基于特征波长选择和建模的高光谱土壤总氮含量估测方法研究

以皖北地区采集的115个砂姜黑土样本为研究对象,获取土壤样本光谱数据,采用竞争性自适应重加权算法(CARS)、连续投影算法(SPA)、随机森林特征选择算法(RFFS)对土壤总氮含量特征波长进行选择,并分别应用偏最小二乘回归(PLSR)、支持向量机回归(SVR)、最小绝对值收缩和选择算子回归(LASSO)建立土壤总氮含量估算模型。结果表明,除CARS-PLSR方法模型精度低于相应的全波长模型外,其他基于选定的特征波长进行建模的效果都优于全波长。综合比较各变量筛选与回归建模组合发现,RFFS方法从全波长(224个波长)中筛选出20个特征波长建立土壤总氮含量的LASSO模型效果最好,该模型在预测集上的决定系数(R²)和相对分析误差(RPD)值分别为0.787 1和2.130 1。RFFS-LASSO模型简单,预测效果好,对土壤总氮含量近地传感器设备开发具有一定的指导意义。

Prediction of soil total nitrogen content from hyperspectral data based on charateristic wavelength selection and modelling

In this paper, a total of 115 lime concretion black soil samples collected from the northern Anhui Plain, China, were used as research objects to obtain hyperspectral data. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and random forest feature selection (RFFS) were used to select the characteristic wavelength of soil total nitrogen content from 224 wavelengths of the hyperspectral data. Partial least square regression (PLSR), support vector regression (SVR), and least absolute shrinkage and selection operator (LASSO) were applied to establish the spectral regression model of soil total nitrogen content. It was shown that all of the wavelength-selecting models outperformed the full-wavelength models except for the CARS-PLSR model. By comparison of all the prediction models built by different combinations of wavelength-selecting methods and regression algorithms with respect to the prediction performance, it was found that the RFFS-LASSO model with 20 characteristic wavelengths got the best prediction results. The coefficient of determination (R²) and relative percent deviation (RPD) value of the model prediction set were 0.787 1 and 2.130 1, respectively. The results illustrated that RFFS-LASSO model was simple and effective for the prediction of soil total nitrogen content, and it had certain guiding significance for the development of proximal sensor of soil total nitrogen content.