黄绵土风干过程中土壤含水率的光谱预测

以2014年两次在陕西省乾县田间采集的129个黄绵土土壤样本为研究对象,建立土壤含水率定量反演模型。在土壤风干过程中测量光谱反射率及含水率,分析土壤含水率与光谱反射率之间的关系,并利用一元线性及指数回归建立土壤含水率光谱预测模型。结果表明在400~1 340、1 460~1 790、1 960~2 390 nm波长范围内,与含水率相关性最大的反射率对应的波长分别为570、1 460、1 960 nm;吸收深度最大的波长位于490、1 460、1 960 nm。土壤光谱特征指标与含水率之间的线性相关关系优于指数相关关系。以特征波长1 980 nm(C1980)、1 980 nm的吸收深度(D1980)和1 480 nm的吸收深度(D1480)为自变量建立的线性模型为土壤含水率预测的最优模型,校正和验证的决定系数R2大于0.92,相对预测偏差(RPD)大于2.5,均方根误差(RMSE)小于2.5%。研究表明利用自然土样,在风干过程中进行土壤含水率光谱快速预测是完全可行的,从而为遥感实时、快速监测土壤水分含量及大面积土壤水分反演提供了参考。

Prediction of Soil Moisture Content in Air-drying Loess Using Spectral Data

129 loess soil samples taken from the field in Qian County of Shaanxi Province in 2014 were chosen as objects to build the inversion model between soil moisture content and spectra. The spectra and gravimetric moisture content of soil samples were measured during the process of soil air drying, and the relationship between spectra and soil moisture content was analyzed. The spectral predictive models of soil moisture content were established by using the linear regression and exponential analysis. Results showed that the biggest correlation coefficients and absorption depth bands located in 570, 1460, 1960nm and 490, 1460, 1960nm in the region of 400~1340, 1460~1790, 1960~2390nm, respectively. The linear relationship between spectral characteristic indexes and moisture content was better than the index relationship. The linear models were optimum models for predicting moisture content of loess by using characteristic band (C1980) and absorption depth (D1980 and D1480) as independent variables. The calibration and validation coefficient of determination R2 and residual prediction deviation (RPD) were higher than 0.92 and 2.5, respectively, and the root mean square error (RMSE) was less than 2.5%. These results showed that the moisture content of natural soil samples can be predicted rapidly by using spectral reflectance during the soil drying process. The study can provide a reference for real-time and rapid soil moisture content monitoring and soil moisture quantitative inversion in large area by using remote sensing technology.