## 6 Conclusions  
By combining EMD and the IMF discriminant method, the modulation and trend terms of the SNR from different satellites were effectively separated. Compared with the traditional low-order polynomial and wavelet analysis, EMD does not require prior knowledge, so it can directly and adaptively decompose the information of different frequencies implied in the SNR data. It was also found that for the SNR of different satellites, the number of layers obtained by EMD decomposition was inconsistent, and the layers were mainly 9-10. Obviously, when separating the trend and the modulation terms, the high number of many decomposition layers increased the difficulties. To solve this problem, the IMF discriminant method was further used to analyze the correlation  
Figure 11: The different assessment results of each method. Top panel: Correlation (\(r\)) between the model test results and the SM reference values. Method 1 and method 2 are represented. Bottom panel: RMSE, MAE, and Max of the model testing. Method 1 and method 2 are represented. between low-frequency and high-frequency components. It could effectively determine the cut-off points of the trend terms and modulation terms for each satellite. When the correlation coefficient threshold \(r_{m}\) corresponding to \(IMF_{T}\) was 0.600, the modulation term of each satellite could be accurately extracted. Thus, for the modulation term separation, the combination of EMD and the IMF discrimination method showed stronger self-adaptability and, thus, has more advantages than the low-order polynomial and wavelet transform. Due to the comprehensive influence of different surface environments and satellite tracks, the response modes of different satellite phases to surface SM changes are inconsistent. The available selection of satellites is the key to the accurate retrieval of SM. The results showed that CCSS could effectively select the available satellites and classify the SNR quality of each satellite to different accuracy levels. Moreover, it reduced the dependence on the SM reference value and achieved an adaptive selection of the satellites. For SM retrieval, the MSER model can fully combine the surface SM information from satellites in different directions, and the model training or the test error is relatively stable. Compared with the single-satellite model, the retrieval accuracy of the multisatellite combination model was effectively enhanced, and the retrieval error of SM in sudden precipitation periods was effectively improved. When the cross-correlation coefficient threshold (\(r_{n}\)) of CCSS was set to 0.700 or above, the correlation between the selected satellite phases and the SM reference values was greater than 0.750. Moreover, these selected satellites also achieved better results after being modeled by MSER, with \(r\) reaching 0.918, which is a value more than 40% higher than that for the single-satellite model. this fully shows that the satellites selected by using CCSS were effective. Therefore, to reduce the modeling complexity, the range of \(r_{n}\) can be set to 0.700, as the cut-off value to select the satellites. Of course, this conclusion is limited to the observation environment selected in this paper. In addition, if higher precision SM retrieval results are required, it is better to combine all satellites with \(r_{n}\) greater than 0.400. In the future, this method will be extended to the application of different satellite navigation systems for different vegetation environments. In this process, the problems of vegetation noise removal and satellite selection for multisatellite and multi-frequency combinations need to be further discussed. [PERSON]. ([PERSON]) and [PERSON] proposed the method; [PERSON]. ([PERSON]), [PERSON] ([PERSON]) and [PERSON] processed the data; [PERSON]. ([PERSON]) and [PERSON] wrote and edited the paper; [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON] and [PERSON] discussed and examined the numerical experiments and final results.