基于高光谱的土壤养分含量反演模型研究

RESEARCH OF SOIL NUTRIENT CONTENT INVERSION MODEL BASED ON HYPERSPECTRAL DATA

为实现土壤养分(有机质SOM、全氮TN、全磷TP、全硫TS)含量的快速测定,以建三江创业农场为例,对土壤原始反射率进行了一阶微分(FD)、倒数对数(RL)、倒数一阶微分(FDR)、多元散射校正(MSC)和连续统去除(CR)变换,分析6种光谱变量与土壤养分的相关性,将在α=0.01水平上显著相关的波段作为特征波段,运用多元逐步回归(SMLR)、偏最小二乘回归(PLSR)和BP神经网络(BPNN)三种分析方法分别建立有机质、全氮、全磷和全硫的高光谱预测模型,并利用决定系数(R2)、均方根误差(RMSE)和相对分析误差(RPD)对预测模型进行评价.结果显示,PLSR和BPNN建立的土壤养分含量预测模型均优于SMLR,能极好地预测有机质和全氮含量,同时具有粗略估算全硫含量的能力.三种方法中仅有CR-BPNN能对全磷含量进行粗略估算.对有机质、全氮、全磷和全硫预测效果最佳的模型及其验证集决定系数分别为:MSC-PLSR(0.86)、MSC-PLSR(0.75)、CR-BPNN(0.56)、FDR-BPNN(0.67).

In order to quickly test the soil nutrient contents(SOM,TN,TP and TS),the authors collect 117 soil samples at 0-20 cm depth from Chuangye Farm in Jiansanjiang reclamation area as research objects.First derivative(FD),logarithmic reciprocal(RL),first derivative of reciprocal(FDR),multivariate scattering correction(MSC)and continuum removal(CR)transformations are performed on the raw spectral reflectance(R).By analyzing the correlation between the six spectral variables and soil nutrient content,the bands that are significantly correlated at theα=0.01 level are adopted as characteristic bands,and the methods of stepwise multiple linear regression(SMLR),partial least squares regression(PLSR)and back propagation neural network(BPNN)are used respectively to establish hyperspectral prediction model of SOM,TN,TP and TS.The model is evaluated by R2,RMSE and RPD.The results show that the soil nutrient content prediction models established by PLSR and BPNN are superior to that by SMLR.The PLSR and BPNN methods can well predict the organic matter and total nitrogen content,and roughly estimate the total sulfur content.Only the CR-BPNN method can roughly estimate the total phosphorus content.The models with the best prediction effect on SOM,TN,TP and TS are,respectively,MSC-PLSR,MSC-PLSR,CR-BPNN and FDR-BPNN,with the validation set determination coefficients of 0.86,0.75,0.56 and 0.67 respectively.