Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

: Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be e ﬀ ective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized di ﬀ erence vegetation index, NDVI; the normalized di ﬀ erence water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ ﬁeld measurements. The results showed that on bare soil, the indices that best ﬁt the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates signiﬁcantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.


Introduction
In the area of agriculture, surface water content is known as soil moisture and is an important variable to consider and study to improve crops and yield. Depending on the soil moisture percentages, plant growth will be optimized, increasing nutrient absorption and the presence of microorganisms, regulating soil temperature, and affecting the speed of matter degradation and weathering processes. From a chemical point of view, soil moisture is essential for plants to undergo photosynthesis [1].
The Andes mountain range is a contrasting region with microclimates associated with its relief, where soil moisture is an important hydrological parameter that plays a vital role in the complex and vulnerable ecohydrology [2]. In agriculture, soil moisture is a complex parameter that can support soil sustainability [3]. In tropical countries such as Colombia, understanding soil moisture behavior model), considering that topography is a first-order control of the spatial variation of hydrological conditions [17]. The TWI is effective for studying soil moisture on a coarse scale with slope variability and is dependent on geology and the possible divergence between surface and subsurface conditions [17].
Multispectral satellite imagery is another approach to estimate soil moisture content [18] by means of the reflectance of the Earth's surface, although pixel spatial resolution is too coarse to be used on agriculture on a plot scale. Satellite measurements are also limited by their return period and are often impacted by cloud cover, particularly in tropical mountainous regions [5], reducing the available images to study the landscape. Soil reflectance is influenced by soil moisture and other intrinsic parameters, such as soil texture, mineral composition, and organic matter [19], affecting the absorption of different wavelengths. Recent laboratory studies have demonstrated the effect of soil moisture on reflectance for different orders of soils [20]. Organic matter and mineral composition affect short visible wavelengths and soil moisture in the NIR (near-infrared) and SWIR (shortwave infrared) spectral bands [19].
Regarding the spectral variations of water absorption, several multispectral indices using NIR and SWIR to analyze water content and soil moisture by means of optical sensors from space have been studied over the last decade [21][22][23]. For instance, the soil moisture of land covered by vegetation has been studied using indices such as the vegetation dryness index (VDI), the temperature vegetation dryness index (TVDI) [5], the enhanced vegetation index (EVI), the green coverage index (GCI) and, most commonly, the normalized difference vegetation index (NDVI), an enhanced vegetation index to determine vegetation status using drought as an indicator of soil moisture, and the normalized difference water index (NDWI), used to determine water bodies and areas where soil is saturated and additionally used to determine the vegetation hydric index, maximizing water reflectance. There are several methodologies to determine the NDWI. The Mc Feeters [24] equation uses the green band and the NIR band, optimizing vegetation moisture reflection and minimizing water bodies, whereas Dr Gao [25] determines the NDWI by means of the relationship between NIR and SWIR. Xu [26] later proposed the modified normalized difference water index (MNDWI), considering the green and SWIR bands. However, Chen et al. [27] state that soil moisture can cause side effects when using the SWIR band because its absorption is constrained to a reasonable extent. A soil-adjusted vegetation index such as the SAVI (soil-adjusted vegetation index) is used to reduce the soil effect, minimizing the related brightness by considering first-order soil vegetation interaction with soil-adjustment parameters [28]. Jeihouny et al. [29] use this index to map soil moisture by means of data mining, finding that SAVI is an important covariate in predicting soil moisture retention properties.
Another common methodology to estimate soil moisture by means of remote sensing is the trapezoid method, based on thermic and optical data regarding the Earth's surface [30]. This methodology has the problem that land surface temperature varies significantly with the ambient atmospheric parameters, while optical reflectance does not [31]. Starting from this assumption, some indices using optical observations have been proposed for soil moisture and drought monitoring based on triangular spaces from pixel distributions of optical observations in different electromagnetic frequency bands [31]. One of these triangular indices is the PDI (perpendicular drought index), designed by Ghulam et al. [32], which determines soil moisture for bare soils and low covers by means of the near infrared correlation of pixels. Amani and Parsian [22] evaluated the PDI, finding that it has some limitations that challenge its performance in areas with dense vegetation, but that it is highly effective for bare soils.
In this study, four indices (NDVI, NDWI, SAVI, and PDI) are evaluated to estimate soil moisture (SM) from high resolution images obtained by means of remote optical sensors and UAV flights in the highest part of the Las Palmas catchment area in Envigado, Colombia (See Figure 1). Soil moisture was evaluated according its land use on Andosol to determine an algorithm to correlate the studied indices with the soil moisture field data at different spatial resolutions. The four indices were evaluated to estimate soil moisture for three land uses (potatoes, bare soil and pasture). In addition, we analyze Appl. Sci. 2020, 10, 5540 4 of 15 these indices in several spatial resolution using re-sampled imagery from UAV. We demonstrate that the performance of these indices is conditioned to both land uses and spatial imagery resolution.

Study Area
The study site is located in the Las Palmas catchment area in the central Andes mountain range. This catchment area supplies the water for La Fe reservoir, which guarantees the drinking water supply for the three million inhabitants of the Aburrá Valley metropolitan region [32]. This study site was selected to characterize soil moisture according to land use in an agricultural microcatchment area located in the upper section of Las Palmas catchment area in Envigado, Colombia ( Figure 1).
There is an automated climatic EPM (Empresas Publicas Medellin) station in the upper part of the basin (44,3831, 68,4977 elevation: 2820 m.a.s.l.). The total annual precipitation average is 2500 mm/year (1980-2020), with a minimum annual precipitation in 1980 (1379.4 mm) and a maximum annual precipitation in 1999 (2837.2 mm). There are usually two dry seasons, from December to March and from June to August. The mean temperature for the same period was 18 °C (min 10.3 °C, max 22.3 °C).
The soil type in the study site is Andosol with its associated physical properties, making good water reservoirs with fluctuant hydrological properties [33]. Andosol is an unfertile soil due to its high degree of meteorization and the fact that it is derived from volcanic ashes that physically condition its porous system and structure, resulting in a high variation of soil moisture. Furthermore, the soil moisture regime in the study site is udic [34], meaning fewer than 90 cumulative days each year when water is not available in the rooting zone in normal years. Perennial plants are adequately supplied with water most years. In most similar areas, two crops can be grown each year, but the

Study Area
The study site is located in the Las Palmas catchment area in the central Andes mountain range. This catchment area supplies the water for La Fe reservoir, which guarantees the drinking water supply for the three million inhabitants of the Aburrá Valley metropolitan region [32]. This study site was selected to characterize soil moisture according to land use in an agricultural microcatchment area located in the upper section of Las Palmas catchment area in Envigado, Colombia ( Figure 1).
There is an automated climatic EPM (Empresas Publicas Medellin) station in the upper part of the basin (44,3831, 68,4977 elevation: 2820 m.a.s.l.). The total annual precipitation average is 2500 mm/year (1980-2020), with a minimum annual precipitation in 1980 (1379.4 mm) and a maximum annual precipitation in 1999 (2837.2 mm). There are usually two dry seasons, from December to March and from June to August. The mean temperature for the same period was 18 • C (min 10.3 • C, max 22.3 • C).
The soil type in the study site is Andosol with its associated physical properties, making good water reservoirs with fluctuant hydrological properties [33]. Andosol is an unfertile soil due to its high degree of meteorization and the fact that it is derived from volcanic ashes that physically condition its porous system and structure, resulting in a high variation of soil moisture. Furthermore, the soil moisture regime in the study site is udic [34], meaning fewer than 90 cumulative days each year when Appl. Sci. 2020, 10, 5540 5 of 15 water is not available in the rooting zone in normal years. Perennial plants are adequately supplied with water most years. In most similar areas, two crops can be grown each year, but the available water is less reliable for some of the year and farmers often plant more drought-tolerant crops [35].

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.

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 February, 5, 6, and 7th 2019 to evaluate the soil moisture of three study plots measuring 1 ha per land use evaluated (i.e., pasture (Pennisetum clandestinum), 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 (Kfs) 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.  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 × 10 m between them ( Figure 3). To verify 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 (Pennisetum clandestinum), 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 (Kfs) 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. 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 × 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© 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.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 16 the exact location of the sampling points, 5 sub-metric high-precision GPS spots were georeferred by Simultaneously with the ground measurement, aerial images were acquired using a hexacopter UAV and a multispectral RedEdge camera, Micasense©, 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© software, Prilly, Switzerland, Europe. Radiometric correction of the images PlanetScope©, 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.  Simultaneously with the ground measurement, aerial images were acquired using a hexacopter UAV and a multispectral RedEdge camera, Micasense©, 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© software, Prilly, Switzerland, Europe. Radiometric correction of the images PlanetScope©, 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.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 16 the exact location of the sampling points, 5 sub-metric high-precision GPS spots were georeferred by means of a Topcon© 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©, 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© software, Prilly, Switzerland, Europe. Radiometric correction of the images PlanetScope©, 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.  The climatological information for the month prior to the sampling for the field experiment was collected at the EPM meteorological station located 450 m from the study plots, considering rainfall, temperature, and wind as influent parameters.
Optical Planet Scope 3m resolution images in four bands (R, G, B and NIR) were obtained for the same week as the ground measurements were taken. The images used were divided by bands and subsequently multiplied by the reflectance coefficient to convert the Digital number radiance, reescaled into an 8-bit digital number (DN) with a range between 0 and 255, into Top of Atmosphere (TOA) Reflectance.
The vegetation indices were computed using both the UAV and the planet scope images. According to the literature, the NDVI is defined as Equation (1), NDWI (Equation (2)), SAVI (Equation (3)) and PDI (perpendicular drought index) (Equation (4)) To determine the PDI, a soil line was built by means of red and NIR reflectivity correlation of pixels on bare soil, where red was the independent variable and NIR the dependent variable [30]. This drought index was compiled using spatial characteristics of the soil moisture in red and NIR feature spaces to assess soil moisture stress. M is the slope of the soil line in the red-NIR spectral feature space, forming one edge of the triangle in the NIR-red spectral space represented by the soil line ( Figure 5).
After extracting the pixel information from the spectral vegetation indices calculated from the UAV and satellite images, a regression analysis was carried out using the obtained field data.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 16 The climatological information for the month prior to the sampling for the field experiment was collected at the EPM meteorological station located 450 m from the study plots, considering rainfall, temperature, and wind as influent parameters.
Optical Planet Scope 3m resolution images in four bands (R, G, B and NIR) were obtained for the same week as the ground measurements were taken. The images used were divided by bands and subsequently multiplied by the reflectance coefficient to convert the Digital number radiance, reescaled into an 8-bit digital number (DN) with a range between 0 and 255, into Top of Atmosphere (TOA) Reflectance.
The vegetation indices were computed using both the UAV and the planet scope images. According to the literature, the NDVI is defined as Equation (1), NDWI (Equation (2)), SAVI (Equation (3)) and PDI (perpendicular drought index) (Equation (4) To determine the PDI, a soil line was built by means of red and NIR reflectivity correlation of pixels on bare soil, where red was the independent variable and NIR the dependent variable [30]. This drought index was compiled using spatial characteristics of the soil moisture in red and NIR feature spaces to assess soil moisture stress. M is the slope of the soil line in the red-NIR spectral

Pedo-hydrological Characterization of the Study Area
The study plots were located in areas with a udic soil moisture regime, a deep soil profile, 0-5% flat topography, and an isothermal temperature regime with well drained soils. The soils in the study site have loam textures in the upper layers and a mean depth of volcanic ashes of 109.29 cm before saprolite presence. Table 1 shows the pedologic and hydrologic variables analyzed to determine the

Pedo-Hydrological Characterization of the Study Area
The study plots were located in areas with a udic soil moisture regime, a deep soil profile, 0-5% flat topography, and an isothermal temperature regime with well drained soils. The soils in the study Appl. Sci. 2020, 10, 5540 8 of 15 site have loam textures in the upper layers and a mean depth of volcanic ashes of 109.29 cm before saprolite presence. Table 1 shows the pedologic and hydrologic variables analyzed to determine the homogeneity of the study plots (located near to soil profiles 3 and 4 in the case of pasture and potatoes, and near to soil profiles 5, 6, and 7 in the case of the bare soil study plot).

The PDI According the Spatial Resolution
The NIR-red linear regression was obtained to calculate the soil line ( Figure 5), and the M value was determined ( Table 2) by means of Equation (4) to determine the PDI (perpendicular drought index). To validate the PDI, the in situ SM (soil moisture) data measurements every 10 m were compared with the PDI, obtaining the results shown in Figure 6 according to land use. Among these results, correlation is strongest between PDI and soil moisture under bare soil (R2 = 0.5062), followed by pasture and then potatoes. homogeneity of the study plots (located near to soil profiles 3 and 4 in the case of pasture and potatoes, and near to soil profiles 5, 6, and 7 in the case of the bare soil study plot).

The PDI According the Spatial Resolution
The NIR-red linear regression was obtained to calculate the soil line ( Figure 5), and the M value was determined ( Table 2) by means of equation 4 to determine the PDI (perpendicular drought index). To validate the PDI, the in situ SM (soil moisture) data measurements every 10 m were compared Ghulam et al. [32] state that visible and near infrared spectral data are closely related to soil moisture at a soil depth of 10 cm.
The results obtained from repeating the same process at 3 m spatial resolution using the Planet Scope images are shown in Table 3. They show that there is a high correlation between the red and the NIR bands on satellite images with a spatial resolution of 300 cm, whereas the correlation between the PDI and soil moisture is lower than the UAV (unmanned aerial vehicle) 4 cm spatial resolution correlation.  Ghulam et al. [32] state that visible and near infrared spectral data are closely related to soil moisture at a soil depth of 10 cm.
The results obtained from repeating the same process at 3 m spatial resolution using the Planet Scope images are shown in Table 3. They show that there is a high correlation between the red and the NIR bands on satellite images with a spatial resolution of 300 cm, whereas the correlation between the PDI and soil moisture is lower than the UAV (unmanned aerial vehicle) 4 cm spatial resolution correlation.
A comparison of the PDI and soil moisture can be influenced by plant albedo and shade. The results shown in Figure 7 clearly demonstrate that potatoes at 4 cm resolution correlate less than bare soil and pastures at the same resolution. A comparison of the PDI and soil moisture can be influenced by plant albedo and shade. The results shown in Figure 7 clearly demonstrate that potatoes at 4 cm resolution correlate less than bare soil and pastures at the same resolution.

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. 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

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.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 9 of 16 A comparison of the PDI and soil moisture can be influenced by plant albedo and shade. The results shown in Figure 7 clearly demonstrate that potatoes at 4 cm resolution correlate less than bare soil and pastures at the same resolution.

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. 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 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 Spearman'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).
Appl. Sci. 2020, 10, x FOR PEER REVIEW 12 of 16 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). The index that performs the best on bare soil is the NDWI at any spatial resolution, the NDVI at 4 or 12 cm resolution or from satellite images, and the PDI at 4 cm resolution. On bare soil, the reflectance effect of the existent furrows every 2 m must be considered on coarser scales (Figure 3c),  Statistical significance: p-value is significant at 5% ( ) when it is lower than 0.05, and significant at 1% ( ) when it is lower than 0.01.
On bare soil land use, any resolution can be used to estimate soil moisture by means of optical images with the NDWI.
The index that performs the best on bare soil is the NDWI at any spatial resolution, the NDVI at 4 or 12 cm resolution or from satellite images, and the PDI at 4 cm resolution. On bare soil, the reflectance effect of the existent furrows every 2 m must be considered on coarser scales (Figure 3c), because the land roughness could cause differences on the averaged land reflectance.

Discussion
Farhan and Al Bakri [36] report that the NDVI mainly reflects seasonal vegetation conditions, showing higher correlations with seasonal soil moisture stress, whereas the PDI does not show this relationship.
The NDVI in this research was the vegetation index that performed better on coarser resolution than thinner spatial resolution, regardless of land use.
Both sensing drought indices, the NDVI and the PDI can explain soil moisture variability in all the studied land uses. One study [37] showed significant negative correlations in spring, summer, and autumn between the NDVI and soil moisture, whereas farmland showed a significant positive correlation between NDVI and soil moisture in winter. In the current research, NDVI positively correlates with potato and pasture land uses and negatively correlates with bare soil, possibly due to the higher evaporation on bare soils ( Figure 9).
Bare soils are not affected by vegetation cover, so their reflectivity in red and NIR bands is only affected by the soil moisture content. If there is a decrease in soil moisture, the reflective+ity of the red and NIR bands increases [38]. When vegetation cover increases, reflectance in the NIR band is higher than in the red band. Where land use in the current study plot includes both soil and vegetation, the points scatter inside a triangular region in the NIR-R, as shown in Figure 5. These results on bare soils concur with laboratory reflectance studies [20].
Spatial resolution clearly determines the ability of a sensor to generate the indices that can successfully approximate soil moisture.
The NDVI produced no significant correlations with soil moisture on UAV images, whereas Planet Scope NDVI variants with their higher spectral and spatial resolution positively correlated with bare soils, concurring with [39]. The ease of calculating the NDVI and the high temporal resolution of the data may mean that Sentinel-2 Planet Scope may play a future role in early warning systems of drought, as it enables high-resolution vegetation condition monitoring, which may be useful in detecting the onset of agricultural drought.
In regard to the SAVI, this can only be used with a significant correlation to estimate the soil moisture of bare soils.
The NDWI was the index that performed best on detailed resolutions, especially to study the soil moisture of land use for vegetables such as potatoes, which is useful when considering precision agriculture.
Observing Table 4, it can be seen that on pasture, the most significant correlations are found on coarser scales, whereas bare soil and potatoes have better results on detailed resolutions. These results show that UAV with multispectral cameras are useful to evaluate bare soil and potato soil moisture at detailed scales, and, above all, with the NDWI, SAVI and PDI.

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, Amani et al. [21] found that bare soils have good significance on a coarse scale with Landsat8 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 10cm, as supported by Ghulam 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.