## 1 Introduction  
Image fusion has received increasing attention as simultaneous purchase of a PAN and MS image from a high-resolution satellite is usually very costly [1]. It is also necessary as many sensors acquire information about the Earth in either only PAN (EROS-A, EROS-B, Worldview-1) or only MS (Rapid Eye) mode [2]. Image fusion has many applications in different fields like remote sensing [3], urban sprawl monitoring, coastal mapping [4], land cover change detection [5], land use classification [6], crop monitoring [7], land use classification on flood areas [8], changes in vegetation cover [9], and vegetation indices [10; 11]. A high-resolution and good-quality VI mapping is necessary [1] for monitoring the earth's vegetative cover as a precise radiometric measure of green vegetation [12]. Such high-resolution VIs derived from satellite images area useful data source for many agricultural, environmental, and climate studies [13]. An image with high quality is preferred for analysis and other applications [3]. However, it is important to assess the quality of the fused image before using it for various applications of remote sensing like VI mapping [14]. Recently, many image fusion algorithms have been developed; for example, the sparse images fusion algorithm (named as Sparse FI, pronounced as \"sparsify\") has super-resolution capability and robustness of sparse signal reconstruction algorithms, which gives a higher spatial resolution and, in most cases, less spectral distortion compared with the conventional methods [15]. Similarly, [PERSON] et al. [16] proposed a convolutional sparse representation (CSR)-based image fusion framework, in which each source image is decomposed into a base layer and a detail layer, for multi-focus image fusion and multi-modal image fusion which outperform the SR-based methods in terms of both objective assessment and visual quality. To recover the original information covered by the clouds and the accompanying shadows, a nonnegative matrix factorization (NMF) and error correction method (S-NMF-EC) is proposed [17]. [PERSON] et al. [18] also proposed a new algorithm, applied to a reflectance product and a land surface temperature product, in which a qualitative visual effect and the quantitative objectives are effective. However, there are several popular image fusion algorithms which also have good spectral characteristics preservation, and are frequently used in many image fusion applications like GS [6; 19; 20], Ehlers [21], Principal Component (PC) [6; 22; 23; 24], Modified Intensity Hue Saturation (MIHS) [25; 26] and HPF [22; 23]. Commonly used image fusion techniques generally produce good results with a standard SRR (ratio of PAN resolution to MS resolution) of 1:4 for images obtained from the same sensor [27]. [PERSON], 2008 [28] showed that SRR can get worse if data from different satellites are used; for example, SRR between IKONOS and SPOT 5 (multispectral mode) is 1:10. [PERSON] et al. [29]'s experimental results indicated that SRR of 1: 10 or higher is desired for optimal multisensory image fusion and also indicated that with decreasing SRR, the fused image quality decreases. However, they were focused on fused image quality, not FVI quality. VIs involve further image transformations of the fused image bands, which can have additional impacts on their spectral and spatial quality, so it is necessary to evaluate the effects that these further transformations have on the fused imagery [13]. Image quality must be inferred from measurements of spatial resolution, calibration accuracy, SNR, contrast, bit error rate, sensor stability, and other factors [30]. This indicates that in addition to SRR, there are other factors which need to be identified to study the effect of image fusion on VI quality properly and to evaluate the relationship between SRR and FVI quality. Only a few studies have evaluated the effects of image fusion methods on VIs quality. For example, [PERSON] et al.