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An iterative inversion algorithm with application to the polarimetric radar response of vegetation canopies

机译:迭代反演算法在植被冠层极化雷达响应中的应用

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摘要

The retrieval of scene parameters from polarimetric radar data using an iterative inversion approach is considered. The theoretical development of a general, model-based iterative algorithm for inversion of polarimetric radar data is presented. Factors relevant to its implementation, such as sensor configuration, algorithm optimization and computational structure are discussed. The algorithm is applied to the specific problem of inverting the vector radiative transfer model for a simplified, representative vegetation canopy consisting of vertical trunks, leaves, and a rough ground surface. The results of this inversion are in excellent agreement with simulated data generated using the radiative transfer model. The convergence properties of the algorithm are evaluated, and it is found that successful convergence is achieved in about 90% to 95% of the cases tested for the implementation used in this work. An error analysis is presented which considers the effect of both systematic and measurement derived errors. Typical error bounds for the current application are approximately /spl plusmn/3%, allowing for /spl plusmn/0.5 dB accuracy in the measured radar data.
机译:考虑使用迭代反演方法从极化雷达数据中检索场景参数。提出了一种通用的基于模型的极化雷达数据反演迭代算法的理论发展。讨论了与其实现相关的因素,例如传感器配置,算法优化和计算结构。该算法应用于将矢量辐射传递模型反演的特定问题,从而简化了由垂直树干,树叶和粗糙地面组成的代表性植被冠层。反演的结果与使用辐射传输模型生成的模拟数据非常吻合。对算法的收敛性进行了评估,发现在此工作中使用的实现测试的案例中,约有90%至95%成功实现了收敛。提出了一种误差分析,其中考虑了系统误差和测量误差的影响。当前应用的典型误差范围约为/ spl plusmn / 3%,从而在所测得的雷达数据中具有/ spl plusmn / 0.5 dB的精度。

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