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 present 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 paper 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 (SVM) as a classifier and the adaptive synthetic sampling algorithm (ADSYN) is deployed to eliminate the effects caused by data imbalance. For the second issue, the long short-term (LSTM) 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 12.32GHz. 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. Index Terms-rainfall monitoring, remote sensing, machine learning, earth-space link, Ku-band I. INTRODUCTION 1 CCURATE and real-time rainfall measurement plays an important role in many aspects of human life such as agricultural issues, water resource management and natural disaster warning. Existing rainfall detection method mainly comprises rain gauge, weather radar and weather satellite [1]. Based on the exploitation of existing radio spectrum sources, the opportunistic use of microwave links has become a new approach to detecting precipitation. Messer et al. firstly suggested the application of commercial wireless communication networks (CWCNs) to environmental monitoring [2]. In recent years, the use of horizontal microwave links (HMLs) has been developed rapidly in many fields such as path-average rain intensity inversion [3, 4], radar calibration [5] and regional rainfall monitoring [6].

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High-precision rainfall field reconstruction and nowcasting play an important role in many aspects of social life. In recent years, the rain-induced signal attenuation of oblique earth-space links (OELs) has been presented to monitor regional rainfall. In this paper, we set up the first OEL in Nanjing, China, for the estimation of rain intensity. A year of observations from this link are also compared with the measurements from laser disdrometer OTT-Parsivel (OTT), between which the correlation is 0.86 and the determination coefficient is 0.73. Then, the simulation experiment is carried out: an OELs network is built, and the Kriging interpolation algorithm is employed to perform rainfall field reconstruction. The rainfall fields of plum rain season from 2016 to 2019 have been reconstructed by this network, which shows a good agreement with satellite remote sensing data. The resulting root-mean-square errors are lower than 3.46 mm/h and spatial correlations are higher than 0.80. Finally, we have achieved the nowcasting of rainfall field based on a machine-learning approach, especially deep learning. It can be seen from experiment results that the motion of rain cell and the position of peak rain intensity are predicted successfully, which is of great significant for taking concerted actions in case of emergency. Our experiment demonstrates that the densely distributed OELs are expected to become a futuristic rainfall monitoring system complementing existing weather radar and rain gauge observation networks.

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The large-scale monitoring of rainfall is of great significance in the research of meteorology, hydrology, and atmospheric measurement science. In recent years, with the quick development of communication satellite constellation, the use of Earth-space link (ESL) to measure rainfall in the atmosphere is expected to be a potential approach for the largescale monitoring of global rainfall. In this paper, to verify the long-term performance of rainfall measurement using ESL, the data of an ESL at the Ku band and a Thies Laser Precipitation Monitor (LPM) in Nanjing were collected, the rainfall inversion model using ESL was optimized according the height of 0 ℃layer from to the radiosonde data of 10 years, and the inversion results in the different types of rainfall were discussed. The results show that the rainfall inversed by the optimized ESL model are in good agreement with the rainfall measured by LPM (correlative coefficient is 0.985), the relative errors of rain intensity inversed by ESL in light rain, moderate rain, heavy rain, and extreme rain are 20.00%, 15.17%, 8.93%, and 8.99% respectively. The average relative errors (RE) of rain intensity measured by the ESL in convective rainfall and stratiform rainfall are 16.01% and 26.59% respectively.