## 5 Conclusions  
In this paper, we carried out the research of combining multiple ESLs with HMLs to retrieve high-precision rainfall fields. First of all, we deployed a rainfall detection network for retrieving rainfall fields in Xiamen based on the atmospheric propagation model of ESL and HML. Then, the methods of the OK algorithm and the RBF neural network were given to reconstruct the sparse distribution of rainfall measured by the rainfall detection network to obtain a continuous distribution of rainfall fields in the area. Finally, the HYCELL model and real rainfall fields were used to validate the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area. The experimental results verify the feasibility of combining ESLs and HMLs to accurately retrieve rainfall fields. Detailed conclusions are presented below. 1. For the HYCELL model, the joint network of ESLs and HMLs is able to retrieve the distribution of rain rates in rain cells with high accuracy. The RMSE and MB of the OK algorithm for retrieval of the different rain cells are lower than 0.75 mm/h and 0.14 mm/h, respectively, and the CC of the retrieved results is above 0.985. In contrast, the RMSE and MB of the RBF neural network are lower than 0.69 mm/h and 0.29 mm/h, respectively, and the CC of the retrieved rain cells is higher than 0.994. Moreover, the structural distribution of the rain cells retrieved by the RBF neural network is generally in better consistency with the initial rain cells. 2. For the rainfall from CMORPH, the joint network of ESLs and HMLs can accurately retrieve the rain rates of the real rainfall fields. In particular, the error and correlation of the RBF neural network in retrieving the rain rates from the real rainfall field are better than those of the OK algorithm. However, the performance of the RBF neural network in retrieving the average rain rate is inferior to that of the OK algorithm, and the rain rates would be underestimated for retrieving extreme rainfall. 3. The joint network of ESLs and HMLs also shows a good performance in monitoring actual rainfall event. The results for stratiform rainfall and convective rainfall retrieved by the joint network based on the OK algorithm and the RBF neural network are substantially consistent with the distribution of actual rainfall events, and they show correctly the characteristics of stratiform rainfall and convective rainfall. Moreover, the approach of the RBF neural network performs better for the retrieval of actual rainfall events. The paper only provides a preliminary verification of the feasibility of retrieving rainfall fields by combining ESLs and HMLs through a simulated network. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of ESLs and HMLs to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. Next, we will build multiple real ESLs and HMLs in different topographic areas to form a rainfall observation network and monitor rainfall over a large area in real time. Furthermore, it is worthwhile to develop a study to combine ESLs and HMLs with traditional rain instruments to obtain rainfall fields with higher precision. Conceptualization, Y.Z. and X.L. ; methodology, Y.Z. and X.L. ; software, Y.Z. and M.X. ; validation, X.L. and K.P. ; formal analysis, J.Y. and M.X. ; investigation, X.L. and J.Y. ; resources, Y.Z. and X.L. ; data curation, Y.Z. and X.L. ; writing--original draft preparation, Y.Z. ; writing--review and editing, X.L. and K.P. ; visualization, Y.Z. and J.Y. ; supervision, X.L. ; project administration, X.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 41975030), Excellent Youth Scholars of Natural Science Foundation of Hunan Province of China (Grant No.