基于组合模型的黑土区土壤有机质含量预测分析

Predictive analysis of soil organic matter content in black soil region based on combined model

为提高典型黑土区土壤有机质含量的预测精度,结合田间实测数据与遥感影像反射率数学变换数据筛选出最佳特征波段,并建立多种回归模型,对研究区土壤有机质含量预测模型进行优选。结果表明:对影像反射率进行不同的数学变换处理能够扩大数据中对有机质含量变化敏感的细微吸收特征,突出敏感光谱信息。利用标准化模型对处理后的光谱数据贡献率进行量化,结合相关系数筛选最佳特征波段。建模结果显示,支持向量机模型在检验集上的决定系数为0.89,均方根误差为2.81 g·kg^-1,模型整体的相对分析误差为2.14,对土壤有机质含量的预测能力极好。研究结果可为黑土区土壤有机质含量的预测模型优选提供参考,也可为中国北部地区耕地的有机质含量监测和有效开发提供基础理论依据。

To improve the prediction accuracy of soil organic matter content in typical black soil region via spectral processing and optimization of model methods,partial least squares regression(PLSR)model,back propagation neural network(BPNN)model and support vector machine(SVM)model were established based on the ground measured data and the first-order,second-order differential and principal component analysis data of image reflectance,and the best characteristic band selection and regression prediction of soil organic matter content in the study area were carried out.The results showed that different mathematical transformation of image band could enlarge some fine absorption characteristics of image data and highlight sensitive spectral information.The contribution rate of the spectral data after treatment was quantified by using the PLSR standardized model,and the best characteristic band was screened out along with the correlation coefficient.Among all the established models,the decision coefficient,root mean square error,and relative percent difference of the SVR model on the test set were 0.89,2.81 g·kg^-1 and 2.14,respectively,which exhibited the best performance.The present study could provide a new method for the selection of the best characteristic band in the inversion modeling of soil organic matter content in black soil region,and provide reference for selection of the best soil organic matter content inversion model.Meanwhile,the established SVR model could be used for rapid monitoring of soil organic matter content in typical black soil region,and could provide digital support and theoretical basis for the future effective development of cultivated land.