## 4 Discussion  
As stated in Section 2.2, to obtain the spatial coverage over the globe during certain time intervals, the globe was gridded spatially and the number of cells sampled over that interval of time calculated. For a certain LEO constellation configuration, the visit coverage of the optimal LEO constellation depends on the choice of the size of the cells, or the spatial resolution.  
Figure 5 presents the coverage as a function of time during one day for different cell sizes (i.e., 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), 0.75\({}^{\circ}\)\(\times\) 0.75\({}^{\circ}\), 0. 50\({}^{\circ}\)\(\times\) 0.50\({}^{\circ}\), and 0.25\({}^{\circ}\)\(\times\) 0. 25\({}^{\circ}\)), obtained by the optimal 8-, 24-, 60-, and 120-satellite 2D-LFCs, respectively. It can be seen that for certain optimal constellations, the smaller the cell size, the longer the time for reaching the 90% coverage goal, which is consistent with [16]. While even with the smallest cell size, 0.25\({}^{\circ}\)\(\times\) 0. 25\({}^{\circ}\), the 90% coverage goal could be reached during one day for all the optimal 2D-LFCs, except for the 8-satellite one.  
Figure 5 also shows that the larger the number of the LEO satellites, the less significant the dependence of the visit coverage on the choice of the cell size. Figure 2 presents that with the 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) cell size, the improvement in the visit coverage obtained by the optimal 120- satellite 2D-LFC when compared with that obtained by the 60-satellite was very slight. From Figure 5, it can be seen that although smaller cell sizes do bring a more discernible difference in the visit coverage obtained by the  
Figure 5: Coverage as a function of time during one day for different cell sizes obtained by the optimal 8-, 24-, 60-, and 120-satellite 2D-LFCs, respectively.  
two optimal LEO constellations, the superiority of the optimal 120-satellite constellation was still not significant when compared with the 60-satellite.  
Considering both the computing efficiency of the optimization process and the application of the GNSS-R mission in the monitoring of extreme weather phenomena such as hurricanes and tsunamis, it is desirable that the 90% coverage goal can be reached within one day. Therefore, following [15], \(1^{\circ}\times 1^{\circ}\) (latitude \(\times\) longitude) cells were applied in this study.  
Another issue that needs to be mentioned is that the results were obtained by using simulated data. The present study was focused on the optimization of the LEO constellation parameters, so other issues that might affect the reflection observations were simplified. Following [16], the differences between signal frequencies were not taken into consideration, and as stated in Section 2.3, only the locations of the specular points with the 10 highest RCG were considered, the LEO satellites were set as 3-axis stabilized, and the antenna beam widths of the LEO satellites were set the same as that of the TDS-1 satellite. As pointed out by [16], different settings on the number of simultaneous measurements and on the beam widths of the antennas will produce different coverage statistics, but the general conclusions of this study should be applicable to any mission generally employing GNSS-R reflectometry techniques. Furthermore, the errors in simulated orbits and multipath effects will inevitably cause errors in the positions of the specular reflection points when compared with real conditions, while it is impractical for us to perform officially sensitivity/uncertainty assessments on these issues [15].