| 基于散射计波浪参数反演模型的改进算法实验 |
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| 引用本文： | 过杰,何宜军,张彪,Vladimir Yurjevich Karaev,M.A.Panfilova,Yuriy Titchenko.基于散射计波浪参数反演模型的改进算法实验[J].遥感技术与应用,2016,31(5):907-911. |
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| 作者姓名： | 过杰 何宜军 张彪 Vladimir Yurjevich Karaev M.A.Panfilova Yuriy Titchenko |
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| 作者单位： | (1.中国科学院烟台海岸带研究所山东省海岸带环境过程重点实验室,山东 烟台 264003;;
2.南京信息工程大学海洋科学学院,江苏 南京 210044;;
3.Department of Hydrophysics,Institute of Applied Physics,Russian Academy of Science;
Nizhny Novgorod,Russia,46603950) |
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| 基金项目： | 中国科学院与乌俄白国际合作项目“理论数字模拟海况对散射计海面风场反演精度的影响”(Y429011031),国家自然科学基金委员会与俄罗斯基础研究基金会合作交流项目“利用SAR数据分析关于雷达后向散射及风场反演的影响因子”(4141101049),国家自然科学基金委面上项目“多波段极化合成孔径雷达岸高风场反演研究”(41176160)。 |
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| 摘 要： | 一种新的利用散射计(ERS1/2)数据反演有效波高和平均周期的模式被提出。通过俄罗斯学者利用浮标数据建立完全成长风浪条件下有效波高与风速之间的关系,与匹配浮标观测的有效波高数据对比,区分完全成长风浪、成长风浪和涌浪3种海况下的匹配数据;利用BP神经网络建立模式反演3种海况下的有效波高,均方根误差分别为0.53、0.57和0.86m,反演平均周期均方根误差分别为0.69、1.04和1.36s。这种反演方法在完全成长风浪海况下最好,依次是成长风浪和涌浪海况。该研究为散射计数据反演波浪参数提供了依据,使大面积反演波浪参数成为可能。
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| 关 键 词： | 有效波高 平均周期 BP神经网络 散射计(ERS1/2)数据 浮标数据 |
Based on Wave Parameters Inversion Model Experiment by Scatterometer Data Improved Dlgorithm |
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| Guo Jie,He Yijun,Zhang Biao,Vladimir Yurjevich Karaev,M.A.Panfilova,V.Yuriy Titchenko.Based on Wave Parameters Inversion Model Experiment by Scatterometer Data Improved Dlgorithm[J].Remote Sensing Technology and Application,2016,31(5):907-911. |
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| Authors: | Guo Jie He Yijun Zhang Biao Vladimir Yurjevich Karaev MAPanfilova VYuriy Titchenko |
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| Affiliation: | (1.Shandong Provincial Key Laboratory of Coastal Environmental Processes,Yantai Institute of;
Coastal Zone Research,Chinese Academy of Sciences,Yantai 264003,China;;
2.School of Marine Sciences,Nanjing University of Information;
Science & Technology,Nanjing 210044,China;;
3.Department of Hydrophysics,Institute of Applied Physics,Russian Academy;
of Science,Nizhny Novgorod,Russia,46603950) |
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| Abstract: | A new model is proposed to estimate the significant wave height and average period with ERS-1/2 scatterometer data.The relationship between the wind speed and significant wave height in full developing wind\|wave domination is established by Russian scholars.Through this formula,the data that based on the ERS1/2 data and the NDBC buoys data matching were distinguished into three states:developing wind\|wave,full developing wind\|wave and swell wave,respectively.The significant wave heights and average period are retrieved by Back Propagation neural network,the root mean square is 0.53,0.57,0.90 m and 0.69 s,1.04 s,1.36 s in three states,respectively.This method inversion significant wave height and period is found that the full developing wind\|wave domination is best effect,in turn,is the developing wind\|wave,the last is swell wave.The study provides the basis for scatterometer data inversion of wave parameters.It makes the biggest possible inversion wave parameters in large area. |
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| Keywords: | Significant wave height Average period Back Propagation(BP) neural network ERS1/2 data Buoys data |
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