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国土资源遥感  2016, Vol. 28 Issue (1): 72-77    DOI: 10.6046/gtzyyg.2016.01.11
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于改进的BP神经网络裸露地表土壤水分反演模型对比
胡丹娟1, 蒋金豹1, 陈绪慧1, 李京2
1. 中国矿业大学地球科学与测绘工程学院, 北京 100083;
2. 北京师范大学减灾与应急管理学院, 北京 100875
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
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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%。

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关键词 青藏高原被动微波遥感Mann-Kendall检验地形因子    
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%.

Key wordsTibetan Plateau    passive microwave remote sensing    Mann-Kendall test    terrain factor
收稿日期: 2014-09-08      出版日期: 2015-11-27
ZTFLH:  TP79  
基金资助:

国家科技支撑计划项目"旱区多遥感平台农田信息精准获取技术集成与服务"(编号:2012BAH29B04)资助。

作者简介: 胡丹娟(1989-),女,硕士研究生,主要研究微波遥感、多光谱遥感在土壤水分反演中的应用。Email:hdjcuomat@126.com。
引用本文:   
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.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2016.01.11      或      http://www.gtzyyg.com/CN/Y2016/V28/I1/72

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