监测采样间隔对作物模型性能的影响研究

Effect of Different Monitoring and Sampling Intervals on Crop Model Performance

为了获得不同监测采样间隔对作物生长模型的影响规律,避免现有研究和应用中存在的主观性或随意性,以番茄叶片蒸腾速率和CO2交换率为对象,采用3种采样间隔数据及2种常用方法进行建模、预测和比较研究。研究结果显示,在15、30、60 min的监测采样间隔下,番茄叶片蒸腾速率模型和CO2交换率模型的预测误差是不同的,GA-BP神经网络模型的预测能力普遍优于纯二次回归模型,但两者的结论是一致的,即对蒸腾速率和CO2交换率而言,最合适的监测采样间隔分别为30 min和15 min。本研究结果为监测采样间隔的设定、试验数据的选取和建模方法的运用提供依据,对作物模型研究和应用具有重要意义。

In order to obtain the impact of different monitoring and sampling intervals on crop modelperformance,and avoid the subjectivity and randomness in the existing research and application,thetranspiration rate and CO2 exchange rate of tomato leaf were modeled by two common methods with three kindsof sampling intervals,then the prediction error of these models were compared.The results showed that theprediction errors of transpiration rate model and CO2 exchange rate model were different respectively with thesampling intervals of 15,30 and 60 minutes,the prediction ability of the GA-BP neural network model wasgenerally superior to the pure quadratic regression model,but the conclusions were consistent,showing that themost appropriate sampling interval was 30 minutes for transpiration rate model and 15 minutes for CO2 exchange rate model respectively.The results of this study provided the basis for the setting of monitoring andsampling intervals,the selection of experimental data and the application of the modeling methods,which wasof great significance for the research and application of crop model.