## 4 Discussion  
The study showed that the three sources of information utilized have contributed to the representation of the soil moisture in the determined conditions. On the other hand, considering each source singularly, some limitations have been noted, especially in the case of different soil conditions and cultivations type (triticale, corn, uncultivated soil). The analysis of the correlation maps displayed interesting results that may lead to identifying \"exclusivity\" or \"complementarity\" between the methods. For exclusivity, it can be intended that one system (e.g., satellite system) may replace another (e.g., soil sensor) because of high correlations. However, for complementarity, we intend that one method can be functionally similar to another for the goal of defining a precision irrigation schedule. This complementarity was observed in the conditions of minor correlation between the reading methods. For instance, the maps obtained from the ground sensors (R1) showed less correlation with the satellite maps (R2) when the soil was cultivated with triticale, with an average correlation of \(-\)0.02 for the MCARI2 index and of 0.1 for the 'NDWI index. Therefore, in the period in which the triticale was cultivated, the satellite indices did not allow us to obtain maps corresponding to the proximal readings. This was probably because of the meteorological trend, which showed a significant influence on the values of correlation. Instead, the satellite information (R2) correlate with those of soil sensor (R1) during corn cultivation, meaning that the satellite, in this case, can substitute the information of soil sensors. The same consideration applies to both geoelectric methods (R1 and R3) and between the MCARI2 satellite index and ARP (R2 and R3). The two geoelectric methods showed an average correlation in the period of uncultivated soil (\(-\)0.45) and in the period of corn \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Condition (Corn,** & **Indication** \\  & **Soil, Titicale)** & **Moisture (\textgreater{}40 mm)** & **Condition-Moiture** \\ \hline R1-R2 (MCARI2) & *** (corn) & n.s. & *** \\ R1-R2 (NDWI) & *** (corn) & *** & *** \\ R1-R3 & *** (soil, triticale) & *** & *** \\ R3-R2 (MCARI2) & *** (triticale) & *** & *** \\ R3-R2 (NDWI) & *** (triticale) & *** & *** \\ \hline \hline \end{tabular}  
*** corresponds to \(p\)-value \(<\) 0.001.  
\end{table} 
Table 2: Significance of the soil condition and soil moisture in relation to the correlations between the survey methods. culty (\(-0.2\)). This can be attributable to the fact that reading with ARP (R3) was performed only once, while the readings with the soil sensors (R1) were performed at a high frequency, detecting variations over time. On the other hand, the same was also observed between the maps processed through the MCARI2 satellite index and the map obtained with the ARP, showing a very low correlation, lower than \(-0.4\), in the condition of uncultivated soil. Previous studies have observed correlations between soil moisture and electromagnetic observations in the visible-near infrared [36; 37], in the thermal infrared [38; 39] and in microwaves [40; 41; 42]. Based on these correlations, [PERSON] et al. [39] have considered the opportunity to detect and localize the variability of soil moisture. Additionally, the study conducted by [PERSON] et al. [43] explored the need to integrate soil surface humidity indexes and vegetation indexes to plan irrigation events during the entire vegetative season. However, despite the numerous studies performed, the extreme soil variability and the different field conditions may limit the applicability of the use of satellite indexes for this purpose. The most interesting aspect of this study is the correlation and complementarity between the sensor network and the satellite indexes. This is because sensor networks can be considered the most reliable and repeatable measurements, while satellite indexes are the most affordable.