不同覆盖类型土壤电阻率影响因子及其PLS和BP模型的预测研究

Influencing Factors of Soil Electrical Conductivity of Different Coverage Types and Its Prediction Based on PLS and BP Neural Network Model

土壤电导率是反映土壤质量和物理性质的重要参数。本研究通过对试验区不同覆盖类型下土壤温度、含水量及电导率的测试,探讨土壤温度和含水量对土壤电阻率的影响。结果表明,不同土壤覆盖类型土壤温度的变化对土壤电阻率的影响不同,土壤电阻率随着土壤含水量的增加而逐渐变小。将偏最小二乘回归模型(PLS)与BP神经网络模型应用于土壤电阻率的预测,PLS模型及BP神经网络模型对土壤电阻率预测皆有较好效果,偏最小二乘回归模型对沙地和草地土壤电阻率预测的误差较小,BP神经网络对农田土壤电阻率建模精度较为理想。

The soil electrical conductivity is an important parameter to reflect soil quality and physical properties.ln this study,the soil temperature, water content and electrical conductivity of different coverage types of farmland, grassland and sand were tested to explore the effect of soil temperature and water content on soil electrical conductivity. The results showed that different coverage types had different effects on soil electrical conductivity, the soil electrical conductivity decreased gradually with the increase of soil water content. The partial least squares regression （PLS）model and BP neural network model were applied predict soil electrical conductivity, and got good prediction effects. The PLS model had little error in the prediction of soil electrical conductivity of grassland and sand ,and BP neural network model was more ideal for modeling farmland soil electrical conductivity.