以新疆艾比湖湿地保护区采集的300个荒漠土壤样品为研究对象, 利用ASD Field Spec○R 3 HR光谱仪获取的土壤可见-近红外光谱数据以及化学分析获取的土壤全磷数据为数据源, 将原始光谱数据经过卷积平滑、 标准正态变量变换以及一阶微分预处理后, 采用蚁群-遗传结合区间偏最小二乘法提取荒漠土壤全磷含量特征波长, 构建土壤全磷含量偏最小二乘回归预测模型; 并与全谱偏最小二乘、 蚁群-区间偏最小二乘、 遗传-偏最小二乘模型进行比较。 结果表明: 经蚁群-区间偏最小二乘法筛选后, 荒漠土壤全磷特征波段为500~700, 1 101~1 300, 1 501~1 700, 1 901~2 100 nm; 进一步采用遗传-区间偏最小二乘法进行变量选择, 得到共线性最小的13个有效波长, 分别为: 1 621, 546, 1 259, 573, 1 572, 1 527, 564, 1 186, 1 988, 1 541, 2 024, 1 118和1 191 nm。 建模方法比较显示, 采用蚁群-遗传结合区间偏最小二乘法选择的特征变量, 建立的模型精度最高, 其次是遗传算法、 蚁群算法和全光谱。 蚁群-遗传结合区间偏最小二乘法建立的土壤全磷含量的模型, 效验证均方根误差RMSECV以及预测集均方根误差RMSEP分别为0.122和0.108 mg·g-1, 效验证相关系数Rc以及预测集的相关系数Rp分别为0.535 7, 0.555 9。 因此, 经过卷积平滑、 标准正态变量变换以及一阶微分预处理, 并利用蚁群-遗传结合区间偏最小二乘法建立的模型不仅简单, 而且具有较高的预测精度和较好的稳健性, 可以估算荒漠土壤全磷含量。

In this paper, 300 samples of desert soil collected in the Ebinur Lake Wetland Nature Reserve of Xinjiang were used as the research subject, and the visible/near-infrared spectra data about the soil obtained with the ASD Field Spec○R 3 HR spectrometer and the data about total phosphorus in the soil obtained through chemical analysis were used as the data sources; following Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment, the combination of ant colony optimization interval partial least squares (ACO-iPLS) and genetic algorithm interval partial least squares (GA-iPLS) were employed to extract the characteristic wavelengths of the total phosphorus content in desert soil, before the partial least squares regression model for predicting the total-phosphorus content in soil was constructed; and this model was compared with the full-spectrum partial least squares model, ACO-iPLS and GA-iPLS. According to the results: through filtering with ACO-iPLS, the total-phosphorus characteristic wavebands in the desert soil were 500~700, 1 101~1 300, 1 501~1 700, and 1 901~2 100 nm; through further variable selection with GA-iPLS, 13 effective wavelengths with the minimum colinearity were selected, which were respectively: 1 621, 546, 1 259, 573, 1 572, 1 527, 564, 1 186, 1 988, 1 541, 2 024, 1 118, and 1 191 nm. According to the comparison of modeling methods, the most accurate model was the one based on the characteristic variables selected with the combination of ACO-iPLS and GA-iPLS, followed by the ones with genetic algorithm, ant colony optimization algorithm and the full spectrum method. For the total phosphorus content in soil model established with the combination of ACO-iPLS and GA-iPLS, the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were respectively 0.122 and 0.108 mg·g-1, and the related coefficient for cross validation (Rc) and the related coefficient for prediction (Rp) were 0.535 7 and 0.555 9, respectively. Therefore, it can be seen that the model constructed through Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment and by using the combination of ACO-iPLS and GA-iPLS has simple structure, high prediction accuracy and good robustness, and can be used for estimating the total phosphorus content in desert soil.