## 4 Results
### Verification of Our Regression Models  
In this subsection, we verify our regression models by using data gathered just before and after the dataset used in the previous section. Used images correspond to 12 June 2021 and 20 September 2021, which are the closest dates without cloud coverage in the studied area. First, we show a visual verification which consists of applying the regression model to the area close to the golf course using ArcMap 10.4.1 software [21]. The results are visually analyzed as a qualitative verification, and the most relevant findings are in Figure 9, which includes the Orthophoto of the area [25]. We can see the differences in resolution in the models, especially in the detailed view in which the pixel resolution can be seen. Comparing the data, we can affirm that the results of the model calculated with a 10 m (presented in Figure 5) tend to estimate lower soil moisture. It is especially visible in the fairways and green areas. It must be noted that this model is designed for the natural areas with no irrigation or limited irrigation, and the results of irrigated areas might not be accurate. The areas in which the model is applied include both irrigated areas (as the greens and fairways of the golf course) and non-irrigated areas (such as some parts of the golf course, the surrounding fields of rainfed crops, and the natural areas as the mountains and the river). We can affirm that this area constitutes an excellent testbench to evaluate our results due to this heterogeneity. In both general and detailed view, we can see that the calculated soil moisture for the surroundings of the golf camps reflect the expected trends with (i) higher values in the areas close to the rives (sud-est of the picture), (ii) intermediate value in the natural areas with vegetation and, (iii) low values in areas such as olive orchards in which no vegetation retains the water. Regarding the detailed view, we see in detail the practice area composed of the driving range and the putting green. Both models' data accurately depicts the driving range's  
Figure 8: Multiple Regression Model for clay soil for bands with 20 m of resolution. Figure 7: Simple Regression Models for clay soil with (a) band 4 and (b) band 12 for bands with 20 m of resolution. composition with higher soil moisture values in the green areas and their surroundings. Regarding the differences among dates, we can affirm that the most relevant difference in the analyzed periods is that the soil moisture level is lower in the second period. It is the expected change since after the summer, the whole area, most of it composed of natural areas and rainfed crops, has less soil moisture due to the reduction of rain and elevation of evapotranspiration during the summer. Therefore, based on our knowledge of the area and its direct observation during the studied period, we can affirm that the visual verification of results indicate that the predicted values are aligned with the expected variability of soil moisture in the golf course and their surroundings. On the other hand, we perform a quantitative verification. For this verification, the soil moisture data of sensors from the clay soil of the golf course are compared with the results of generated models. In Figure 9(a), we can see the six groups of data, which correspond to the 2 days and the 3 areas. In each data group, we can see in dark blu the  
Figure 9: Comparison of proposed models. Models with transparency of 50% and Orthophoto of 2019.  
value predicted by the mathematical model that uses bands with a resolution of 10 m, and in light blue, the results for the model with the bands with 10 m resolution. In Figure 10b, the bivariate correlations between measured and estimated soil moisture values are shown. Meanwhile, the dark grey bars indicate the soil moisture values sensed by the sensors. In almost all cases, the mathematical model overestimates the soil moisture. The model calculated with the bands with a resolution of 10 m is the one with a higher overestimation.