with studied sites. S_t and EC_1∶5 were significantly related with spectral reflectance, first derivative reflectance and continuum-removed reflectance, respectively; but correlation coefficients between S_t and spectral indices were higher than those between EC_1∶5 and spectral indices, which was obvious in some sensitive bands for soil salinization such as 660, 35, 1 229, 1 414, 1 721, 1 738, 1 772, 2 309 nm, and so on. Prediction equations of St and EC_1∶5 were established using multivariate linear regression, principal component regression and partial least-squares regression methods, respectively. Coefficients of determination, determination coefficients of prediction, and relative analytical errors of these equations were analyzed. Coefficients of determination and relative analytical errors of equations between S_t and spectral indices were higher than those of equations between EC_1∶5 and spectral indices. Therefore, the responses of high spectral information to St were more sensitive than those of high spectral information to EC_1∶5. Accuracy of St predicted from high spectral data was higher than that of EC_1∶5 estimated from high spectral data. The results of this study can provide a theoretical basis to improve hyperspectral remote sensing monitoring accuracy of soil salinization.