基于连续小波变换下的土壤有害元素砷含量估测

与传统检测方法相比，利用高光谱技术进行土壤有害元素砷含量的估算，具有快速、准确，成本低的特点，可对干旱区绿洲土壤有害元素砷污染进行动态监测。基于新疆渭干河-库车河三角洲绿洲耕层土壤样品的采集，获取土壤光谱数据和有害元素砷含量。通过bior1.3，db4，gaus4和mexh这4种小波基函数对土壤原始光谱反射率进行连续小波变换，并将变换后光谱数据与有害元素砷进行相关分析，以筛选出的敏感小波系数为自变量，采用偏最小二乘回归、支持向量机回归、BP神经网络和随机森林回归方法对有害元素砷含量进行高光谱反演。研究结果显示：（1）4种小波基函数在3~8尺度的光谱分解效果明显优于其他尺度，特别是4~6尺度的连续小波变换有效提升了光谱反射率与土壤有害元素砷之间的相关性，通过显著性检验的小波系数数量有了明显增多（p<0.01），在可见光的400~700 nm以及近红外的1 100~1 700和2 200~2 400 nm附近具有较强的相关性；（2）通过比较4种小波基函数对光谱数据中有效信息的辨识能力，认为小波基函数bior1.3和mexh要优于db4和gaus4，其中bior1.3的光谱分解效果最好，gaus4相对最弱；通过bior1.3第5尺度的光谱变换，与土壤有害元素砷显著相关的波段数量最多，为507个（p<0.01）；（3）比较4种建模方法的反演结果发现，SVMR，BPNN和RFR模型相较于PLSR模型具有更强的估测能力，模型的估测精度更高。综合分析各模型的稳定性及估测精度后，认为bior1.3-25-RFR模型可作为研究区土壤有害元素砷的最佳估测模型。该模型的训练集和验证集的R2分别为0.893和0.639，RMSE为1.075和1.651 mg·kg-1，RPD分别为2.89和1.64，表明模型估测效果较好，稳定性较强。采用合适的小波基函数进行连续小波变换可减少土壤高光谱数据中的白噪声，挖掘出土壤光谱数据中的有效信息，对土壤有害元素砷含量的准确估测提供有力的技术保障。

Estimation of Arsenic Content in Soil Based on Continuous Wavelet Transform

Compared with the traditional detection methods, hyperspectral technology has the characteristics of rapid, accurate and low cost in estimating soil heavy metal arsenic content and can dynamically monitor heavy metal arsenic pollution of oasis soils in arid regions. Based on the collection of soil samples from the cultivated layer of the delta oasis of Weigan-Kuqa river in Xinjiang, soil spectral data and heavy metal arsenic content were obtained. Through the four wavelet basis functions bior1.3, db4, gaus4 and mexh, the original spectral reflectance of the soil was subjected to continuous wavelet transformation. The transformed spectral data was correlated with the heavy metal arsenic so that the selected sensitive wavelet coefficients were taken as independent variables, using partial least square regression, support vector machine regression, BP neural network and random forest regression methods to perform hyperspectral inversion of heavy metal arsenic content. The results showed that: (1) The spectral decomposition effect of the four wavelet basis functions at scales 3 to 8 was obviously better than that of other scales, especially the continuous wavelet transform at scales 4 to 6, effectively improved the correlation between the spectral reflectance with soil heavy metal arsenic, and the number of wavelet coefficients passing the significance test increased significantly (p<0.01), and there had a strong correlation in the vicinity of 400~700 nm in visible light and 1 100~1 700 and 2 200~2 400 nm in near-infrared. (2) By comparing the ability of the four wavelet basis functions to identify effective information in the spectral data, it was believed that the wavelet basis functions bior1.3 and mesh were better than db4 and gaus4. Among them, bior1.3 had the best spectral decomposition effect, and gaus4 was relatively weak. Through the spectral transformation of the 5th scale of bior1.3, the number of bands significantly related to soil heavy metal arsenic was the largest, which was 507 (p<0.01). (3) Comparing the inversion results of the four modeling methods, it was found that the SVMR, BPNN and RFR models had stronger estimation capabilities than the PLSR model, and the estimation accuracy of the model was high. After comprehensively analyzing each model’s stability and estimation accuracy, it was concluded that the bior1.3-25-RFR model could be used as the best estimation model for the heavy metal arsenic in the study area. The R2 of the training set and the validation set of the model were 0.893 and 0.639 respectively, the RMSE were 1.075 and 1.651 mg·kg-1, and the RPD were 2.89 and 1.64 respectively, indicating that the model had a better estimation effect and powerful stability. Using appropriate wavelet basis functions to carry out continuous wavelet transform can reduce the white noise in hyperspectral soil data, excavate the effective information in soil spectral data, and provide a strong technical guarantee for accurate estimation of soil heavy metal arsenic content.