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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Inversion of Spaceborne X-Band Synthetic Aperture Radar Measurements for Precipitation Remote Sensing Over Land
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Inversion of Spaceborne X-Band Synthetic Aperture Radar Measurements for Precipitation Remote Sensing Over Land

机译：用于陆地降水遥感的星载X波段合成孔径雷达测量值的反演

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摘要

Several spaceborne X-band synthetic aperture radar (X-SAR) systems were launched in 2007, and more will be launched in the current decade. These sensors may significantly augment the sensors that comprise the Global Precipitation Mission (GPM) constellation. X-SAR rainfall measurements may be beneficial particularly over land where rainfall is difficult to measure by means of satellite microwave radiometers. Inversion techniques to quantitatively derive precipitation fields over land at high spatial resolution are developed and illustrated in this paper. These inversion algorithms are the model-oriented statistical (MOS) methodology and the Volterra integral equation (VIE) approach. Simplified rain-cloud models are used to train and test the inversion algorithms by evaluating the expected error budget. Two case studies, using data obtained from measurements of SIR-C/X-SAR in 1994 over Bangladesh and the Amazon, are introduced, and retrieved precipitation maps are discussed. Even though no validation of the precipitation estimates was possible, the obtained results are encouraging, showing physically consistent retrieved structures and patterns.
机译：2007年发射了几种星载X波段合成孔径雷达（X-SAR）系统，并且将在当前十年中发射更多的系统。这些传感器可能会大大增强组成全球降水任务（GPM）星座的传感器。 X-SAR降雨测量可能特别有益于在难以通过卫星微波辐射计测量降雨的土地上。本文开发并举例说明了反演技术，该技术可用于以高空间分辨率定量导出陆地上的降水场。这些反演算法是面向模型的统计（MOS）方法和Volterra积分方程（VIE）方法。通过评估预期的误差预算，简化的雨云模型用于训练和测试反演算法。介绍了两个案例研究，利用从1994年孟加拉国和亚马逊地区SIR-C / X-SAR的测量获得的数据，并对获取的降水图进行了讨论。即使无法对降水估算进行验证，所获得的结果还是令人鼓舞的，显示出物理上一致的检索结构和模式。

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