Recently, the oblique earth-space links (OELs) between satellite and earth station have been used for rainfall monitoring as a supplement to existing observation methods. Most recent studies achieved the rainfall measurement by OELs based on the empirical method such as power-law (PL) model. In practice, two crucial issues need to be addressed: 1) identification of rain and no-rain periods; and 2) determination of attenuation baseline. To solve these problems, this article adopts several machine learning algorithms based on the analysis of earth-space link signal characteristics. For the first issue, we choose the support vector machine as a classifier and the adaptive synthetic sampling algorithm is deployed to eliminate the effects caused by the data imbalance. For the second issue, the long short-term neural network is selected for the determination of attenuation baseline since it has a good ability to solve time-series problem. In terms of the rainfall inversion, we establish a new model by combining the back-propagation (BP) network and genetic algorithm (GA). The PL model is also used as a comparison. To validate the proposed method, we set up an earth-space link that receives the signal from AsiaSat5 in 12.32 GHz. The results demonstrate that the two issues are successfully addressed and the inversion of precipitation is also carried out. Compared to disdrometer, the correlation and mean absolute error of GA-BP model are 0.83 and 1.30 mm/h, respectively, indicating a great potential to use densely OELs for global precipitation monitoring.

中文翻译：

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Ku波段地空链路基于机器学习的降雨监测

最近，卫星和地球站之间的倾斜地球空间链路（OEL）已被用于降雨监测，作为对现有观测方法的补充。最近的研究基于幂律 (PL) 模型等经验方法实现了 OEL 的降雨量测量。在实践中，需要解决两个关键问题：1) 雨期和无雨期的识别；和 2) 衰减基线的确定。针对这些问题，本文在分析地空链路信号特性的基础上，采用了几种机器学习算法。对于第一个问题，我们选择支持向量机作为分类器，并采用自适应合成采样算法来消除数据不平衡带来的影响。对于第二个问题，选择长短期神经网络来确定衰减基线，因为它具有很好的解决时间序列问题的能力。在降雨反演方面，我们结合反向传播（BP）网络和遗传算法（GA）建立了一个新的模型。PL 模型也用作比较。为了验证所提出的方法，我们建立了一个地空链路，以 12.32 GHz 的频率接收来自 AsiaSat5 的信号。结果表明，这两个问题都得到了成功的解决，并且还进行了降水的反演。GA-BP 模型的相关性和平均绝对误差分别为 0.83 和 1.30 mm/h，与 Disdrometer 相比，表明使用密集 OEL 进行全球降水监测的潜力很大。