# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
###### Abstract
However, the fact that each pixel of the image, at best, covers 10 \(\times\) 10 m\({}^{2}\) of soil, makes it impossible to discriminate the value of these indices for soil and vegetation, in plots with crops in which the planting frames are much smaller and the tops do not fully cover the ground, as is the case of the olive grove that concerns us. One solution to this problem is to combine or merge the higher spatial resolution information provided by aircraft images, with the temporal frequency and image quality of the multispectral sensors installed in the Sentinel 2 missions. Thus, in this article, we use the Sentinel 2 Level 2A product to merge it with orthophotos and images obtained with drones, in order to produce a series of high-resolution synthetic images. The results of the process are of considerable use for applications that require both high spatial resolution and frequent image coverage. \begin{table} \begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Satellites**} & \multicolumn{2}{c}{**Aerial Vehicles**} \\ \hline
**Information** & **Resolution** & **Domain** & **Resolution** & **Domain** \\ \hline Temporal & weekly/daily & Past-Future & Programmable & Present-Future \\ Spatial & 1–\(\delta\)0 m & 0.5 ha-worldwide & 0.01 m–0.6 m & 0.5-tens of ha \\ Radiometric & Multi- & hyperspectral & Visible microwave & Multispectral & VIS-NIR-LIDAR \\ \hline Cost & Free/Paid & \multicolumn{2}{c}{Paid} \\ \hline \hline  & Atmospheric conditions & Automomy & \\  & Spatial resolution & Air regulations & \\  & Development costs & Atmospheric conditions & \\  & & Costs & \\ \hline \hline  & & & & \\ \hline \hline  & & & & \\ \end{tabular} \begin{tabular}{c c} \hline \hline  & & \\ \end{tabular}  \ \begin{tabular}{c} \hline \hline  & & \\ \end{tabular} 
\begin{tabular}{c} \hline \hlineSome satellites incorporate sensors that provide high-resolution panchromatic images and, at the same time, images of different spectral bands at lower resolution. For this reason, since the beginning of remote sensing, image fusion techniques aimed at generating high-resolution multispectral images have been developed. They combine the spectral information of low-resolution data with the high spatial resolution of panchromatic images. Often, these works have been aimed at increasing the contrast or sharpness perceived by a human operator, and therefore, the calibration of spectral radiance or reflectance has not been considered. These approaches work, for example, with images that result from the composition of three bands and their conversion to saturation and tone values in color spaces, such as HSV, the analysis of the main components of the bands [6], or with wavelet transformations [7]. These approximations are not suitable for the problem considered in this work, since one of its goals is to capture quantitative values of changes in surface reflectance, and therefore, in spectral vegetation indexes caused by the phenology of the crop. Attention must be paid both to the effects caused by the different spectral responses of the sensors that are used and to the effects related to the different illumination of the scene. The problem is close to the studies on the elimination of clouds and their shadows in the composition of satellites [8] or aerial images [9]. In them, the effects on histograms are studied in a special way. Several online platforms are now available to remote sensing users to perform data analysis easily and without increasing the demand for local computing resources. One of these, free to access, is Google Earth Engine [10]. In this context, the major objective of this work is to describe and apply a procedure to generate time series of synthetic RGI images (red, green, infrared) with the same high resolution of orthophotos and drone images, reassigning the gray levels of the latter from the combination of their own RGI bands and bands B3, B4 and B8 of Sentinel 2. Moreover, the robustness of the proposed methodology is validated by comparing differences between original images and between synthetic ones.