## 2 Materials and Methods
### Procedure  
The workflow used in this study is shown in Figure 2. It consisted of four steps: (a) preprocessing of datasets; (b) determination of vegetation indices; (c) analysis of the optimal resolutions; and (d) comparison of remote sensing variables for SM retrieval according to land use. The field campaigns were carried out during the dry season on 5, 6, and 7 February 2019 to evaluate the soil moisture of three study plots measuring 1 ha per land use evaluated (i.e., pasture (_Pennisteum dandestinum_), potatoes (_Solanum tuberosum_), and bare soil) located in the highest part of the Las Palmas catchment area, Envigado, Colombia (Figure 1). Soil characterization of the study site was determined by means of 7 soil profile descriptions and pedologic and hydrological measurements (Figure 1, Table 1), analyzing NaF (sodium fluoride) reaction and pH, profile depth, volcanic ashes depth, infiltration, and field-saturated soil hydraulic conductivity (Ks) in the upper soil layer. The reaction of sodium fluoride solution with soils and soil minerals is used as a parameter to determine the presence of amorphous minerals and hydromorphic soil conditions. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Soil** & **x** & **y** & \begin{tabular}{c} **pH** \\ **(0-10 cm)** \\ \end{tabular} & \begin{tabular}{c} **Soil** \\ **Texture** \\ **(0-10 cm)** \\ \end{tabular} & \begin{tabular}{c} **NaF** \\ **Reaction** \\ \end{tabular} & \begin{tabular}{c} **Depth A** \\ **Profile** \\ \end{tabular} & \
\begin{tabular}{c} **Depth** \\ **Volcanic** \\ **Ashes** \\ \end{tabular} &
\begin{tabular}{c} **Kfs** \\ **T10** \\ \end{tabular} \\ \hline
1 & 841,661.7 & 1,174,433.7 & 5.1 & Loam & Moderate & 47 & 125 & 0.01574 & 39.3 \\ \hline
2 & 841,812.6 & 1,174,370.8 & 5.2 & Silty loam & Strong & 34 & 106 & 0.01168 & 15 \\ \hline
3 & 840,311.1 & 1,176,556.6 & 5.9 & Loam & Strong & 44 & 72 & 0.01815 & 7.33 \\ \hline
4 & 840,298.9 & 1,176,382.1 & 5.5 & Loam & Strong & 44 & 150 & 0.00406 & 16.39 \\ \hline
5 & 841,312.4 & 1,176,329.7 & 5.6 & Loam & Strong & 33 & 100 & 0.00899 & 134.33 \\ \hline
6 & 841,552.3 & 1,176,249.2 & 5.3 & Loam & Strong & 26 & 120 & 0.015583 & 44.67 \\ \hline
7 & 841,476.8 & 1,176,338.2 & 5 & Loam & Strong & 26 & 92 & 0.032536 & 123.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Soil profile descriptions and associated hydrological and pedological variables. Kfs: field-saturated soil hydraulic conductivity. Figure 2: Workflow of the pursued methodology. R (Red), G (Green), B (Blue), NIR (Near Infra-Red). Source: Current research. The ground data used for the calibration and validation of the regression models were collected from 110 sampling points on each study plot, previously marked using 25 cm diameter polystyrene dishes and forming a regular grid with a distance of 10 m \(\times\) 10 m between them (Figure 3). To verify the exact location of the sampling points, 5 sub-metric high-precision GPS spots were georeferred by means of a Topcon(c) Hiper V RTK, Livermore, CA, USA (Figure 3). On each studied plot, 110 sampling points were considered, and the soil moisture and temperature data were collected using a TDR sensor. Simultaneously with the ground measurement, aerial images were acquired using a hexacopter UAV and a multispectral RedEdge camera, Micasense(c), Seattle, WA, USA obtaining multiple sets of images in five spectral bands, blue (475 nm), green (560 nm), red (668 nm), red edge (717 nm) from the visible rank, and NIR (840 nm), to determine soil moisture reflectance (Figure 4). UgCS software, Riga, Latvia, Europe, was used for the automated drone mission planning. The images were later merged and postprocessed in the laboratory for geometric correction and calibration using the Pix4D(c) software, Prilly, Switzerland, Europe. Radiometric correction of the images PlanetScope(c), San Francisco, CA, USA was carried out by means of the Qgis software, Gossau, Switzerland, Europe and the required parameters were obtained from the image metadata.