Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite.
Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite.
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深度神经网络在风云三号卫星微波温湿度探测仪反演大气温湿度剖面中的应用
DOI：
10.3390/s21144673
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发表时间：
2021-07-08
期刊：
影响因子：
--
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
He Q;Wang Z;Li J
浅层神经网络（SNN）是微波遥感大气参数反演的一种常用算法。然而，深度神经网络（DNN）相比SNN具有更强的非线性映射能力，在微波遥感中具有巨大的应用潜力。风云三号卫星上的微波湿度和温度探测仪（中国北京，MWHTS）具有独立反演大气温度和湿度廓线的能力。研究了动态神经网络在微波加热站大气温湿廓线反演中的应用。在微波遥感观测偏差订正和微波遥感大气温湿廓线反演研究中，提出了3种基于DNN的微波遥感大气参数反演方案。实验结果表明，与SNN相比，DNN应用于MWHTS观测时，能获得更好的偏差校正效果，且在3种反演方案中均能获得更高的温湿廓线反演精度。同时，DNN在反演温湿廓线时表现出比SNN更高的稳定性。将DNN和SNN应用于不同大气参数反演方案的对比研究表明，DNN具有更优越的上级性能。
The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.
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发表时间：
2003-02-01
影响因子：
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10.1109/tgrs.2011.2158438
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Boukabara, Sid-Ahmed;Garrett, Kevin;Meng, Huan
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