## 2 Materials and Methods
### Dataset and Statistical Analysis  
The dataset collected in the experimental fields during the observation period was composed of the following dependent variables:  
* the measurements for each geographic soil coordinate of the electrical conductivity obtained from the ARP system;
* the difference in electrical conductivity detected by the surface sensors compared to those placed in the depth of the network of the transmitter nodes, with a frequency of 15 min;
* The spectral indices NDWI and MCARI2 are calculated by the Sentinel 2 source. The independent variables of the experiment were the meteorological data (mm of rain) and the field conditions (crop present or not). The methodology of data processing was dependent on the satellite information, both for resolution (20 m \(\times\) 20 m) and dates of availability to be compared. During the whole observation period, 55 maps of the spectral bands of the two satellites were downloaded and observed. However, from these, only maps with no interference due to cloud densities were used. There is a list below of the dates when maps were sharp and implementable (Table 1). From the available dates, the spectral indices MCARI2 and NDWI were calculated; the spectral bands utilized were the B3, the B4, and the B8 for MCARI2 and the bands B3 and B11 for the NDWI. The B3, B4, and B8 bands are available with a resolution of 10 m \(\times\) 10 m, while the B11 band relating to the short-wave infrared (SWIR) has a resolution of 20 m \(\times\) 20 m.  
Therefore, for the source of information coming from the sensor network, a data filter was carried out by selecting the dates and times corresponding to the passage of the satellites. The differences in conductivity between the sensor placed on the soil surface and that placed 0.15 m deeper were calculated. For the geoelectric source, spatial vectors were prepared and subjected to clustering for the determination of homogeneous zones. From these zones, soil samples were taken in order to establish their composition in sand, silt and clay. Subsequently, a vector with the information obtained from the sampling point was extracted. The flow chart depicted in Figure 3 shows that information from spatial raster coming from the satellites was extracted as well and corresponded to the coordinates of the ground sensors. The data were subjected to geostatistical analysis using the \"R\" statistical software. The values coming from the three sources, with the same coordinates for construction, were interpolated with the kriging method through the krige function of the _gstat package_ of R, using a regular grid of 5 m \(\times\) 5 m points in the quadrilateral defined by the area observed by the sensor network, to observe its distribution in space. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Date** & **Condition** & **mm Rain (Last 5 Days Cumulative)** \\ \hline
25 July 2020 & corn & 66.50 \\
30 July 2020 & corn & 42.80 \\
9 August 2020 & corn & 40.00 \\
14 August 2020 & corn & 0.00 \\
19 August 2020 & corn & 40.00 \\
13 September 2020 & corn & 0.00 \\
19 September 2020 & corn & 0.00 \\
17 November 2020 & soil & 4.30 \\
22 November 2020 & soil & 0.00 \\
27 December 2020 & soil & 42.40 \\
16 January 2021 & triticale & 0.00 \\
15 February 2021 & triticale & 16.50 \\
25 February 2021 & triticale & 0.00 \\
2 March 2021 & triticale & 0.00 \\
7 March 2021 & triticale & 0.00 \\
17 March 2021 & triticale & 0.00 \\
22 March 2021 & triticale & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dates of availability of information used for this study. Figure 3: Flow diagram of the correlation study. Therefore, 17 rasters interpolated from the values came from the sensor network (R1); 17 rasters interpolated for satellite information (R2), and 1 raster from the geoelectric reading (R3) were obtained.