Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

: Surface reﬂectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reﬂectance to compute the vegetation indices and basal crop coe ﬃ cients (K cb ). To evaluate the models statistically, 54 plants were collected in the ﬁeld and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The K cb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the K cb values derived from the Normalized Di ﬀ erence Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R 2 = 0.74) was observed between the actual transpiration of the crop estimated by the K cb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R 2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m − 2 and an R 2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m − 2 , and the R 2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the K cb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture. and L.P.V.; software, R.A.d.S. and R.F.; validation, R.A.d.S., R.F., E.I.F.-F. and E.C.M.; formal analysis, R.A.d.S. and R.F.; investigation, R.A.d.S. and R.F.; resources, E.I.F.-F., R.F., E.C.M. and A.C.B.d.S.; data curation, R.A.d.S.; writing—original preparation, R.A.d.S; writing—review and R.A.d.S., R.F., E.C.M., E.I.F.-F. and L.P.V.; visualization, R.F. and E.C.M.; E.C.M. and E.I.F.-F.; E.C.M.;


Introduction
Modern agriculture faces the major challenge of feeding the world's population, which is growing at an accelerated pace and must become fully sustainable in environmental terms [1]. By 2050, twice the current production of food is expected to be needed to fulfill the needs of the population [2]. For this, the use of water resources in agriculture should be intensified in the coming years to facilitate greater Since water is an important input for agriculture and remote sensing is an important tool in AGB prediction, the present study aimed to apply and evaluate, using an RGNIR sensor onboard a UAV, empirical models based on Kcb to estimate the actual crop evapotranspiration via a normalization of the vegetation indices' Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) values and to apply, using the evapotranspiration parameters, models to estimate the aboveground dry biomass (AGB) of the maize crop.

Study Area
This study was carried out in an area of 2600 m 2 located in the "Prof. Diogo Alves de Mello" experimental field, which belongs to the Federal University of Viçosa (UFV) in the municipality of Viçosa, Minas Gerais State, Brazil ( Figure 1). According to Köppen's climatic classification [23], the climate of the region is Cwa (humid subtropical). The maximum average temperature is 26.7 °C, and the minimum average temperature is 15.4 °C, with an average annual accumulated precipitation of 1250 mm [24]. The soil of this area is classified as deep clay-textured Latossolo Amarelo (Oxisol) [25].

Crop Planting and Management
Maize (Zea mays L.) was sown on 15 October 2018, with a spacing of 0.28 m between plants and 0.50 m between rows. Seedling emergence began on 20 October 2018. At 25 days after emergence (DAE), chemical control of weeds was performed using the active ingredient glyphosate at a dose of 0.89 kg ha −1 . Two fertilizations were performed: one involved sowing with 300 kg ha −1 of the NPK formulation 8:28:16, and the other involved sowing at 38 DAE with nitrogen fertilization at the recommended dose of 300 kg ha −1 using urea as the source.

Field Data
In total, 54 maize plants were collected at 46 (18 plants), 52 (22 plants), and 59 (14 plants) DAE to quantify the aboveground dry biomass (AGB). Different dates were used for collection to follow the temporal variability of the crop; as the maize develops, until it reaches antithesis, the vegetation index also tends to increase. In this way, the sensor was able to identify the spatial and temporal variability. The collections were carried out randomly between plants, as well as between planting rows, avoiding the collection of two nearby plants to avoid the influence of the leaf of one plant on the other, which would be captured by the RGRNIR sensor. After collection, the plants were separately placed in paper bags and dried in a forced-air circulation oven at a temperature of 70 • C for 72 h. After this period, the samples were removed from the oven, and the plants were weighed. Each collected plant was georeferenced with a Global Navigation Satellite System (GNSS) RActual-Time kinematic (RTK) Topomap T10 receiver. This receiver was used due to its high accuracy and to determine the exact location of each plant collected in the field.

Aerial Data
Aerial imaging started from 03 DAE and ended at 61 DAE (Table 1), always at 10 h (UTC/GMT-3). The imaging ended at 61 DAE because of the technical conditions of the device. Innovations ( Figure 2). Phantom line drones are multirotor vehicles [26] with a flight autonomy of approximately 30 min. The sensor (Figure 2b) was a Survey 3 manufactured by the Mapir company. According to the manufacturer, this sensor has three filters that differentiate between the red, green, and near-infrared wavelengths. The wavelength ranges for the respective filters are 640-680, 525-575, and 820-880 nm, respectively [27]. The sensor can capture images at a resolution of 12 Megapixels (4000 × 3000 pixels) and with adjustable time intervals (in the present study, we used 2 s). These images were stored automatically on a micro memory card in the JPEG format with an 8-bit radiometric resolution.
For the images to always have the same pattern of capture (to avoid interfering with the results), flight planning was elaborated using the DroneDeploy application. Flight planning was set to an altitude of 50 m with a front overlap of 85%, a side overlap of 75%, and a velocity fixed at 3 m s −1 . The time spent by the UAV flying in the study area was approximately 3 min. After acquiring the image sets, the images were mosaiced. This process was carried out in the software Agisoft PhotoScan, thereby generating a file in the GeoTIF format with three bands (Red, Green and NIR). The images exported from this software had pixels with a spatial resolution of 0.025 m, which were aggregated to 0.5 m for optimized processing. For the field information to be estimated by the sensor, it was necessary to perform the geometric correction and digital number conversion using the physical values of reflectance to allow for the subsequent calculation of the vegetation indices.

Geometric Correction
The MAPIR survey 3 Sensor uses a GNSS receiver that allows one to capture the georeferenced images. However, the receiver has low accuracy that generates random errors in each image, thus requiring accurate georeferencing. For this purpose, eight wooden pickets were installed in the area designated for the experiment, and their coordinates were obtained by a GNSS RTK Topomap T10 receiver. These pickets served as a control point for georeferencing. Geometric correction was performed in the images after the mosaicking process.

Conversion from Digital Numbers to Physical Values
The sensor onboard the UAV provides image values using digital numbers (DNs), which need to be converted to physical values of reflectance. For this process, five low-cost panels at different grayscales (black; dark grey; intermediate gray; light gray; and white) were used. These panels were made of artificial Napa, a material whose composition is based on PVC, with the textile portion made from polyester. These panels had an area of 1.5 m 2 each and were placed next to the study area on all flights carried out. The reflectance of each panel was determined in the laboratory using a The sensor (Figure 2b) was a Survey 3 manufactured by the Mapir company. According to the manufacturer, this sensor has three filters that differentiate between the red, green, and near-infrared wavelengths. The wavelength ranges for the respective filters are 640-680, 525-575, and 820-880 nm, respectively [27]. The sensor can capture images at a resolution of 12 Megapixels (4000 × 3000 pixels) and with adjustable time intervals (in the present study, we used 2 s). These images were stored automatically on a micro memory card in the JPEG format with an 8-bit radiometric resolution.
For the images to always have the same pattern of capture (to avoid interfering with the results), flight planning was elaborated using the DroneDeploy application. Flight planning was set to an altitude of 50 m with a front overlap of 85%, a side overlap of 75%, and a velocity fixed at 3 m s −1 . The time spent by the UAV flying in the study area was approximately 3 min. After acquiring the image sets, the images were mosaiced. This process was carried out in the software Agisoft PhotoScan, thereby generating a file in the GeoTIF format with three bands (Red, Green and NIR). The images exported from this software had pixels with a spatial resolution of 0.025 m, which were aggregated to 0.5 m for optimized processing. For the field information to be estimated by the sensor, it was necessary to perform the geometric correction and digital number conversion using the physical values of reflectance to allow for the subsequent calculation of the vegetation indices.

Geometric Correction
The MAPIR survey 3 Sensor uses a GNSS receiver that allows one to capture the georeferenced images. However, the receiver has low accuracy that generates random errors in each image, thus requiring accurate georeferencing. For this purpose, eight wooden pickets were installed in the area designated for the experiment, and their coordinates were obtained by a GNSS RTK Topomap T10 receiver. These pickets served as a control point for georeferencing. Geometric correction was performed in the images after the mosaicking process.

Conversion from Digital Numbers to Physical Values
The sensor onboard the UAV provides image values using digital numbers (DNs), which need to be converted to physical values of reflectance. For this process, five low-cost panels at different grayscales (black; dark grey; intermediate gray; light gray; and white) were used. These panels were made of artificial Napa, a material whose composition is based on PVC, with the textile portion made Water 2020, 12, 2359 6 of 20 from polyester. These panels had an area of 1.5 m 2 each and were placed next to the study area on all flights carried out. The reflectance of each panel was determined in the laboratory using a spectroradiometer. With the aid of the QGis ® 2.18 software [28], the mosaiced images were cut based on the area of each panel. Subsequently, the average DNs of the red and infrared bands corresponding to each panel were extracted, and a regression was used to determine an equation of conversion between the DNs of the panels quantified by the MAPIR sensor and their average values of reflectance obtained by the spectroradiometer. This procedure was performed for each image mosaic (i.e., on each imaging occasion).
To improve the computational performance of image processing, pixels of the images were aggregated from 0.025 to 0.5 m. In this way, the images were reduced from 4,100,000 to 10,400 pixels.

Vegetation Indices (VI)
To analyze crop behavior during the crop cycle and apply models of evapotranspiration to the maize crop, the Normalized Difference Vegetation Index (NDVI) developed by Rouse et al. (1974) [29] and the Soil-Adjusted Vegetation Index (SAVI) developed by Huete (1988) [30] were used. These indices were obtained according to Equations (1) and (2), respectively, where ρ NIR is the near-infrared reflectance, ρ R is the red reflectance, and L is the soil adjustment factor. This factor varies according to the canopy density, and values from 0.25 to 1.0 can be adopted. However, the present study adopted 0.5, as recommended by Houte (1998) [30], and applied the general approach by García and Pérez (2016) [31] and Gilabert et al. (2002) [32], and the specific approach for maize crops by Zhang et al. (2019) [33].

Actual Crop Evapotranspiration from VI
After estimating the indices, the actual crop evapotranspiration (ET a, ) was calculated. ET a is the sum of the actual transpiration (T a ) and evaporation (E). The actual transpiration (Equation (3)) is a biophysical process involving water loss through the stomata present in the leaves after being involved in the physiological processes of the plant [34] while considering the soil moisture conditions where K cb is the basal crop coefficient, K s is the stress coefficient, and ET o is the reference evapotranspiration calculated from the meteorological data by the Food and Agriculture Organization (FAO)-56 method [16]. In turn, evaporation (Equation (4)) is a physical process of water loss from the soil or the surface of plants, as water transitions from a liquid form to vapor [16] where K e is the evaporation coefficient.
K cb Estimated by the VI According to Choudhury et al. (1994) [35], there is an exponential relationship between the transpiration coefficient (T c ), shown in Equation (6), and the leaf area index (LAI). Thus, when the LAI is maximal, the crop will have a higher coefficient of transpiration. T c is described as the ratio of transpiration under stress-free conditions to the maximum crop evapotranspiration when the vegetation Water 2020, 12, 2359 7 of 20 fully covers the soil (that is, under higher values of LAI). Thus, K cb is given by the product of the maximum value of K cb (K cbmax ) (the tabulated value [16]) and the coefficient of transpiration (Equation (5)) When no saturation occurs, the maximum value of LAI coincides with the maximum value of VI [35]. Choudhury et al. (1994) [35] replaced LAI with VI to estimate T c (Equation (6)) (6) where n corresponds to the K/K' ratio; K is a coefficient related to the leaf architecture of the crop, varying from 0.5 to 0.7; and K' is a coefficient that varies from 0.5 to 0.7 and from 0.8 to 1.3 when the vegetation indices used are SAVI and NDVI, respectively [35].
Replacing T c in Equation (5) with Equation (6) yields K cb from the VI (Equation (7)) Equation (7) is a generalized model used to determine the K cb of the crop from the VI. The K cbmax adopted for the maize crop was 1.15 [16].
In the present study, the VIs of Equations (1) and (2) were used to determine the K cb of maize throughout its growth. For NDVI, it was only necessary to replace the VI of Equation (7) with the NDVI values, assuming the mean value between coefficients K and K'. For SAVI, the VI parameters of the equation above were replaced; however, according to Choudhury et al. (1994) [35], the ratio between K and K' is equal to 1.

Evaporation Coefficient (K e )
To quantify the K e , it was necessary to determine the daily water balance in the soil over the 61 days of crop monitoring. In the study area, the soil was collected to measure the field capacity on a volumetric basis (θ FC ), the wilting point on a volumetric basis (θ WP ), and the soil bulk density (d s ). The soil of the study area had a θ FC of 0.33 m 3 m −3 , a θ WP of 0.21 m 3 m −3 , and a density of 1.20 kg m −3 . θ FC and θ WP were used to calculate the maximum water depth that could be evaporated, as suggested by Allen et al. (1998) [16], using a 0.10 m layer of soil subject to evaporation. The entire water balance procedure was carried out strictly as described in Bulletin 56 published by FAO [16].
Stress Coefficient (K s ) K s was quantified via the daily soil water balance using Equation (8) [16] where TAW is the total available soil water in the root zone (mm), D r is the moisture depletion, RAW is the readily available water in the root zone, and p is the fraction of TAW that the plant can extract from the root zone without undergoing water stress. TAW is calculated based on the daily rooting depth of the crop (Z r ) along with θ FC and θ WP (Equation (9)) RAW was calculated by Equation (10) RAW = pTAW (10) According to FAO Bulletin 56 [16], the p for a maize crop is 0.55 when the crop potential ET (ET pc ) is equal to 5 mm d −1 . When the ET pc diverges from 5 mm d −1 , Equation (11) is used for correction p = 0.55 + 0.04 5 − ET pc (11) After completing all the processes above, the actual transpiration and evapotranspiration of the crop were determined.

Estimation of Aboveground Dry Biomass
The aboveground dry biomass (AGB) was calculated using the model developed by Monteith (1972) [36] according to Equation (12), which quantifies the daily increase in the dry biomass of the crop based on the water and radiation parameters where AGB is the aboveground dry biomass in kg ha −1 d −1 ; ε max is the maximum radiation use efficiency, which is equal to 3.5 g MJ −1 for maize [19]; E f is the evaporative fraction; APAR is the absorbed photosynthetically active radiation (W m −2 ); and 0.864 is the factor for unit conversion.
The evaporative fraction was replaced by the ratio between the actual crop evapotranspiration and the reference evapotranspiration used by some authors [37,38], which was also adopted in the present study.
The APAR was obtained by the product between the NDVI and incident solar radiation (R s ) [39][40][41], according to Equation (13 To statistically compare the aboveground dry biomass estimated by the models and the aboveground dry biomass observed in the field, the geographic coordinates of each plant collected were used to establish a 1 m radius from the georeferenced point and generate a representative circular area of 3.14 m 2 for each plant collected. This area provided the average biomass increase on each day of the imaging until plant collection. Subsequently, the values of daily biomass were integrated with time to obtain the value of the dry biomass accumulated until the day of the collection.

Validation of Estimated Aboveground Dry Biomass
Since biomass is a parameter measured in the field, validation using measurements of dry biomass was performed to assess the reliability of the models for actual crop evapotranspiration (ET a ) estimated by the VI because it was not possible to measure ET a in the field during the experimental period.
For statistical comparisons between the estimated and observed AGB, the statistics presented in Equations (14)- (17) were applied to quantify the relationships and the errors between the biomass measured in the field and the biomass estimated by the images of the UAV Water 2020, 12, 2359 9 of 20 where Pi is the value predicted by the model, Oi is the observed value, P is the average value obtained by the model, O is the average value observed, and n is the sample number.

K cb Derived from the VIs
The mean values of the vegetation indices (VIs) NDVI and SAVI can be visualized in Figure 3. The VIs were observed to increase over time following plant growth.
Water 2020, 12, x FOR PEER REVIEW 9 of 20

Kcb Derived from the VIs
The mean values of the vegetation indices (VIs) NDVI and SAVI can be visualized in Figure 3. The VIs were observed to increase over time following plant growth. The NDVI values are higher than SAVI values because of the L coefficient used for soil brightness attenuation present in the methodology of the latter. For both indices, greater spatial variability was observed from 03 to 27 DAE, as illustrated by the greater interval between the first and third quartiles of the boxplots. This situation occurred because of the high weed infestation in the experimental area since the crop does not yet fully cover the cultivated area. Ten days after chemical control, this variability was reduced, and only maize plants were present in the area. For NDVI, the maximum recorded value was 0.69, and for SAVI, the maximum value was 0.58-both at 61 DAE. Zhang [43], using terrestrial sensors, found a maximum value of 0.69 for SAVI, noting that maize plants fully covered the soil. Figure 4 presents the mean values of Kcb, Ke, dual Kc, and Ks for the maize crop on each day of the image.
As observed in Figure 3, the VI tends to increase with crop growth. Thus, the Kcb values derived from these indices also tend to increase (Figure 4a) following an increase in the VIs. However, a greater discrepancy is observed with the Kcb determined by the empirical model of NDVI. This result may be related to the coefficient "n" applied to the model, which determines the Kcb via the NDVI proposed by Choudhury et al. (1994) [35], whereas SAVI does not use this coefficient [44].
Studies using Kcb derived from NDVI have demonstrated a good linear relationship, with a coefficient of determination above 0.80 between Kcb and NDVI [12,42]. Likewise, the present study found linearity between these two parameters, with an R 2 of 0.81, whereas SAVI and its relationship with Kcb yielded an R 2 of 0.99. This difference between the two indices is related to the aforementioned coefficient "n".
By using the NDVI index to estimate Kcb, a saturation of the vegetation index may occur, especially for plants with a C4 metabolism. This means that the plant will continue to develop, but the Kcb estimated by the NDVI will remain stable at a maximum limit. However, according to Duchemin et al. [44], the impact of NDVI saturation is not critical for the estimation of transpiration. The SAVI index, on the other hand, due to the L coefficient of soil brightness attenuation, does not The NDVI values are higher than SAVI values because of the L coefficient used for soil brightness attenuation present in the methodology of the latter. For both indices, greater spatial variability was observed from 03 to 27 DAE, as illustrated by the greater interval between the first and third quartiles of the boxplots. This situation occurred because of the high weed infestation in the experimental area since the crop does not yet fully cover the cultivated area. Ten days after chemical control, this variability was reduced, and only maize plants were present in the area. For NDVI, the maximum recorded value was 0.69, and for SAVI, the maximum value was 0.58-both at 61 DAE. Zhang et al.  [43], using terrestrial sensors, found a maximum value of 0.69 for SAVI, noting that maize plants fully covered the soil. Figure 4 presents the mean values of K cb , K e , dual K c , and K s for the maize crop on each day of the image.
As observed in Figure 3, the VI tends to increase with crop growth. Thus, the K cb values derived from these indices also tend to increase (Figure 4a) following an increase in the VIs. However, a greater discrepancy is observed with the K cb determined by the empirical model of NDVI. This result may be related to the coefficient "n" applied to the model, which determines the K cb via the NDVI proposed by Choudhury et al. (1994) [35], whereas SAVI does not use this coefficient [44].
Studies using K cb derived from NDVI have demonstrated a good linear relationship, with a coefficient of determination above 0.80 between K cb and NDVI [12,42]. Likewise, the present study found linearity between these two parameters, with an R 2 of 0.81, whereas SAVI and its relationship with K cb yielded an R 2 of 0.99. This difference between the two indices is related to the aforementioned coefficient "n". show saturation and may be more appropriate for reducing errors in the estimation of Kcb and subsequent transpiration. Contrary to the Kcb value (Figure 4a), Ke (Figure 4b) tends to decrease as the crop grows and develops. This is a normal condition, as the effect of this coefficient is reduced with an increase in vegetation cover. The expansion of leaf area intercepts solar radiation, which can reach the ground, reducing the latent heat of vaporization. Consequently, with less energy interacting with the moist soil, there will be less evaporation of water from the soil. Another factor affecting the value of Ke is the soil moisture condition, as moister and more exposed soil has greater potential for evaporation [45]. Figure 4b shows that the reduction in Ke is not constant, with variations in the values caused mainly by precipitation ( Table 1). As observed for Kcb, the values of Ke differ in the same image based on the parameter's calculation, as proposed by the FAO Bulletin 56 [16], which takes into account the values of Kcb. Figure 4c presents the value of the dual Kc obtained by the sum of Kcb and Ke. Throughout the crop cycle, the dual Kc values were high, almost always close to 1.00. At the beginning of the cycle, the dual Kc values were influenced mainly by the high value of Ke due to the availability of water in the soil and decreased with maize growth. In Figure 4c, it is possible to observe that some values of dual Kc exceed 1.20. Theoretically, this should not happen because, when calculating Ke, the value of 1.20 is adopted as the maximum Kc allowed for the maize crop [16]. Thus, until the moment that Ke exerts influence on the dual Kc, the latter's value should not exceed 1.20, and, when Ke ceases to exert influence, this value should not exceed 1.15 (maximum Kcb for maize). However, due to the presence of weeds and excess moisture in the evaporating layer, higher values of Kcb occurred at the beginning of the cycle.
During the monitoring, a total precipitation of 436.00 mm (Table 1) was recorded, with occurrence at least once a week until 52 DAE. From that day until 61 DAE, there was no record of precipitation. Thus, the soil was expected to remain close to field capacity until 52 DAE. Figure 4d presents the stress coefficient (Ks) in the experimental area, where mild stress was observed at 27 DAE due to low precipitation (7 mm) over an interval of 3 days. Ks decreased slightly after 56 DAE By using the NDVI index to estimate K cb , a saturation of the vegetation index may occur, especially for plants with a C4 metabolism. This means that the plant will continue to develop, but the K cb estimated by the NDVI will remain stable at a maximum limit. However, according to Duchemin et al. [44], the impact of NDVI saturation is not critical for the estimation of transpiration. The SAVI index, on the other hand, due to the L coefficient of soil brightness attenuation, does not show saturation and may be more appropriate for reducing errors in the estimation of K cb and subsequent transpiration.
Contrary to the K cb value (Figure 4a), K e (Figure 4b) tends to decrease as the crop grows and develops. This is a normal condition, as the effect of this coefficient is reduced with an increase in vegetation cover. The expansion of leaf area intercepts solar radiation, which can reach the ground, reducing the latent heat of vaporization. Consequently, with less energy interacting with the moist soil, there will be less evaporation of water from the soil. Another factor affecting the value of K e is the soil moisture condition, as moister and more exposed soil has greater potential for evaporation [45]. Figure 4b shows that the reduction in K e is not constant, with variations in the values caused mainly by precipitation ( Table 1). As observed for K cb , the values of K e differ in the same image based on the parameter's calculation, as proposed by the FAO Bulletin 56 [16], which takes into account the values of K cb . Figure 4c presents the value of the dual K c obtained by the sum of K cb and K e . Throughout the crop cycle, the dual K c values were high, almost always close to 1.00. At the beginning of the cycle, the dual K c values were influenced mainly by the high value of K e due to the availability of water in the soil and decreased with maize growth. In Figure 4c, it is possible to observe that some values of dual K c exceed 1.20. Theoretically, this should not happen because, when calculating K e , the value of 1.20 is adopted as the maximum K c allowed for the maize crop [16]. Thus, until the moment that K e exerts influence on the dual K c , the latter's value should not exceed 1.20, and, when K e ceases to exert influence, this value should not exceed 1.15 (maximum K cb for maize). However, due to the presence of weeds and excess moisture in the evaporating layer, higher values of K cb occurred at the beginning of the cycle.
During the monitoring, a total precipitation of 436.00 mm (Table 1) was recorded, with occurrence at least once a week until 52 DAE. From that day until 61 DAE, there was no record of precipitation.
Thus, the soil was expected to remain close to field capacity until 52 DAE. Figure 4d presents the stress coefficient (K s ) in the experimental area, where mild stress was observed at 27 DAE due to low precipitation (7 mm) over an interval of 3 days. K s decreased slightly after 56 DAE and more sharply at 59 and 61 DAE. As there was a drought period from 52 DAE, the water present in the effective root system zone was demanded by the atmosphere through the processes of evaporation and transpiration, which, consequently, reduced the water content available for the crop (i.e., by increasing moisture depletion in this zone and causing the plant to spend more energy to remove the water chemically bound to the soil). The SAVI model used to determine K cb was more sensitive to water stress because this model tends to have a higher basal coefficient, thereby influencing the greater potential transpiration of the crop.

Actual Transpiration and Evapotranspiration of the Maize Crop
The actual transpiration of the crop (T a ) is presented in Figure 5. Low values of T a occurred at the beginning of the cycle due to the lower vegetation cover (i.e., the smaller the photosynthetic component, the less the transpiration).
Water 2020, 12, x FOR PEER REVIEW 11 of 20 and more sharply at 59 and 61 DAE. As there was a drought period from 52 DAE, the water present in the effective root system zone was demanded by the atmosphere through the processes of evaporation and transpiration, which, consequently, reduced the water content available for the crop (i.e., by increasing moisture depletion in this zone and causing the plant to spend more energy to remove the water chemically bound to the soil). The SAVI model used to determine Kcb was more sensitive to water stress because this model tends to have a higher basal coefficient, thereby influencing the greater potential transpiration of the crop.

Actual Transpiration and Evapotranspiration of the Maize Crop
The actual transpiration of the crop (Ta) is presented in Figure 5. Low values of Ta occurred at the beginning of the cycle due to the lower vegetation cover (i.e., the smaller the photosynthetic component, the less the transpiration).  At 14 DAE, it is possible to observe isolated areas in the image with transpiration around 3 mm caused by the incidence of weeds in the area with an ET o of 4.58 mm (Table 1). In the images for 19 DAE, the weeds showed more advanced development than those in the previous images, but their T a was lower due to the ET on this day (3.29 mm). At 24 DAE, there was high variability of T a influenced by the strong weed development and high value of ET o (4.34 mm).

Ta-SAVI
After herbicide application (25 DAE), there was a reduction in the T a rate influenced by the effects of the toxicity of the active ingredient on the weeds. Glyphosate prevents CO 2 assimilation by reducing the stomatal conductance and, consequently, the transpiration of the plants [46]. Another factor that influenced the T a on this day was K s (Figure 4d), which reduced the transpiration of the crop via water stress. From 35 DAE, only the maize crop prevailed in the area, so transpiration from the experimental area was influenced by both the crop and the environmental conditions during imaging. At 46 and 52 DAE, there was a sudden reduction in T a . This occurrence was attributed to the low radiation on these days, which reduced the photosynthesis and transpiration rates along with the low estimated value of ET o ( Table 1). The image for day 56 was strongly influenced by ET o (Table 1) and by the absence of water stress, K s = 1.00 (Figure 4d). Thus, at 56 DAE, the crop had higher T a . For days 59 and 61, crop transpiration was limited by the water stress (K s < 1) caused by drought. Thus, although the crop had high vegetative vigor, its values of ET o were close to 6 mm (Table 1), so the T a was lower than 5.5 mm on average.
The T a determined by the empirical model of K cb derived from the NDVI showed greater spatial variability than the empirical model of K cb derived from SAVI. This phenomenon can be observed in Figures 5 and 6. In Figure 6a, a greater interval than that in Figure 6b can be clearly observed between the first and third quartiles. The discrepancy between the models is related to the aforementioned coefficient "n".
Water 2020, 12, x FOR PEER REVIEW 12 of 20 At 14 DAE, it is possible to observe isolated areas in the image with transpiration around 3 mm caused by the incidence of weeds in the area with an ETo of 4.58 mm (Table 1). In the images for 19 DAE, the weeds showed more advanced development than those in the previous images, but their Ta was lower due to the ET on this day (3.29 mm). At 24 DAE, there was high variability of Ta influenced by the strong weed development and high value of ETo (4.34 mm).
After herbicide application (25 DAE), there was a reduction in the Ta rate influenced by the effects of the toxicity of the active ingredient on the weeds. Glyphosate prevents CO2 assimilation by reducing the stomatal conductance and, consequently, the transpiration of the plants [46]. Another factor that influenced the Ta on this day was Ks (Figure 4d), which reduced the transpiration of the crop via water stress. From 35 DAE, only the maize crop prevailed in the area, so transpiration from the experimental area was influenced by both the crop and the environmental conditions during imaging. At 46 and 52 DAE, there was a sudden reduction in Ta. This occurrence was attributed to the low radiation on these days, which reduced the photosynthesis and transpiration rates along with the low estimated value of ETo ( Table 1). The image for day 56 was strongly influenced by ETo (Table  1) and by the absence of water stress, Ks = 1.00 (Figure 4d). Thus, at 56 DAE, the crop had higher Ta. For days 59 and 61, crop transpiration was limited by the water stress (Ks < 1) caused by drought. Thus, although the crop had high vegetative vigor, its values of ETo were close to 6 mm (Table 1), so the Ta was lower than 5.5 mm on average.
The Ta determined by the empirical model of Kcb derived from the NDVI showed greater spatial variability than the empirical model of Kcb derived from SAVI. This phenomenon can be observed in Figures 5 and 6. In Figure 6a, a greater interval than that in Figure 6b can be clearly observed between the first and third quartiles. The discrepancy between the models is related to the aforementioned coefficient "n". The transpiration of the crop facilitates the accumulation of AGB, since the efflux of water through the stomatal opening occurs simultaneously with the influx of CO2, and the latter is related to the photosynthetic process [34,47]. Past studies demonstrated that an increase in CO2 entering into the maize plant, up to a certain limit, positively impacts the increase in its biomass (with water available in the soil) [17,48,49]. Thus, the transpiration model most strongly related to AGB has greater potential for predicting transpiration. The transpiration of the crop facilitates the accumulation of AGB, since the efflux of water through the stomatal opening occurs simultaneously with the influx of CO 2 , and the latter is related to the photosynthetic process [34,47]. Past studies demonstrated that an increase in CO 2 entering into the maize plant, up to a certain limit, positively impacts the increase in its biomass (with water available in the soil) [17,48,49]. Thus, the transpiration model most strongly related to AGB has greater potential for predicting transpiration. Figure 7 presents the relationship between the AGB of maize plants collected in the field and the accumulation of the actual crop transpiration for the models derived from the NDVI (Figure 7a) and SAVI (Figure 7b). This Figure shows that the empirical model based on SAVI has a higher predictive capacity than the model based on NDVI, providing an R 2 of 0.74 for the maize biomass. Campos et al. (2018b) [50], using values of K cb derived from SAVI and corrected by water and thermal stress coefficients, recorded an R 2 of 0.94 for the biomass of the same crop in rainfed areas and 0.96 in irrigated areas, albeit using satellite images, unlike the present study, which used low-cost sensors. Twohey et al. (2019) [51], measuring the direct transpiration of maize in a greenhouse, found an R 2 of 0.92 for the maize's biomass. The predictive capacity of transpiration using a low-cost sensor onboard a UAV is important for the agricultural monitoring of crop water conditions in areas with low investment capital where it is not possible to perform orbital imaging. Figure 7 presents the relationship between the AGB of maize plants collected in the field and the accumulation of the actual crop transpiration for the models derived from the NDVI (Figure 7a) and SAVI (Figure 7b). This Figure shows that the empirical model based on SAVI has a higher predictive capacity than the model based on NDVI, providing an R 2 of 0.74 for the maize biomass. Campos et al. (2018b) [50], using values of Kcb derived from SAVI and corrected by water and thermal stress coefficients, recorded an R 2 of 0.94 for the biomass of the same crop in rainfed areas and 0.96 in irrigated areas, albeit using satellite images, unlike the present study, which used low-cost sensors. Twohey et al. (2019) [51], measuring the direct transpiration of maize in a greenhouse, found an R 2 of 0.92 for the maize's biomass. The predictive capacity of transpiration using a low-cost sensor onboard a UAV is important for the agricultural monitoring of crop water conditions in areas with low investment capital where it is not possible to perform orbital imaging. The actual evapotranspiration of the crop (ETa) obtained by the coefficients of the plant, soil, and ETo is shown in Figure 8. The ETa is high at the beginning of the crop cycle, unlike the values of Tr, especially when the soil is not protected from the vegetation cover. These higher values of ETa are due to the evaporation of water present in the soil. This indicates that transpiration was low during these days and that evaporation from the diffusion of water vapor present in the evaporative layer was high. These situations were also observed by Rosa et al. (2016) [45] when studying dual Kc in a maize crop under field conditions. The same behavior of spatial and temporal variability observed for Ta was also observed for ETa. The influence of the evaporative component occurs until 56 DAE (Figure 4b). From this day, the water demand of the area is due to the transpiration component. Figure 9 illustrates the influence of Ke on the water demand of the crop. Here, the average of the boxplots starts above 2 mm, contrary to Figure 6.
The methodology based on dual Kc normally tends to present higher values of evapotranspiration at the beginning of the crop cycle because it considers the quantification of two isolated coefficients that are summed [45]. Conversely, when the water content is quantified using a single Kc, the estimate of water demand in the initial stages of the crop tends to be lower, since this Kc represents the mean value between the evaporation of soil water and the transpiration of the crop [52]. The actual evapotranspiration of the crop (ET a ) obtained by the coefficients of the plant, soil, and ET o is shown in Figure 8. The ET a is high at the beginning of the crop cycle, unlike the values of T r , especially when the soil is not protected from the vegetation cover. These higher values of ET a are due to the evaporation of water present in the soil. This indicates that transpiration was low during these days and that evaporation from the diffusion of water vapor present in the evaporative layer was high. These situations were also observed by Rosa et al. (2016) [45] when studying dual K c in a maize crop under field conditions. The same behavior of spatial and temporal variability observed for T a was also observed for ET a . The influence of the evaporative component occurs until 56 DAE (Figure 4b). From this day, the water demand of the area is due to the transpiration component. Figure 9 illustrates the influence of K e on the water demand of the crop. Here, the average of the boxplots starts above 2 mm, contrary to Figure 6.
The methodology based on dual K c normally tends to present higher values of evapotranspiration at the beginning of the crop cycle because it considers the quantification of two isolated coefficients that are summed [45]. Conversely, when the water content is quantified using a single K c , the estimate of water demand in the initial stages of the crop tends to be lower, since this K c represents the mean value between the evaporation of soil water and the transpiration of the crop [52]. Water 2020, 12, x FOR PEER REVIEW 14 of 20

ETa-NDVI
ETa-SAVI  Figure 10 shows the spatial and temporal variation of the daily increase in aboveground dry biomass (AGB) using the evaporative fraction derived from the NDVI and SAVI. Until 24 DAE, the daily increase was promoted by both the crop (still at the beginning of its growth) and the weeds present in the area, as discussed earlier. After day 27, the largest daily increases occurred at 40 and 56 DAE, and the lowest increases occurred at 46 and 52 DAE for both models. On days 40 and 56, the daily incident solar radiation was equivalent to 23.38 and 29.25 MJ m −2 d −1 (Table 1), respectively. At 46 and 52 DAE, the radiation was 14.67 and 15.07 MJ m −2 d −1 (Table 1), respectively. Thus, radiation promoted gains in the daily increase in biomass.

Estimation of AGB
Water 2020, 12, x FOR PEER REVIEW 15 of 20 Figure 10 shows the spatial and temporal variation of the daily increase in aboveground dry biomass (AGB) using the evaporative fraction derived from the NDVI and SAVI. Until 24 DAE, the daily increase was promoted by both the crop (still at the beginning of its growth) and the weeds present in the area, as discussed earlier. After day 27, the largest daily increases occurred at 40 and 56 DAE, and the lowest increases occurred at 46 and 52 DAE for both models. On days 40 and 56, the daily incident solar radiation was equivalent to 23.38 and 29.25 MJ m −2 d −1 (Table 1), respectively. At 46 and 52 DAE, the radiation was 14.67 and 15.07 MJ m −2 d −1 (Table 1), respectively. Thus, radiation promoted gains in the daily increase in biomass. Solar radiation is the parameter used to calculate the photosynthetically active radiation established in Equation (13). Thus, the higher the radiation is, the more the biomass will increase. Solar radiation stimulates the photosynthesis, formation, and development of plant organs, as well as biomass and grain yield [53]. Thus, the larger the amount of radiation reaching the crop is, the higher the photosynthetic rate and increase in biomass will be. Another factor that influences biomass Solar radiation is the parameter used to calculate the photosynthetically active radiation established in Equation (13). Thus, the higher the radiation is, the more the biomass will increase. Solar radiation stimulates the photosynthesis, formation, and development of plant organs, as well as biomass and grain yield [53]. Thus, the larger the amount of radiation reaching the crop is, the higher the photosynthetic rate and increase in biomass will be. Another factor that influences biomass production when using the model in Equation (12) is the evaporative fraction, which is the ratio between the actual evapotranspiration of the crop and the reference evapotranspiration. In the images for 59 and 61 DAE, the stress coefficient was below 1 (Figure 4d), thereby reducing the evaporative fraction and, consequently, reducing the daily increase in biomass. Some recent studies have shown the negative influence of water stress on maize biomass [54][55][56][57]. However, this premise has already been scientifically consolidated. Figure 11 presents the same behavior of variability for ET a (Figure 8). The first and third quartiles of Figure 11a are higher than those of Figure 11b because the input evaporative fraction in the model comes from ET a .

Estimation of AGB
Water 2020, 12, x FOR PEER REVIEW 16 of 20 production when using the model in Equation (12) is the evaporative fraction, which is the ratio between the actual evapotranspiration of the crop and the reference evapotranspiration. In the images for 59 and 61 DAE, the stress coefficient was below 1 (Figure 4d), thereby reducing the evaporative fraction and, consequently, reducing the daily increase in biomass. Some recent studies have shown the negative influence of water stress on maize biomass [54][55][56][57]. However, this premise has already been scientifically consolidated. Figure 11 presents the same behavior of variability for ETa ( Figure 8). The first and third quartiles of Figure 11a are higher than those of Figure 11b because the input evaporative fraction in the model comes from ETa. The statistical metrics can be visualized in Figure 12 along with a regression in the estimated accumulated AGB versus the observed AGB. The AGB estimated using the evaporative fraction derived from the NDVI tends to overestimate the true values by 0.033 kg m −2 on average, with an RMSE of 0.104 kg m −2 and an R 2 of 0.74 ( Figure  12a). The estimation of AGB using the evaporative fraction derived from the SAVI model tends to overestimate the true values, on average, by 0.006 kg m −2 , with an RMSE of 0.092 kg m −2 and an R 2 of 0.76 (Figure 12b). Thus, using a model based on SAVI to calculate the Kcb offers demonstrably superior performance for estimating the AGB.
In general terms, using an RGNIR sensor to quantify the evaporative fraction and applying the model to estimate the increase in daily biomass proved to be relatively precise. This makes the use of The statistical metrics can be visualized in Figure 12 along with a regression in the estimated accumulated AGB versus the observed AGB.
Water 2020, 12, x FOR PEER REVIEW 16 of 20 production when using the model in Equation (12) is the evaporative fraction, which is the ratio between the actual evapotranspiration of the crop and the reference evapotranspiration. In the images for 59 and 61 DAE, the stress coefficient was below 1 (Figure 4d), thereby reducing the evaporative fraction and, consequently, reducing the daily increase in biomass. Some recent studies have shown the negative influence of water stress on maize biomass [54][55][56][57]. However, this premise has already been scientifically consolidated. Figure 11 presents the same behavior of variability for ETa ( Figure 8). The first and third quartiles of Figure 11a are higher than those of Figure 11b because the input evaporative fraction in the model comes from ETa. The statistical metrics can be visualized in Figure 12 along with a regression in the estimated accumulated AGB versus the observed AGB. The AGB estimated using the evaporative fraction derived from the NDVI tends to overestimate the true values by 0.033 kg m −2 on average, with an RMSE of 0.104 kg m −2 and an R 2 of 0.74 ( Figure  12a). The estimation of AGB using the evaporative fraction derived from the SAVI model tends to overestimate the true values, on average, by 0.006 kg m −2 , with an RMSE of 0.092 kg m −2 and an R 2 of 0.76 (Figure 12b). Thus, using a model based on SAVI to calculate the Kcb offers demonstrably superior performance for estimating the AGB.
In general terms, using an RGNIR sensor to quantify the evaporative fraction and applying the model to estimate the increase in daily biomass proved to be relatively precise. This makes the use of The AGB estimated using the evaporative fraction derived from the NDVI tends to overestimate the true values by 0.033 kg m −2 on average, with an RMSE of 0.104 kg m −2 and an R 2 of 0.74 (Figure 12a). The estimation of AGB using the evaporative fraction derived from the SAVI model tends to overestimate the true values, on average, by 0.006 kg m −2 , with an RMSE of 0.092 kg m −2 and an R 2 of 0.76 (Figure 12b). Thus, using a model based on SAVI to calculate the K cb offers demonstrably superior performance for estimating the AGB.
In general terms, using an RGNIR sensor to quantify the evaporative fraction and applying the model to estimate the increase in daily biomass proved to be relatively precise. This makes the use of an RGNIR camera a viable alternative for predicting the AGB, which is extremely important for farmers who need greater spatial and predictive details for their crops.

Conclusions
The use of an RGNIR sensor onboard a UAV was able to identify the spatial and temporal variability of a maize crop, along with its vegetative development. This result is important because the present device has a low acquisition cost. When applying the NDVI and SAVI vegetation indices, it was possible to note disturbances in the experimental area, caused mainly by weed infestations and water stress.
The K cb estimated with the RGNIR camera using empirical models based on the vegetation indices proved to be a viable alternative for water management in agricultural crops, which is of great interest in small rural properties due to the low cost and versatility of the equipment.
The actual transpiration of the crop using the K cb derived from SAVI had a better correlation with the biomass collected in the field compared to the transpiration calculated by the K cb derived from the NDVI.
The statistical analyses revealed that the biomass estimated using the models of the evaporative fraction derived from the vegetation indices tends to overestimate the biomass measured in the field. Estimating the biomass using the evaporative fraction derived from SAVI was more satisfactory, presenting fewer errors and a better coefficient of determination than the model derived from NDVI.
The results achieved in the present study make it possible to spatially manage water in agriculture using UAV and RGNIR sensors, thereby integrating irrigation management into the assumptions of precision irrigation.