## 2 Materials and Methods  
Seven Galileo-reflected tracks were acquired between November 2015 and March 2019, originating from six different transmitters, as well as one BeiDou-3 track collected in March 2019. Each track was of 130 s length, except the BeiDou-3 track which was 65 s long. All data were acquired by TDS-1 in raw mode and later processed on the ground by Surrey Satellite Technology Ltd. (SSTL) (Guildford, UK) [20]. Two parallel tracks, each 130 s long and \(\sim\)200 km apart, were acquired simultaneously on 18 October 2018, the first being transmitted by GPS PRN26, and the second by Galileo E31, allowing an initial assessment of neighbouring Galileo and GPS reflections collected by the same instrument. The bandwidth of TDS-1 front-end was set to 2.5 Mhz for all collections in this work, except for the final set (targeting E26) where the bandwidth was set to 4.2 MHz. All samples were processed on the ground using different coherent integration times. Assuming that coherent integration interval is suitably selected to avoid self-destructive signal interference, coherent delay Doppler maps (DDMs) based on longer integrations are expected to provide a higher SNR [21]. The 1 ms coherent integration used by the on-board TDS-1 correlator to process reflected GPS signals is relatively short compared to the Galileo code length of 4 ms (the GPS L1 C/A signal has a code length of 1 ms) and refinement of the signal processing strategy, possibly by including longer integration times, may be required to achieve optimal retrieval performance from reflected Galileo data. The GPS DDMs were processed using coherent integration times of 1 ms, corresponding to the coherent integration time used by the TDS-1 on-board GNSS-R processor; and 2 ms, which should provide a higher SNR. Galileo returns were processed using two different coherent integration times: 1 ms (i.e., the same as for GPS), and 4 ms (the same as the Galileo code length). BeiDou-3 data were processed similarly, using 1 ms and 4 ms coherent integration times. In all cases, incoherent accumulation time was adapted to obtain DDMs consistently output at 1 Hz. The longer of the two coherent integration times for each system is analysed here as they should provide a stronger SNR, making key features easier to recognise, (side-by-side comparisons of DDMs obtained using different coherent integration times can be found in the supporting information, Figures S1 and S2, alongside their processing settings in Table S1). Due to the unavailability of corresponding metadata necessary for radiometric calibration of these experimental collections, the preliminary analysis presented here is limited to SNR as a primary observable. The SNR is estimated from individual DDMs of BeiDou-3, Galileo, and GPS reflected data, and the observable is defined as the ratio of the DDM power in the signal and the noise boxes using the DDM characterization and peak power location mechanism described by [4]. The noise power box (N) is fixed in DDM space and of constant size, covering all Doppler frequency bins and the first four delay (1 chip) bins in the signal free area of the DDM. The signal box (S) also has a constant size of four delay bins (1 chip) and a size of ~3000 Hz in Doppler, dependent on the Doppler resolution of a given processing setting. To assess the sensitivity of SNR to surface parameters, model reanalysis wind-speed and observational sea-ice concentration fields were used. Wind speed fields were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 High Resolution (HRES) reanalysis model which is available with a spatial resolution of 31 km and a temporal resolution of 1 h and is shown to provide favourable performance relative to scatterometers [22].