# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 4 Discussion  
This article describes an RGI image fusion procedure in which information from several sources with different spatial and temporal resolutions are originally merged, using a simple, easy-to-implement novel algorithm that can be applied under the assumptions expressed in Section 2.2. The assumption of higher spectral quality of Sentinel 2 data is adopted by [PERSON] and [PERSON] (2012), their and other works use two satellite sources. In these cases, the temporal resolution of higher resolution images is equal to or better than those of Sentinel 2. They use Sentinel 2 basically to correct image series obtained from higher spatial resolution satellites. However, this work explores a different application in the temporal dimension, which is the possibility of using low temporal resolution information to correct Sentinel 2 temporal series, thus providing a practical means to increase the applications of this satellite imagery. The paper provides two examples of agricultural applications of the proposed algorithm. The proposed algorithm re-distributes the low-resolution pixel values of Sentinel 2 in higher resolution pixels and provides a robust method to assess the evolution of spectral indices of points of the soil and points of the vegetation that cannot be distinguished in the original Sentinel 2 images. Therefore, it has demonstrated that it can be applied in a series of Sentinel 2 images, where pixel size is much larger than trees. This same algorithm can obviously be used in images composed of more bands, which expands the number of applications. For instance, in agronomy, it can be used for the determination of spectral indices that also use the blue band, such as Enhanced Vegetation Index (EVI) ([PERSON] et al., 2017), Green Leaf Index (GLI) ([PERSON] et al., 2017), Triangular Greenness Index (TGI) ([PERSON] et al., 2017), Normalized \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**September 3**} & \multicolumn{3}{c}{**September 18**} & \multicolumn{3}{c}{**September 28**} \\ \hline
**Parameter** & **NDVI** & **GNDVI** & **GCI** & **NDVI** & **GNDVI** & **GCI** & **NDVI** & **GNDVI** & **GCI** \\ \hline \(c\) & 0.981 & 0.980 & 0.967 & 0.981 & 0.980 & 0.967 & 0.982 & 0.980 & 0.967 \\ \(s\) & 0.921 & 0.914 & 0.918 & 0.921 & 0.914 & 0.918 & 0.921 & 0.914 & 0.918 \\ \(l\) & 0.970 & 0.998 & 0.992 & 0.973 & 0.998 & 0.992 & 0.975 & 0.998 & 0.992 \\ _SSIM_ & 0.876 & 0.894 & 0.880 & 0.879 & 0.894 & 0.880 & 0.881 & 0.894 & 0.880 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Similarity of NDVI, GNDVI and GCI images obtained from merging the original August and original September images during the overlapping period. Pigment Chlorophyll Ratio Index (NPCRI) [17], or Structure Insensitive Pigment Index (SIPI) [18]. However, since no information on short-wave infrared (SWIR) is provided by the cameras employed to create the orthophotos or mounted in the drones used in this work, the algorithm is not intended to be directly applied to calculate indexes related to water or moisture contents in the soil or vegetation, such as the Moisture Stress Index (MSI) [19] or the Normalized Difference Water Index (NDWI) [20]. SWIR information from RGI can be estimated using different methods, such as those for pansharpening (for instance by linear regression or random forest-based procedures), then the proposed algorithm could be applied. Nevertheless, this has been considered out of the scope of the present work, since it is devoted to demonstrating a new fusion method. Although this work represents a significant advance in remote sensing using satellite images, it should be borne in mind that the spectral response of the sensors that acquired the images used in this work is not exactly coincidental, so an approximation has been made. This approximation can be improved by using calibration transfer techniques, commonly used in spectrophotometry, such as the ones described by [PERSON] et al. [21].