Luan Fu-Ming, Xiong Hei-Gang, Wang Fang, Zhang Fang
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
College of Art and Science, Beijing Union University, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Oct;33(10):2828-32.
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 x kg(-1) respectively, reaching to significant level. Upon testing, it can be used to estimate the alkaline hydrolysis nitrogen content quickly and accurately.
选取新疆奇台130个土壤样本，分别采用4种小波函数对土壤样本对数反射率的一阶导数光谱进行多层分解，分别运用偏最小二乘回归（PLSR）建立预测模型并检验精度值。结果表明：小波分解的1 - 3层低频系数效果较好，其余较差。在所有函数分解的6层中，低频系数构建的反演模型精度最高的均为ca2，且随着分解层数增加，精度和显著性显著降低。在同一尺度下，4种小波函数低频系数构建的反演模型精度差异不大，其中Bior1.3最优。最佳反演模型是由Bior 1.3构建的ca2，R2和RMSE分别为0.977和7.51 mg x kg(-1)，达到显著水平。经检验，可用于快速准确地估算土壤碱解氮含量。
