## 5 Conclusions  
On bare soil, the indices that best fit with the soil moisture results were the NDVI, the NDWI, and the PDI on a detailed scale. In contrast, [PERSON] et al. [21] found that bare soils have good significance on a coarse scale with Landsat images in arid environments. These results are in line with those of a recent sub-metric soil moisture study using UAV and multispectral images in tropical conditions in Peru [5].  
Under potato crops, the NDWI correlates significantly with soil moisture irrespective of the scale of the analyzed image under potato land use.  
The PDI is the index that correlates the highest with detailed scales, showing better results on pasture than on potatoes or bare soil. In regard to pastures at a coarser resolution, the NDVI showed the best correlation with soil moisture. These results are relevant due to the fact that the pasture is an extensive crop in Colombia and soil moisture monitoring can be useful to realize environmental studies of multitemporal changes of this important hydrological parameter.  
A UAV soil moisture study [40] on Karst heterogeneous landscapes determined that the optimal resolution to analyze soil moisture by means of DEMs is 7 m, and that soil moisture variability is mainly explained by the vegetation type (35.7%), which concurs with the results of the current research.  
The study of soil moisture with UAVs study presents several advantages over conventional platforms such as satellites, including the fact that they fly at lower altitudes, increasing the spatial resolution of the images, and cost less than private remote sensing images, allowing for more frequent monitoring. For average-size farms in Colombia, high-resolution remote sensing at 3 m such as Planet Scope combined with UAV data can be used to estimate soil moisture for the evaluated land uses. Remote sensing indices are currently being tested and improved to propose proxies that reflect the physiological status of crops under changing environmental conditions, and they can be used to determine plant water status for several crop species.  
The best scale to study soil surface moisture with optical images is at 3m resolution, which can determine soil moisture at a depth of 0 to 10 cm using either the NDWI or NDVI according to its land use. None of the indices can be used for all crops or land uses with the same resolution. A prior classification of land use is needed to study soil moisture effectively due to the effect of vegetation on soil moisture at depths of 0 to 10 cm, as supported by [PERSON] et al. [32], who state that visible and near infrared spectral data have a close relationship with soil moisture at a soil depth of 10 cm.  
According to land use as a means to determine soil moisture, a different index and resolution were found to provide the most accurate results; that is, resolutions of 3 m appropriate to study soil moisture under pasture, potatoes and bare soil using NDVI correlations with soil moisture in Andosols.  
Conceptualization, M.C. and D.V.; methodology, M.C. and D.V.; software, J.B.-R.; validation, M.C.T.-M., M.C.; formal analysis, M.C.; investigation, M.C.; data curation, M.C.T.-M.; writing--original draft preparation, M.C.; writing--review and editing, D.V.; visualization, J.B.-R.; supervision, D.V. All authors have read and agreed to the published version of the manuscript.  
No external funding was received to carry out this research.  
The authors would like to thank the continuous support to this research to the Secretaria de Medioambiente from Envigado municipality, Colombia. This research was achieved thanks to the valuable support of AGROSAVIA. We would like to thank the engineers [PERSON] and [PERSON] for their technical and professional assistance at field. Finally, the main author would like to specially thank [PERSON] for his contribution in soil sciences description and his invaluable support, guidance and motivation to complete this research.  
The authors declare no conflict of interest.