Hyperspectral inversion of the RF model for soil salinity in oasis tillage layer based on optimal mathematics and wavelet transform
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摘要:
土壤盐渍化会导致土壤结构恶化、作物减产，影响农业生态系统的可持续性，而其监测和管理已成为盐渍化地区亟待解决的问题。为精准识别盐渍化区域以优化农田管理，以新疆渭干河-库车河绿洲耕层土壤为研究对象，测定其光谱反射率并进行数学变换、连续小波变换（continuous wavelet transformation，CWT）、离散小波变换（discrete wavelet transformation，DWT），以及数学变换结合CWT和DWT处理，并基于Pearson相关分析提取与土壤盐分含量（soil salt content，SSC）密切相关的特征波段，从而构建随机森林（random forest，RF）模型定量估算SSC。结果表明：1）在350～
2450 nm范围内，不同盐渍化等级的土壤光谱反射率随SSC增加而升高，但达到极重度盐渍化时反射率降低。2）一阶微分变换、CWT、DWT以及一阶微分结合CWT和DWT处理均能显著提升光谱数据与SSC的相关性，其中DWT效果最优，相关系数最高达−0.621（P＜0.01）。3）一阶微分结合CWT处理显著提升了模型的估测能力，以(1/R)′_CWT_28的全波段构建的估测模型最优，其验证集的决定系数为0.715，均方根误差为17.467 g/kg，相对分析误差为1.56。研究表明数学变换结合小波变换能提升模型的估测效果，可为SSC定量监测提供参考。Abstract:Soil salinization can often result in the continuous accumulation of soluble salt in the surface layer of soil, due mainly to natural and human activities. Saline soil has then posed a serious threat to the soil structure and crop yield in sustainable agriculture. It is very urgent to effectively monitor and manage the soil salinization in saline-alkali areas. Fortunately, hyperspectral imaging can be expected to estimate the soil salinity in recent years, due to its rich spectral information and high resolution. Among them, soil salt content (SSC) is one of the most important indicators to evaluate the degree of soil salinization. This study aims to estimate the soil salinity in the tillage layer of the oasis during hyperspectral imaging. The salinized areas were also precisely identified to optimize farmland management. A total of 193 soil samples were collected from the field in the Weigan-Kuqa River Oasis in Xinjiang, China. The salt content was then measured in the laboratory. Additionally, the spectrum of each soil sample was measured ten times and then averaged to obtain the spectral reflectance. The noisy spectral bands were also removed at the ends of the curves and two water absorption bands, followed by smoothing preprocessing using the Savitzky-Golay method. The spectral data was obtained to further transform using mathematical transformations, continuous wavelet transformation (CWT), and discrete wavelet transformation (DWT). According to Pearson correlation analysis, the spectral bands passing the significance test (P<0.01) were extracted as the SSC characteristic bands. A random forest model was then constructed to quantitatively estimate the SSC. The results indicate that: 1) There was a general consistency in the spectral curve shapes of soils with different salinization levels. There was a great variation in the patterns of reflectance with the wavelength and SSC. As the wavelength increased from the short (350 nm) to the long (2 450 nm), the reflectance increased in the range of 350~800 nm and then remained relatively constant in the range of 800~2 130 nm. While an absorption peak appeared around 2 130 nm corresponding to Na2SO4. After that, the reflectance started to decrease. Furthermore, the reflectance increased with the SSC in the entire range of wavelengths. There was also a positive correlation between them. But there was a decreasing trend at extreme levels of high salinization. 2) First-order differential transformation, CWT, DWT, as well as first-order differential combined with CWT and first-order differential combined with DWT, all significantly enhanced the sensitivity of the spectra to the SSC. Specifically, the DWT was performed the best among them. The highest correlation coefficient between the processed spectra and SSC reached −0.621 (P<0.01), indicating a negative correlation. The performance of the rest was ranked in the descending order of the first-order differential combined with CWT, first-order differential combined with DWT, CWT, and first-order differential transformation. 3) The first-order differential transformation, CWT, DWT, first-order differential combined with CWT, and first-order differential combined with DWT all improved the accuracy and stability of the model. The combination of first-order differential with CWT significantly enhanced the accuracy of SSC estimation. The optimal model used the full-band spectral of (1/R)'_CWT_28, with the coefficients of determination (R2) of 0.821 and 0.715 for the training and validation sets, respectively, the root mean square errors (RMSE) of 16.049 and 17.467 g/kg, respectively, and the relative predictive determinant (RPD) values of 1.50 and 1.70, respectively. As such, the effective SSC estimation was achieved in the study area. Therefore, the mathematical transformations with the wavelet transformation were better utilized to process the smoothed original spectral data. The sensitivity of spectra to SSC was significantly enhanced after processing. Compared with the mathematical or wavelet transformations alone, the combined approach was more effective in quantitatively monitoring the SSC. The finding can also provide a valuable reference to alleviate the soil structure deterioration for agricultural production and environmental protection
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Keywords:
- soils /
- salinity /
- random forest /
- mathematical transformations /
- wavelet transformation
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图 2 各等级盐渍化土壤反射率曲线
Figure 2. Reflectance curves of soils at different salinization levels
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图 3 原始光谱反射率（R）与11种数学变换处理光谱的平均反射率
Figure 3. Original reflectance (R) and average reflectance of spectra processed by 11 mathematical transformations
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图 5 4种连续小波变换和离散小波变换处理方式下小波系数与SSC的相关性
Figure 5. Correlation between wavelet coefficients and SSC under four different continuous wavelet transformation (CWT) and discrete wavelet transformation (DWT) processing methods
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图 6 4种CWT和DWT处理方式下小波系数与SSC正负相关性极值及特征波段数量
注：DS为尺度。
Figure 6. Extremes of positive and negative correlations and NFB for wavelet coefficients and SSC in four CWT and DWT methods
Note: DS is scale.
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图 7 各样本集SSC的描述性统计特征
Figure 7. The descriptive statistical characteristics of SSC in each sample set
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图 8 不同光谱处理方式中最佳模型验证集SSC估算值和实测值散点图
Figure 8. Scatter plot of the estimated SSC and measured values of the best model validation set in different spectral processing methods
表 1 盐渍化各等级样点数量及盐分分布特征
Table 1 The number of samples and the characteristics of salt distribution for soils at each level
盐渍化等级
Salinization grade样点数量占比
Sample size proportion/%土壤盐分含量Soil salt content (SSC)/(g·kg−1) 变异系数
Coefficient of variation/%最小值
Minimum最大值
Maximum中位值
Medium平均值
Mean标准差
Standard deviation非盐渍化Non salinization（NS） 54.92 1.084 7.929 3.476 4.133 1.794 43.4 轻度盐渍化Light salinization（LS） 7.25 8.120 9.461 8.794 8.827 0.471 5.3 中度盐渍化Medium salinization（MS） 15.03 10.150 14.727 10.963 11.621 1.263 10.9 重度盐渍化Heavy salinization（HS） 2.59 16.051 19.607 18.089 17.986 1.395 7.8 极重度盐渍化Very heavy salinization（VHS） 20.21 20.826 207.615 71.400 77.584 46.897 60.5 
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表 2 特征波段数量与相关性值统计
Table 2 Statistics on number of feature bands （NFB）and correlation
变换
Transformation
NFB正相关极值
Positive extremes负相关极值
Negative extremes¯|r| R 643 0.227 −0.160 0.211 1/R 729 0.222 −0.222 0.206 lgR 680 0.225 −0.191 0.199 lg(1/R) 680 0.191 −0.225 0.199 R' 535 0.539 −0.552 0.259 (1/R)' 561 0.585 −0.502 0.261 lg'R 570 0.528 −0.577 0.262 lg'(1/R) 570 0.577 −0.528 0.262 R" 53 0.551 −0.532 0.250 (1/R)" 50 0.566 −0.564 0.255 lg"R 54 0.596 −0.557 0.250 lg"(1/R) 54 0.557 −0.596 0.250
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表 3 SSC估算模型的训练集和验证集结果
Table 3 Training and validation results of the SSC estimation model
变量
Variable光谱处理方式
Spectral processing algorithm训练集Training set 验证集Validation set R2 RMSE/(g·kg−1) RPD R2 RMSE/(g·kg−1) RPD 全波段
Full bandR 0.528 26.267 1.06 0.414 26.335 1.06 (1/R)' 0.656 22.456 1.42 0.547 23.365 1.36 lg'R 0.587 24.418 1.15 0.521 23.009 1.23 lg'(1/R) 0.571 24.921 1.28 0.460 26.051 1.23 R_CWT_27 0.746 19.262 1.81 0.601 22.580 1.54 (1/R)'_CWT_28 0.821 16.049 1.70 0.715 17.467 1.56 lg'R_CWT_28 0.773 18.691 1.65 0.615 20.824 1.48 lg'(1/R)_CWT_28 0.863 14.079 2.36 0.665 19.916 1.67 R_DWT_H9 0.584 24.420 1.20 0.349 28.130 1.04 (1/R)'_DWT_H8 0.780 18.309 1.46 0.588 20.941 1.28 lg'R_DWT_H8 0.795 17.979 1.46 0.616 20.211 1.30 lg'(1/R)_DWT_H8 0.799 17.111 1.73 0.644 19.657 1.51 特征波段
Characteristic bandR 0.544 28.087 0.44 0.505 25.327 0.49 (1/R)' 0.793 18.553 1.36 0.573 21.258 1.19 lg'R 0.736 20.404 1.15 0.521 22.550 1.04 lg'(1/R) 0.743 20.966 1.11 0.487 23.315 1.00 R_CWT_27 0.732 19.565 1.54 0.598 21.144 1.42 (1/R)'_CWT_28 0.713 20.243 1.50 0.705 17.917 1.70 lg'R_CWT_28 0.707 20.843 1.65 0.689 19.593 1.75 lg'(1/R)_CWT_28 0.771 18.150 1.60 0.664 19.106 1.51 R_DWT_H9 0.682 21.287 1.41 0.507 23.790 1.26 (1/R)'_DWT_H8 0.744 19.566 1.60 0.623 20.666 1.51 lg'R_DWT_H8 0.785 17.798 1.52 0.680 18.400 1.47 lg'(1/R)_DWT_H8 0.755 19.001 1.52 0.634 19.878 1.45
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