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Remote sensing land surface wetness by use of TRMM/TMI microwave data

机译:利用TRMM / TMI微波数据遥感土地表面湿度

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The water cycle analysis is the most important part of the GEWEX project. In the water cycle analysis, the land surface wetness information plays an important role. TRMM/TMI is a new kind of microwave image unit, and has great potential applicationin land characteristics analysis, especially in remote sensing of land surface wetness information and the monitoring of flood and drought situations. In this study, the wetness index analysis method was used to analysis surface wetness during the summerof 1998 over Boyang and Tongting lake area in China, and we retrieved the land surface emissivity over the same area to estimate the land surface wetness. To accomplish this, we have first studied the TRMM/TMI forward characteristics. By using the VIDSORT model, we developed wetness indexes BWI by combining three window channels of TRMM/TMI. According to our analysis results, the wetness BWI10 are better than the other indexes. So we use the best wetness indexes (BWI10) sensitive to the land surface wetness changes to do our flood classification and monitoring. In our calibration/validation test, the data from the China L-SAR (located on an airplane) and the Canadian Radar-SAR aboard on the Radarsat were used. At the same time we also have tried to retrieve the surface microwave emissivity from the TMI data. We use the emissivity product to estimate the land surface wetness, and we also got a good result. Future work will focus on investigating possible improvements to the algorithm and extending thetesting of the algorithm to other regions.
机译:水循环分析是GEWEX项目最重要的部分。在水循环分析中,地表湿度信息起着重要作用。 TRMM / TMI是一种新型的微波成像单元,在土地特征分析中具有广阔的应用前景,特别是在遥感地表湿度信息和监测旱涝状况方面。本研究采用湿度指数分析法对1998年夏季中国博阳湖和桐庭湖地区的地表湿度进行分析,并取回同一地区的地表发射率,以估算地表湿度。为此,我们首先研究了TRMM / TMI前向特性。通过使用VIDSORT模型,我们通过结合TRMM / TMI的三个窗口通道开发了湿度指数BWI。根据我们的分析结果,湿度BWI10优于其他指标。因此,我们使用对陆地表面湿度变化敏感的最佳湿度指数(BWI10)进行洪水分类和监控。在我们的校准/验证测试中,使用了来自中国L-​​SAR(位于飞机上)和加拿大Radarsat上的加拿大Radar-SAR的数据。同时,我们还尝试从TMI数据中检索表面微波发射率。我们使用发射率乘积来估算陆地表面的湿度,并且也得到了很好的结果。未来的工作将集中于研究对算法的可能改进并将算法的测试扩展到其他区域。

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