基于小波分析的土壤碱解氮含量高光谱反演

选取新疆奇台县的134个土壤样本，利用土壤反射率对数的一阶导数光谱分别对四种小波函数进行多层离散分解，采用PLSR方法分别建立了土壤碱解氮含量的反演模型，并对其精度值进行检验。结果表明：小波分解获得的各层低频系数以1～3层较高，而其余各层则较低。所有函数分解的6层中，均以第2层低频系数建模的精度最高，随着分解层数的增加，其精度值和显著性明显降低。相同尺度下，采用四种小波函数的低频系数构建的反演模型的精度差异较小，而Bior1.3为最优函数;基于Bior1.3分解的ca2低频系数建模的R2达0.977，RMSE仅为7.51 mg·kg-1，且为极显著，为最佳反演模型，经检验，可用以快速、准确估算土壤高光谱碱解氮含量。

The Inversion of Soil Alkaline Hydrolysis Nutrient Content with Hyperspectral Reflectance Based on Wavelet Analysis

One hundred thirty for soil samples of Qitai in Xinjiang were selected, and the first derivative spectrum of the soil sample logarithmic reflectance was decomposed to many layers by using 4 wavelet functions respectively, and PLSR was used to establish the prediction models respectively, and precision values were tested. The results show that: 1～3 layers low-frequency coefficients of wavelet decomposition were better, while the rest were worse. In 6 layers of all function decomposition, the highest accuracy of inversion models constructed by low-frequency coefficients were all ca2, while with increasing the decomposition layers, the precision and significance decreased significantly. In the same scale, there was little accuracy difference between inversion models constructed by 4 wavelet functions low-frequency coefficients, while Bior1.3 was optimal. The best inversion model was ca2 that built by Bior 1.3, with R2 and RMSE being 0.977 and 7.51 mg·kg-1 respectively, reaching to significant level. Upon testing, it can be used to estimate the alkaline hydrolysis nitrogen content quickly and accurately.