摘要:
土壤水分对于全球水循环十分重要,大面积、快速获取土壤水分信息具有重要意义.微波遥感数据可以用于反演土壤水分.以Matlab为平台建立BP神经网络,通过改进BP神经网络的权值、阈值和网络结构,对该算法进行了优化;在研究区范围,分别利用积分方程模型(integral equation model,IEM)、Oh模型、Shi模型生成模拟数据,训练改进的BP神经网络,构建裸露地表土壤水分反演模型,并用野外实测土壤水分数据对模型进行了验证.结果表明,改进后的BP神经网络算法反演精度明显提高,且Shi模型训练网络反演精度较其他2种模型更高,绝对误差为2.47 g/cm3,相对误差仅为7.78%.
Abstract:
Soil moisture is very important for the global water cycle in that the fast obtaining of large area's soil moisture content becomes very significant.Due to the advantages of microwave remote sensing,this technique can be applied to the inversion of soil moisture.In this paper,the authors built the BP neural network based on Matlab and,through improving the neural network' s weights,threshold and the network structure,optimized the BP neural network.According to the measured data of the study area,IEM model,Oh model and Shi model were used to train the neural network so as to build soil moisture retrieval model,and the measured soil moisture content was used to test it.The result shows that the improved BP neural network algorithm obviously improves the inversion results,and Shi model is better than the other two kinds of model in training the network,with its absolute error being 2.47 and relative error being 7.78%.
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