## 2 Materials and Methods
### Satellite Images to Monitor Earth Surface  
To continuously monitor the phenomena and changes that occur on the Earth's surface, space agencies have satellites in orbit that carry different sensors on board to acquire data in this regard. Regarding the physical-mathematical model that underlies the acquisition of satellite images, it should be noted that it encompasses the conjunction of several phases, considering the Earth/atmosphere interaction of radiation and the internal physical behavior of the sensors. Sentinel-2 and Landat-8 images used in this paper are Level-2 products so that they are calibrated images with reflectance values at the bottom of the atmosphere [22,23]. Specifically, NASA and the United States Geological Survey (USGS), since 1972, has launched different Earth observation satellites under the name Landsat. Although Landsat-9 is the last satellite put into orbit (on 27 September 2021), Landsat-8 is the last to offer its products to date. Landsat-9 will start offering its products in early 2022. Of the two instruments that Landsat-8 has on board, the OLI (Operational Land Imager) sensor captures images in different parts of the optical spectrum (Table 1) with a 16-day temporal resolution and a 12-bit radiometric resolution. Once Landsat-9 data begin to be offered, the temporal resolution offered by this constellation of satellites will be 8 days instead of 16 days. In recent years, the European Space Agency (ESA) has become a reference space agency thanks to the Copernicus earth observation program, in which satellite data are the main source of information. With a total of six families of satellites, under the name Sentinel, ESA is able to monitor different important parameters to improve the environmental management. Specifically, the constellation Sentinel-2 (2A and 2B), thanks to its MSI sensor, captures data within the optical spectrum (Table 2) with a 5-day temporal and a 12-bit radiometric resolution. Regarding the physical-mathematical model that underlies the acquisition of satellite images, it should be noted that it encompasses the conjunction of several phases, considering the Earth/atmosphere interaction of radiation and the internal physical behavior of the sensors. Sentinel-2 and Landat-8 images used in this paper are Level-2 products so that they are calibrated images with reflectance values at the bottom of atmosphere [22; 23]. Both NASA and ESA have their own online platforms for downloading data captured by the corresponding satellite instrument. Due to the need for up-to-date images to carry out suitable monitoring of the appearance of water bodies or anomalous vegetation surfaces in the plant surroundings, a continuous download of data is required. This results in a large amount of information to be managed. For this reason, a Python script has been developed so that it allows: (i) reporting the available Landsat-8 and Sentinel-2 Level-2 products along the photovoltaic plants by a proper query [24; 25] to the downloading platforms, (ii) the automatic download of such information, (iii) the registration of the downloaded \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 & 30 \\ B2—Blue & 0.45–0.51 & 30 \\ B3—Green & 0.53–0.59 & 30 \\ B4—Red & 0.64–0.67 & 30 \\ B5—NIR & 0.85–0.88 & 30 \\ B6—SWIR 1 & 1.57–1.65 & 30 \\ B7—SWIR 2 & 2.11–2.29 & 30 \\ B8—Panchromatic & 0.50–0.68 & 15 \\ B9—Cirrus & 1.36–1.38 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of the Landsat-8 OLI sensor.