土壤有机质含量高光谱估测模型构建及精度对比

Hyperspectral Estimation Model Construction and Accuracy Comparison of Soil Organic Matter Content

土壤有机质含量对作物的生长发育有着显著影响。为实现对苹果果园土壤有机质含量快速、实时估测,本研究以山东省烟台市栖霞市苹果园为研究区,采集100个土壤样本,利用ASD FieldSpec3便携式地物光谱仪获取其高光谱反射率,利用定量化学方法测定土壤有机质含量。采用移动平均法对高光谱数据进行预处理,分析果园土壤的反射光谱特征,研究光谱反射率与其有机质含量的相关关系,筛选土壤有机质含量的敏感波长并构建光谱指数后,分别建立多元线性回归模型(MLR)、支持向量机(SVM)和随机森林(RF)模型,并对模型精度进行验证比较。结果表明,筛选出的土壤有机质含量的敏感波长为678、709、1931、1939、1996和2201 nm。用筛选出的波长构建光谱参数,最终构建的光谱指数分别为NDSI(678,709)、NDSI(678,1931)、NDSI(678,2201)、NDSI(709,1939)和NDSI(1939,2201)。建立的MLR、SVM和RF回归模型中,以RF模型精度最优,其校正样本集R2为0.8804,RMSE为0.1423,RPD达到2.25;验证模型的R2为0.7466,RMSE为0.1266,RPD为1.79,建立的RF定量模型反演苹果果园土壤有机质含量效果较好。因此,可以利用RF方法快速预测苹果果园土壤有机质含量,了解土壤养分分布状况,指导农民合理施肥,从而提高果园生产管理效率。

Soil organic matter(SOM)is an important source of crop growth,its content can reflect soil fertility status.In order to realize the fast and real-time estimation of the SOM,based on hyperspectral data,a rapid estimation model of SOM content in orchards was established.A total of 100 brown soil samples were collected from the apple orchard of Qixia county,Yantai city,Shandong province.After drying and grinding,the hyper-spectrum of the soil was measured in the laboratory using ASD Field‐Spec.The spectral data was preprocessed by the method of moving average,and the spectral reflectance features of orchard soil were analyzed to study the correlation between spectral reflectance and its soil organic matter content.In order to enhance the correlation between relevant spectral parameters and soil indexes,the original data were processed by using the multivariate scattering correction,the first derivative and the first derivative of MSC.After the sensitive wavelengths of soil organic matter content were selected and the spectral indexes were constructed.Multiple linear regression models(MLR),support vector machines(SVM)and random forest(RF)models were respectively established.The estimation accuracy of the orchard soil organic matter estimation model was measured by the determination coefficient(R2),root mean square error(RMSE)and relative analysis error(RPD).The sensitive wavelengths of soil organic matter content selected were 678,709,1931,1939,1996 and 2201 nm.The spectral parameters were constructed using the selected wavelengths,which were NDSI(678,709),NDSI(678,1931),NDSI(678,2201),NDSI(709,1939),and NDSI(1939,2201).These models established include MLR,SVM and RF model.The RF model had the best precision.The calibration sample R2 was 0.8804,the RMSE was 0.1423 and RPD reached 2.25;the R2 of the verification model was 0.7466,the RMSE was 0.1266,and the RPD was 1.79.The results showed that the fitting effect of the hyperspectral inversion model based on RF regression analysis was better than that based on MLR analysis and SVM regression analysis.As a promising and effective method,RF can play a vital role in predicting soil organic matter.The results can help understanding the distribution of soil nutrients,guiding farmers to apply fertilizer reasonably and improving the efficiency of orchard production and management.