基于改进的BP神经网络裸露地表土壤水分反演模型对比

  本文关键词：基于遗传BP神经网络的主被动遥感协同反演土壤水分，由笔耕文化传播整理发布。

[1] 舒宁.微波遥感原理[M].武汉:武汉大学出版社,2003. Shu N.Microwave Remote Sensing Principle[M].Wuhan:Wuhan University Press,2003.

[2] 李森.基于IEM的多波段、多极化SAR土壤水分反演算法研究[D].北京:中国农业科学院,2007. Li S.Soil Moisture Inversion Model Research of Multi-Band and Multi-Polarization SAR Based on IEM[D].Beijing:Chinese Academy of Agrieultural Sciences,2007.

[3] Oh Y,Sarabandi K,Ulaby F T.Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(6):1348-1355.

[4] Shi J C,Wang J,Hsu A Y,et al.Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data[J].IEEE Transactions on Geoseience and Remote Sensing,1997,35(5):1254-1266.

[5] 田芳明,周志胜,黄操军,等.BP神经网络在土壤水分预测中的应用[J].电子测试,2009(10):14-17. Tian M F,Zhou Z S,Huang C J,et al.Application of BP artificial neural network on prediction of soil water content[J].Electronic Test,2009(10):14-17.

[6] 黄飞.基于AMSR-E和BP神经网络的川中丘陵区土壤水分反演[D].四川农业大学,2012:1-76. Huang F.Soil Moisture Retrieval Using AMSR-E Data by BP Neural Network for Sichuan Middle Hilly Area[D].Sichuan Agricultural University,2012:1-76.

[7] 余凡,赵英时,李海涛.基于遗传BP神经网络的主被动遥感协同反演土壤水分[J].红外与毫米波学报,2012,31(3):283-288. Yu F,Zhao Y S,Li H T.Soil moisture retrieval based on GA-BP neural networks algorithm[J].Journal of Infrared and Millimeter Waves,2012,31(3):283-288.

[8] 林洁,陈效民,张勇,等.基于BP神经网络的太湖典型农田土壤水分动态模拟[J].南京农业大学学报,2012,35(4):140-144. Lin J,Chen X M,Zhang Y,et al.Simulation of soil moisture dynamics based on the BP neural network in the typical farmland of Tai Lake region[J].Journal of Nanjing Agricultural University,2012,35(4):140-144.

[9] 蔡满军,程晓燕,乔刚.一种改进BP网络学习算法[J].计算机仿真,2009,26(7):172-174. Cai M J,Cheng X Y,Qiao G.An improved learning algorithm for BP network[J].The Computer Simulation,2009,26(7):172-174.

[10] 陈思.BP神经网络学习率参数改进方法[J].长春师范学院学报:自然科学版,2010,29(1):26-28. Chen S.Learning rate parameter improve methods for BP neutral network[J].Journal of Changchun Normal University:Natural Science,2010,29(1):26-28.

[11] 高红.BP神经网络学习率的优化方法[J].长春师范学院学报:自然科学版,2010,29(2):29-31. Gao H.Optimal methods of learning rate for BP neutral network[J].Journal of Changchun Normal University:Natural Science,2010,29(2):29-31.

[12] 李翱翔,陈健.BP神经网络参数改进方法综述[J].电子科技,2007(2):79-82. Li A X,Chen J.Summarize of parameter improve methods for BP neural network[J].Electronic Science and Technology,2007(2):79-82.

[13] Hecht-Nielson R.Theory of the backpropagation neural network[C]//Proceedings of the International Joint Conference on Neural Networks.Washington,DC,USA:IEEE,1989:593-605.

[14] Fung A K,Li Z,Chen K S.Backscattering from a randomly rough dielectric surface[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2):356-369.

[15] Pan H,Wang X Y,Chen Q,et al.Application of BP neural network based on genetic algorithm[J].Computer Application,2005,25(12):2777-2779.

[16] Barre H M J,Duesmann B,Kerr Y H.SMOS:The mission and the system[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(3):587-593.

[17] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:136-154. Zhao Y S.Analysis Principle and Method of Remote Sensing Applications[M].Beijing:Science Press,2003:136-154.

[18] Kerr Y H,Waldteufel P,Wigneron J P,et al.The SMOS mission:New tool for monitoring key elements of the global water cycle[J].Proceedings of the IEEE,2010,98(5):666-687.

[19] Kerr Y H,Waldteufel P,Wigneron J P,et al.Soil moisture retrieval from space:The Soil Moisture and Ocean Salinity(SMOS) mission[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(8):1729-1735.

[20] 张玲,蒋金豹,崔希民,等.利用ANFIS方法反演裸土区土壤水分含量[J].国土资源遥感,2013,25(2):63-68.doi:10.6046/gtzyyg.2013.02.12. Zhang L,Jiang J B,Cui X M,et al.ANFIS method to soil moisture inversion in bare region[J].Remote Sensing for Land and Resources,2013,25(2):63-68.doi:10.6046/gtzyyg.2013.02.12.

[21] 余凡,赵英时.ASAR和TM数据协同反演植被覆盖地表土壤水分的新方法[J].中国科学:地球科学,2011,41(4):532-540. Yu F,Zhao Y S.A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas[J].Science China Earth Sciences,2011,54(12):1955-1964.

[22] Bacour C,Baret F,Béal D,et al.Neural network estimation of LAI,fAPAR,fCover,and LAI×Cab,from top of canopy MERIS reflectance data:Principles and validation[J].Remote Sensing of Environment,2006,105(4):313-325.

[23] 高婷婷.基于IEM的裸露随机地表土壤水分反演研究[D].乌鲁木齐:新疆大学,2010. Gao T T.Study on Soil Moisture Inversion of Bare Random Surface based on IEM Model[D].Urumqi:Xinjiang University,2010.

[24] Merzouki A,Bannari A,Teillet P M,et al.Statistical properties of soil moisture images derived from Radarsat-1 SAR data[J].International Journal of Remote Sensing,2011,32(19):5443-5460.

[25] 李芹.青藏高原地区主被动微波遥感联合反演土壤水分的研究[D].北京:首都师范大学,2011. Li Q.Soil Moisture Inversion Research of Qinghai-Tibet Plateau by Passive and Aetive Microwave Remote Sensing[D].Beijing:The Capital Normal University,2011.

  本文关键词：基于遗传BP神经网络的主被动遥感协同反演土壤水分，由笔耕文化传播整理发布。