# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
###### Abstract  
In the Valencian Community, the applications of precision agriculture in multiannual woody crops with high added value (fruit trees, olive trees, almond trees, vineyards, etc.) are of priority interest. In these plots, canopies do not fully cover the soil and the planting frames are incompatible with the Resolution of Sentinel 2. The present work proposes a procedure for the fusion of images with different temporal and spatial resolutions and with different degrees of spectral quality. It uses images from the Sentinel 2 mission (low resolution, high spectral quality, high temporal resolution), orthophotos (high resolution, low temporal resolution) and images obtained with drones (very high spatial resolution, low temporal resolution). The procedure is applied to generate time series of synthetic RGI images (red, green, infrared) with the same high resolution of orthophotos and drone images, in which gray levels are reassigned from the combination of their own RGI bands and the values of the B3, B4 and B8 bands of Sentinel 2. Two practical examples of application are also described. The first shows the NDVI images that can be generated after the process of merging two RGI Sentinel 2 images obtained on two specific dates. It is observed how, after the merging, different NDVI values can be assigned to the soil and vegetation, which allows them to be distinguished (contrary to the original Sentinel 2 images). The second example shows how graphs can be generated to describe the evolution throughout the vegetative cycle of the estimated values of three spectral indices (NDVI, GNDVI, GCI) on a point in the image corresponding to soil and on another assigned to vegetation. The robustness of the proposed algorithm has been validated by using image similarity metrics. m 20222222222222222222222222222222222222222222  
Despite its enormous usefulness in economic and environmental terms, the implementation of technology associated with Precision Agriculture and remote sensing in the Valencian agricultural sector is scarce, due to the lack of information on its possibilities, the nature of production (mainly associated with Mediterranean fruit trees with high added value) and the excess of smallholdings, which collides with the resolution of satellite images. In addition, Precision Agriculture based on remote sensing has been developed mainly in extensive annual crops (corn, wheat, rice, soybeans, rice, etc.). However, in the Valencian Community, its implementation in multi-year crops is of priority interest, such as fruit trees (orange, mandarin, persimmon, pomegranate, avocado, medlar, etc. ), olive trees, almond trees and vineyards. Satellite imagery is an indispensable source of data, but it offers relatively low spatial resolution (currently 3-10 m). In addition, the images produced by satellites equipped with multi or hyperspectral sensors are very susceptible to cloud cover. Parallel to the rapid evolution of image sources from satellites, new drones are being developed with greater autonomy and versatility that allow obtaining aerial images with multispectral sensors and LIDAR at very high spatial resolution (centimeters). Drones acquire images with a certain temporal flexibility and at a relatively low cost, but they are limited by their flight autonomy, weather conditions and different air safety regulations. A summary of the advantages and disadvantages of both systems is shown in Table 1.  
Sentinel 2's Level 2A products provide data based on constantly calibrated and atmospherically corrected reflectances and are critical for estimating biophysical parameters and detecting land cover. There is a large amount of scientific literature aimed at establishing mathematical relationships between biophysical or soil parameters and certain spectral indices that combine information from two or more bands, such as the Normalized Difference Vegetation Index (NDVI) [3], the Green Normalized Difference Vegetation Index (GNDVI) [4], the Green Chlorophyll Index (GCI) [5], etc.