A Neural Network Technique for the Retrieval of Land Surface Temperature

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<P ><STRONG style="mso-bidi-font-weight: normal"><FONT face="Times New Roman">A Neural Network Technique for the Retrieval of Land Surface Temperature From Advanced Microwave Scanning Radiometer-EOS Passive Microwave Data Using a Multiple-Sensor/ Multiresolution Remote Sensing Approach<P></P></FONT></STRONG></P>
<P ><FONT size=3><FONT face="Times New Roman"><STRONG style="mso-bidi-font-weight: normal">Kebiao Mao</STRONG>, Jiancheng Shi, Huajun Tang, Qingbo Zhou, Zhao-liang Li, Kunshan Chen,</FONT></FONT></P>
<P ><FONT face="Times New Roman" size=3>It is very difficult to retrieve land surface temperature (LST) from passive microwave remote sensing because a single multi-frequency thermal measurement with N bands has N equations in N+1 unknowns (N emissivities and LST) which is a typical ill-posed inversion problem. However, the emissivity is mainly influenced by dielectric constant which is a function of physical temperature, salinity, water content, soil texture, and other factors (the structure and types of vegetation). These make it almost impossible to develop a general physical algorithm. This paper intends to utilize the multiple-sensor/resolution and neural network to retrieve land surface temperature from AMSR-E data. MODIS LST product is made as ground truth data which overcomes the difficulty of obtaining large scale land surface temperature data.</FONT><A><FONT face="Times New Roman" size=3> </FONT></A><FONT size=3><FONT face="Times New Roman">The mean and the standard deviation of retrieval error are about 2 K and 2.5 K relative to the MODIS LST product when five frequencies (ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used, which can effectively eliminate the influence of soil moisture, roughness, atmosphere and other factors. In addition, we provide an application example and compare it with MOD11_L2 LST_<ST1:CHMETCNV w:st="on" tcsc="0" numbertype="1" negative="False" hasspace="False" sourcevalue="1" unitname="km">1KM</ST1:CHMETCNV>. Finally, we make some validation by using FLUXNET of North American.<P></P></FONT></FONT></P>
<P ><P><FONT face="Times New Roman" size=3> <STRONG>Kebiao Mao</STRONG>, Jiancheng Shi, Huajun Tang, Qingbo Zhou, Zhao-liang Li, Kunshan Chen, A Neural Network Technique for the Retrieval of Land Surface Temperature From Advanced Microwave Scanning Radiometer-EOS Passive Microwave Data Using a Multiple-Sensor/ Multiresolution Remote Sensing Approach, Journal of Geophysical Research-atmosphere. doi:10.1029/2007JD009577, (in press )  </FONT></P><a href="http://www.sciencenet.cn/upload/blog/file/2009/6/200962514182575243.pdf" target="_blank" ><FONT color=#0000ff>PDF download(点击下载)</FONT></A></P>
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<TD class=h vAlign=top align=left>本文引用地址：<a href="http://www.sciencenet.cn/blog/user_content.aspx?id=231556" target="_blank" >http://www.sciencenet.cn/blog/user_content.aspx?id=231556</A> </TD></TR>
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<TD >本文关键词： </TD>
<TD >微波遥感 地表温度 AMSR-E </TD></TR>
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