## 2 Materials and Methods
### Satellite Images to Monitor Earth Surface
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bands** & **Wavelength Range (\(\mu\)m)** & **Spatial Resolution (m)** \\ \hline B1—Coastal aerosol & 0.43–0.45 & 60 \\ B2—Blue & 0.45–0.52 & 10 \\ B3—Green & 0.54–0.57 & 10 \\ B4—Red & 0.65–0.68 & 10 \\ B5—Red Edge 1 & 0.68–0.71 & 20 \\ B6—Red Edge 2 & 0.73–0.74 & 20 \\ B7—Red Edge 3 & 0.77–0.79 & 20 \\ B8—NIR 1 & 0.78–0.90 & 10 \\ B8A—NIR 2 & 0.85–0.87 & 20 \\ B9—Water Vapor & 0.93–0.95 & 60 \\ B10—Cirrus & 1.36–1.39 & 60 \\ B11—SWIR 1 & 1.56–1.65 & 20 \\ B12—SWIR 2 & 2.10–2.28 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main characteristics of the Sentinel-2 MSI sensor. products in the system catalog, (iv) the automatic calculation of various spectral indexes for monitoring the status of the photovoltaic plants, (v) the registration of these spectral indexes in the system catalog, and finally (vi) the publication of the spectral indexes in the SDI viewer through a map server. Likewise, the integration of Landsat-8 and Sentinel-2 images has been carried out according to an LNG developed by the authors [20]. There is a great variety of spectral indices that are applicable to different areas of knowledge such as agriculture, hydrology, and forestry, among others [26; 27; 28]. For studies on vegetation in general, the Normalized Difference Vegetation Index (NDVI) is the most widely used since it allows estimating the quantity, quality, and growth of vegetation, thanks to the amount of radiation reflected by vegetation in the red and near infrared electromagnetic spectrum (Equation (1)) [29]. \[NDVI=\frac{(NIR-Red)}{(NIR+Red)}, \tag{1}\]  
representing \(NIR\) and \(Red\) the surface reflectance values at the near infrared and red bands, respectively. This index takes values between \(-1\) and \(1\), where values close to zero indicate absence of vegetation or existence of dead vegetation, while values close to \(1\) indicate the existence of abundant and vigorous vegetation. Regarding identifying water masses, the Normalized Differential Water Index (NDWI) is the most widely used spectral index [30]. Within the \(NDWI\), there are different variants, although the most widely used, defined by the \"McFeeters Method\", considers the green and the near infrared bands (Equation (2)) [31]. \[NDWI=\frac{(Green-NIR)}{(Green+NIR)}, \tag{2}\]  
representing \(Green\) and \(NIR\) the surface reflectance values at the green and near infrared bands respectively. The \(NDWI\) is a variant of the \(NDVI\), in which the equation reverses the order of the \(NIR\), and the red band is replaced by the green one. This relationship makes it possible to maximize the reflectance of the water when working with green wavelengths, minimizing the \(NIR\) reflectance of the water and maximizing the \(NIR\) reflectance of the vegetation. With this, it is possible to correctly delimit the water masses, as well as to analyze the degree of humidity of the vegetation. Such as the \(NDVI\), the values that \(NDWI\) can take are between \(-1\) and \(1\), where values greater than \(0\) indicate bodies of water or areas with the presence of moisture. Finally, it should be mentioned that satellite images may contain inaccuracies, mainly due to the resolution limitation of the capture sensors. While this type of uncertainty can be minimized using fuzzy image pre-processors [32], in this case, it is admissible since the target is the detection of significant changes in vegetation and bodies of water.