土壤有机质高光谱反演模型研究

Research on high spectral inversion model of soil organic matter

土壤有机质含量的多少是衡量土壤肥力的一个重要指标。针对传统的土壤养分化学测定方法存在耗时、费力等问题,力图用小波变换、一阶导数、对原始光谱的平均值处理、光谱背景及深度方法对土壤反射值光谱数据进行变换,同时利用人工神经网络构建土壤有机质高光谱反演模型来解决这一问题,结果表明:利用小波变换与原始光谱的平均值处理这两种方法和神经网络结合得到的反演模型有较高的预测精度,其预测值与实测值相关系数达到0.63,且其均方根误差小于0.17,具有实际应用的潜力。

The amount of soil organic matter content is an important index to measure soil fertility.Aiming at the problems of time-consuming and laborious,this paper tries to transform the spectral data of soil reflection value by using wavelet transform,first derivative,mean processing of original spectra,spectral background and depth method,and constructs the high spectral inversion model of soil organic matter by using artificial neural network to solve this problem.The result shows that the inversion model obtained by wavelet transform and the average value of the original spectrum with the neural network has higher prediction accuracy with each other.The correlation coefficient between the predicted value and the measured value is above 0.63 and the root mean square error is less than 0.17,which has the potential of practical application.