## 5 Discussion on Potential Applications of the Data Products
### Spaceborne GNSS-R from Different GNSS Constellation and Frequency Bands  
One of the main features of the complex waveform products is their multi-GNSS and multi-frequency feature. Indeed, the development of new GNSS constellations (e.g., Galileo and BDS-3) can significantly increase the number of available sources of signal of opportunity, and thus can improve by folds the spatial-temporal sampling efficiency of spaceborne GNSS-R systems. However, most of the GNSS-R missions can only provide their Level 1 products for GPS L1 C/A signal. The unprocessed nature of the raw IF data makes it possible to extract GNSS-R measurements from all available GNSS signals within the receiver bandwidth. Specifically, the Galileo E1 open service (OS) signal and the BDS-3 BIC signal share the same frequency band with the GPS L1 C/A signal, and thus the corresponding complex waveforms are also generated. In addition, the reflected  
Figure 5: An example scheme for accessing, searching and analyzing the complex waveform products. signals with different carrier frequencies may provide additional geophysical information comparing to these from only the L1/E1/B1C band. Figure 6 shows a set of examples of the power waveforms obtained from different GNSS systems and frequency bands. The upper row corresponds to the power waveforms of reflected GPS L1 C/A, Galileo E1 B/C and BDS B1C signals, using the corresponding publicly available PRN codes. The bottom row corresponds to the power waveforms of reflected GPS L2C (1227.60 MHz) and BDS-3 B1I (1561.098 MHz) signals together with these of reflected L1 C/A and B1C signals as the references. The presented results show that the raw IF data processing software is capable of adequately processing the reflected signals from different GNSS constellations and at different frequency bands. The multi-GNSS and multi-frequency features of the complex data product can facilitate the community in the following explorations:  
* As the outputs of the raw IF data processing, the prototype receiver processing algorithms (e.g., the combination of these GNSS signals with multiplexing structures) have been applied to the reflected signal samples. Thus, the complex waveforms themselves cannot be used directly for the developments of low-layer signal processing algorithms for multi-constellation, multi-frequency GNSS-R. However these intermediate results (e.g., code phases and carrier frequencies/phases of the reflected signals) provided in the data product can facilitate the users to re-process the raw IF data by using  
Figure 6: Examples of GNSS-R waveforms processed from multi-mission Spaceborne Raw IF data sets. (**a**) Normalized power waveform of reflected GPS L1 C/A signal (GPS PRN 16), the raw IF data is collected by CYGNSS FM04 on 17 November 2020. (**b**) Normalized power waveform of reflected Galileo E1 BC signal (Galileo PRN 14), the raw IF data is collected by CYGNSS FM08 on 13 October 2018. (**c**) Normalized power waveform of reflected BDS-3 B1C signal (BDS-3 PRN 30), the raw IF data is collected by CYGNSS FM07 on 11 October 2018. (**d**) Comparison between normalized power waveforms of reflected GPS L1 C/A and L2C signals (GPS PRN 27), the raw IF data of GPS L1 and L2 signals is collected simultaneously by SPIRE GNSS RO satellite (FM115) on 9 December 2020. (**e**) Comparison between normalized power waveforms of reflected BDS-3 B1C and B1I signals (BDS-3 PRN 27), the raw IF data of BDS-3 B1I and BIC signals is collected simultaneously by BF-1A satellite on 19 February 2020.  
advanced signal processing algorithms even without the direct signal processing and the computation of the open-loop tracking model. * These complex waveforms are integrated coherently for a relative short integration time (i.e., 1 ms), which can be used to assess the optimal coherent integration times for different GNSS-R signals.