## 4 Discussion
### Influence of SRR (with different SRF and SNR (Good to Poor))  
Multi-sensor fusion showed different visual quality for different algorithms for different VI (Table 4). Some image fusion algorithms had a good visual result in preserving all types of details and some algorithms did not. In the objective quality results, the pattern was fluctuating (both CC and RMSE) (Figure 4) depending on SRR, SRF and SNR. For SRR 1:6 in GS fusion, for ARVI CC is 84.27% and RMSE is 33.97% but when the SRR is 1:8, for ARVI CC is 92.56% and RMSE is 22.59% (Table 6), remaining VI has a similar trend (Figure 4). With decreased SRR, the image fusion accuracy was assumed to be decreased but the result showed an increased accuracy(better quality). This might be due to two reasons: one is the difference in SRF and the other is the resolution of the MS image. [PERSON] and [PERSON] [56] indicated that for fusing spatiotemporal images, spectral responses of input images have to be unified, so the difference in SRF between two sensors has an impact on the quality of FVI. In SRR 1:8; the MS image has a 30 m resolution and PAN image has a 4 m resolution which is a better resolution than SRR 1:6 (PAN (8 m) with MS (50 m)). SRR 1:10 fusion is the result of a low-resolution PAN (50 m) image and a very low-resolution MS (500 m) images. The SNR is a measure of the purity of a signal, the ratio between the information and noise of the fused image [57]. MODIS (500 m) and GF4 (50 m) has very low resolution; both of them have poor SNR, so FVI quality is poor for them. The influence of SRF is also prominent along with SNR for multi-sensor FVI. However, the SRR effect is not very high. The visual analysis showed that FVI has a different quality depending on its SRF, SNR and SRR (Table 8). Not only does GS have a good result but also Ehlers has a good result for ARVI and their quality indices are very close. This shows that very low-resolution image fusion has inconsistent results due to poor SNR. For SRR 1:12 in GS fusion; for ARVI CC is 91.59% and RMSE is 25.25% (Table 6) which is a better result than SRR 1:6, although it has a higher SRR than 1:8 and 1:10. The reason might be the same, due to the high-resolution (4 m) PAN image used in SRR 1:12, although it has lower SRR. Not only does GS fusion have this trend for VI mappings but all the remaining fusion methods have the same pattern for all FVI (Figure 4). We changed the fusion algorithms (keeping same VI) but the CC values are very close to each other. Similarly, the change in image fusion algorithms did not make a large difference in RMSE values. This indicates that image fusion is insensitive to VI (i.e., change in image fusion algorithms did not make a large difference in VI quality). However, the HPF has a scattered graph and seems to be sensitive. ARVI and GNDVI have quite lower RMSE, high CC values so they are well separated in all image fusion and in all SRR (Figure 4). However, for other VI (except ARVI and GNDVI), CC and RMSE, the graph is almost same and overlapped. The GS pan-sharpen method has become one of the most widely used high-quality methods since its publication [20]. In multi-sensor FVI, also the GS fusion has a good quality (low RMSE and high CC) for all VI and in all SRR. In general, the quality of FVI is decreasing with decreasing SRR (1:6 to 1:12). However, the higher effect on FVI quality is due to SNR and SRF rather than SRR (Figure 4).