Comparison of bared soil moisture inversion models based on improved BP neural network
HU Danjuan1, JIANG Jinbao1, CHEN Xuhui1, LI Jing2
1. College of Geoscience and Surveying Enginneering, China University of Mining and Technology, Beijing 100083, China;
2. College of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
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%.
胡丹娟, 蒋金豹, 陈绪慧, 李京. 基于改进的BP神经网络裸露地表土壤水分反演模型对比[J]. 国土资源遥感, 2016, 28(1): 72-77.
HU Danjuan, JIANG Jinbao, CHEN Xuhui, LI Jing. Comparison of bared soil moisture inversion models based on improved BP neural network. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 72-77.
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2016, Vol. 28 
