## 5 Conclusions  
Many image fusion techniques have developed and applied in various field but the quality assessment is always a key factor before their application. As a preliminary work towards image fusion, different satellite images (GF2, GF1, Landsat-8 OLI, GF4 and MODIS) with PAN and MS bands were acquired, preprocessed and precisely registered. The main objective of this research was to study the effect of image fusion for the quality of VI mapping. Using a strategy of image fusion and quality assessment (in different SRR, SRF and SNR) we came to a robust conclusion. **First**, image fusion has the least impact on GNDVI and ARVI compared to other VI. The quality of FVI is mostly insensitive to image fusion. However, for HPF, the quality of FVI is sensitive. **Second**, the value of CC and RMSE shows that the GS method has the least impact on FVI quality. For example, ARVI in GF1-GF4 with CC and RMSE (84.27%, 33.97%), GF2-Landsat-8 OLI (92.56%, 22.59%), GF4-MODIS (82.20%, 28.94%) and GF2-GF4 (91.59%, 25.25%) proves that GS has a better quality in FVI. **Third**, some factors affect FVI results; they are SRF, SRR and SNR. For example, in GF1-GF4 (SRR 1:6), the influence of image fusion is higher than GF2-Lansat-8 OLI (SRR 1:8) and GF2-GF4 (SRR 1:12) due to the SNR factor. Moreover, GF4-MODIS (SRR 1:10) fusion has a quite different result due to the poor SNR. **Fourth**, with decreasing SRR, the FVI quality is decreasing at a slow rate. For example, in GF2 to Landsat-8 OLI (30 m, 40 m, 48 m and 60 m) fusion, there is a uniform relationship between SRR and FVI quality (CC and RMSE) for ARVI (92.56%, 22.59%), (92.11%, 23.05%), (91.88% and 23.43%) and (91.17% and 23.45%) in SRR 1:8, 1:10, 1:12 and 1:15, respectively.  
Hence, this research shows how image fusion influences VI quality with a critical analysis. The research confirms that the quality of FVI is good even for small SRR (1:6 to 1:15) as long as they have a minimum SRF effect and a good SNR. It would be very helpful for making high-resolution VI map using a lower SRR (not limited to SRR 1:8 or higher) in different applications like agricultural, environmental, and climate studies. Even in small SRR, VI mapping has a good quality, which means we can use high-resolution PAN with low-resolution MS band for image fusion to get high-resolution MS bands to calculate FVI. Although the PAN band should have a high resolution, we can use low-resolution MS bands to get high-resolution MS band and FVI, which will minimize the cost for data acquisition. However, the use of a higher number of fusion algorithms, more VI and more quality indices could give a better result for a comparative FVI quality analysis. Similarly, testing the result in different images like Sentinel-2, World View-4, IKO NAS, SPOT and making a decision could have a better result. In the future, minimizing these limitations, and the developing image fusion algorithmswith the least effect on SRR, SRF and SNR for band-band fusion and FVI could be new research topics to gain a better quality FVI.  
[PERSON] and [PERSON] designed and developed the research idea. [PERSON] downloaded the data (GF1, GF2 and GF4) and pre-processed. [PERSON] downloaded and processed all remaining data and wrote the manuscript. [PERSON] and [PERSON] contributed to result and data interpretation, discussion and revision of the manuscript. All the authors revised and approved the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research & Development Program of China (no.2018 YFC0706004).  
The authors are very thankful for Ms [PERSON] and Ms [PERSON] for their valuable support.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.