## 3 Results
### Soil Moisture vs. Vegetation Indices  
For each land use, the SAVI, NDVI, and NDWI were determined from the UAV images (4 cm pixel), as can be seen in Figure 8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Land Use** & **NIR – Red Equation (R2)** & **M** & **PDI – SM (R2)** \\ \hline Pastures & y = 0.9083x + 4.2942(R2 = 0.214) & 0.9083 & R2 = 0.141 \\ \hline Potatoes & y = \(-\)1.3897x + 6.562(R2 = 0.322) & \(-\)1.3897 & R2 = 0.0191 \\ \hline Bare Soil & y = 1.6884x \(-\)1.8164 (R2 = 0.465) & 1.6884 & R2 = 0.1137 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NIR– Red linear equations to obtain the M value to calculate the PDI at 300 cm spatial resolution. Figure 8: Normalized difference wetness index (NDWI) data obtained by means UAV composite images at 4 cm pixel resolution with the ground measurement location per land use. (**a**) pasture study site, (**b**) potatoes study site, (**c**) bare soil study site. Source: Current research. Figure 7: The PDI vs. soil moisture at 0–10 cm depth under different soil uses: (**a**) pasture R2 = 0.4392; (**b**) potatoes R2 = 0.002; (**c**) bare soil R2 = 0.5062) at UAV resolution (4 cm pixel). n = 110. Source: Current research. Satellite Planet Analyst Scope images (300 cm pixel) were processed and then the same indices were determined per studied land use. Data for 12, 40, 100, 300 cm were obtained by means of an oversampling of the pixels of the UAV images on several scales, and from the means of the index values for each buffer zone of the sampling points. Posteriorly the georeferred data of each index were correlated with the ground soil moisture measurements. The soil moisture data obtained from the sampling plots did not have a normal statistical distribution. The correlation between the measured soil moisture data and the indices obtained by means of the obtained images was analyzed by applying [PERSON]'s rank correlation rho test according to the spatial resolution and land use. The following table shows these correlations (Table 4), where the triangle symbols denote the significant correlations. It can be seen that there is an index that fits better, or presents a better correlation, with soil moisture for each of the land uses and resolutions studied. Regarding pasture land use, soil moisture analysis by means of satellite images at 3 m resolution only had significant correlations with the NDVI. Pasture land use at a detailed 4 cm resolution scale showed a significant correlation between the PDI and soil moisture. Under land use for potatoes, all the indices showed a positive correlation with soil moisture (Figure 9). Satellite images at 3 m resolution can be used to determine the soil moisture of potatoes land use using the NDWI and NDVI, that is, the indices that showed the best correlations (Table 4). At detailed resolution, only the NDWI showed a significant positive correlation with soil moisture (Figure 9). The best representation to analyze soil moisture under bare soil is by means of the PDI with UAV images at high resolution, whereas the same index with a coarser satellite resolution (3 m) cannot be directly correlated with surface soil moisture (0 to 10 cm). At 3 m resolution, the NDVI and NDWI show the best significant correlations with soil moisture under bare soils, showing negative correlations (Figure 9). Figure 9: Rho correlation between the index and soil moisture according to land use and spatial resolution. (**a**) pasture study site, (**b**) potatoes study site, (**c**) bare soil study site. Source: Current research. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Spatial**} & \multicolumn{4}{c}{**Pastures**} & \multicolumn{4}{c}{**Potuses**} & \multicolumn{4}{c}{**Bare Soil**} \\ \cline{2-13}
**Resolution** & **NDVI** & **NDWI** & **SAVI** & **PDI** & **NDVI** & **ND  
On bare soil land use, any resolution can be used to estimate soil moisture by means of optical images with the NDWI.