摘要
[目的]为了探索伽玛能谱数据实现土壤表层(0～30 cm)肥力空间分布预测.[方法]使用偏最小二乘回归(PLSR)、支持向量机(SVM)和反向传播神经网络(BPNN)分别建立土壤伽玛能谱肥力因子模型.[结果]BPNN模型土壤肥力因子预测精度整体要优于PLSR模型和SVM模型;土壤全氮、pH、黏粒和砂粒含量BP神经网络模型预测精度较高,决定系数R2分别为0.564、0.556、0.612和0.626,全钾和全磷含量预测精度较低;研究区土壤全氮、pH、黏粒和砂粒预测空间分布与样本点实际空间分布相比,数值统计特征和趋势均基本一致.[结论]研究区伽玛能谱数据预测土壤全氮、pH、黏粒和砂粒含量空间分布具有一定的可行性,全磷、全钾和粉粒含量无法实现有效预测.
Abstract
[Objective]The aim was to explore the gamma spectrum data to predict the spatial distribution of fertility in the soil surface layer(0～30 cm).[Method]The soil fertility factor models were established by using partial least squares regression(PLSR),support vector machine(SVM)and BP neural network(BPNN).[Result]The soil fertility factors prediction accuracy of BP neural network model was better than partial least squares regression and support vector machine.The prediction accuracy of BP neural network model for total nitrogen content,pH,clay content and sand content were higher,R2 values were 0.564,0.556,0.612 and 0.626,respectively.However,the prediction accuracies for total potassium and total phosphorus contents did not meet expectations.Compared with the actual distribution results of sample points,the prediction results of BP neural network of soil total nitrogen content,pH,clay content and sand content in the study area were basically consistent in numerical statistical characteristics and trends.[Conclusion]It was feasible to predict the spatial distribution of soil total nitrogen content,pH,clay content,and sand content using gamma spectroscopy data in the research area,but total phosphorus,total potassium and silt content could not be predicted.
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