*2.4. Data Processing and Analysis*

**3. Results**

The 60 GS–NDVI measurements acquired from each of the five zones were first averaged to create a single representative value for each zone. A map was then compiled of these five GS–NDVI values using the ArcMap software (ESRI, Redlands, CA, USA).

The UAV images were processed in three steps as shown in Figure 3. First, the original images were stitched and geometrically and radiometrically corrected using the Pix4D mapper software (Pix 4D, Inc., Lausanne, Switzerland). Using these images, the UAV–NDVI values were computed using the red and NIR bands of the RedEdge-M camera: *Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 6 of 15

$$\text{UAV} - \text{NDVI}(668\text{ nm}, 860\text{ nm}) = \frac{R\_{840} - R\_{668}}{R\_{840} + R\_{668}}\tag{2}$$

where *R*<sup>840</sup> is the reflectance of near-infrared light (840 nm) and *R*<sup>668</sup> is the reflectance of red light (668 nm). NDVIs was determined using the Standardized Regression Coefficients [31] analysis which reflects the relative importance of different variables. This analysis was conducted using the SPSS® software package (IBM, NY, USA) to evaluate the order of influence.

**Figure 3.** Image processing workflow. **Figure 3.** Image processing workflow.

**Figure 4.** Map of the five zones (F1–F5) and their average normalized difference vegetation index

In this experiment, we analyzed the influence of the FA on the UAV–NDVIs. The resulted, shown in Figure 5, suggest five trends: (i) the mean and maximum UAV–NDVIs increase with decreasing FA (Figure 5a,c); (ii) the minimum UAV–NDVIs increase with increasing FA (Figure 5b); (iii) the standard deviation values decrease with increasing FA (Figure 5d); (iv) the mean, minimum

(NDVI) values (GS–NDVIs) based on ground data.

*3.2. Effects of the Flight Altitude (FA) on the NDVI Values*

to the fertilizer strategy and the zones thus represent different growth levels of paddy rice.

In Figure 4, the mean GS–NDVI values, obtained through ground measurements, are shown. As can be seen in figure, there is a trend from the highest NDVI values in zone F5 toward the lowest NDVI values in zone F1. This trend is consistent with fertilizer use, shown in Figure 2, that increases from zone F1 toward zone F5. Therefore, it is obvious that the differences in the NDVI values are due

Second, five shapefiles were created to represent each zone using ArcMap. Finally, the Zonal Statistics tool of ArcMap was used to calculate the mean, minimum, maximum and standard deviation (STDEV) of the pixel-scale UAV–NDVI data. These statistics were calculated separately for the five zones, represented by the five shapefiles created in step two. Of the collected values, the mean UAV–NDVI reflects the mean productivity and biomass, and the standard deviation indicates the spatial variability in productivity. The minimum and maximum UAV–NDVIs can provide information on pixel mixing. The count of pixels (COP) of a certain NDVI can give indicators of the NDVI distribution and is determined by the ground sampling distance (GSD), which in turn is influenced by the FA. Therefore, the COPs of each NDVI value were computed to analyze the effect of the FA on the UAV–NDVIs. GreenSeeker instrument has its internal light source and hence, the effects of the TOD are negligent in the ground data. The significance of the effects of the FA, TOD, SZA and growth level on the UAV– NDVIs was determined using the Standardized Regression Coefficients [31] analysis which reflects the relative importance of different variables. This analysis was conducted using the SPSS® software package (IBM, NY, USA) to evaluate the order of influence.

*Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 6 of 15

In addition to the aforementioned, a total of 48 SZAs were calculated using the NOAA Solar Calculator tool (https://www.esrl.noaa.gov) that was given the location of the study site and the TOD of each flight mission. From herein, identifiers M6–M17 are used to represent each TOD. For example, M6 stands for TOD from 6 a.m. to 7 a.m., and M13 represents the TOD from 1 p.m. to 2 p.m., and so on. It should be noted that this experiment was only conducted for the UAV data because the GreenSeeker instrument has its internal light source and hence, the effects of the TOD are negligent in the ground data. The significance of the effects of the FA, TOD, SZA and growth level on the UAV–NDVIs was determined using the Standardized Regression Coefficients [31] analysis which reflects the relative importance of different variables. This analysis was conducted using the SPSS® software package (IBM, NY, USA) to evaluate the order of influence. **Figure 3.** Image processing workflow.

#### **3. Results 3. Results**

#### *3.1. The Rice Growth Levels Determined by the GS–NDVIs 3.1. The Rice Growth Levels Determined by the GS–NDVIs*

In Figure 4, the mean GS–NDVI values, obtained through ground measurements, are shown. As can be seen in figure, there is a trend from the highest NDVI values in zone F5 toward the lowest NDVI values in zone F1. This trend is consistent with fertilizer use, shown in Figure 2, that increases from zone F1 toward zone F5. Therefore, it is obvious that the differences in the NDVI values are due to the fertilizer strategy and the zones thus represent different growth levels of paddy rice. In Figure 4, the mean GS–NDVI values, obtained through ground measurements, are shown. As can be seen in figure, there is a trend from the highest NDVI values in zone F5 toward the lowest NDVI values in zone F1. This trend is consistent with fertilizer use, shown in Figure 2, that increases from zone F1 toward zone F5. Therefore, it is obvious that the differences in the NDVI values are due to the fertilizer strategy and the zones thus represent different growth levels of paddy rice.

**Figure 4.** Map of the five zones (F1–F5) and their average normalized difference vegetation index (NDVI) values (GS–NDVIs) based on ground data. **Figure 4.** Map of the five zones (F1–F5) and their average normalized difference vegetation index (NDVI) values (GS–NDVIs) based on ground data.

#### *3.2. Effects of the Flight Altitude (FA) on the NDVI Values 3.2. E*ff*ects of the Flight Altitude (FA) on the NDVI Values*

In this experiment, we analyzed the influence of the FA on the UAV–NDVIs. The resulted, shown in Figure 5, suggest five trends: (i) the mean and maximum UAV–NDVIs increase with In this experiment, we analyzed the influence of the FA on the UAV–NDVIs. The resulted, shown in Figure 5, suggest five trends: (i) the mean and maximum UAV–NDVIs increase with decreasing FA (Figure 5a,c); (ii) the minimum UAV–NDVIs increase with increasing FA (Figure 5b); (iii) the standard deviation values decrease with increasing FA (Figure 5d); (iv) the mean, minimum and maximum UAV–NDVIs increase from zone F1 to zone F5 and (v) the standard deviation values decrease from zone F1 to zone F5. These trends were based on the obtained UAV–NDVI values throughout the day. *Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 7 of 15 and maximum UAV–NDVIs increase from zone F1 to zone F5 and (v) the standard deviation values decrease from zone F1 to zone F5. These trends were based on the obtained UAV–NDVI values throughout the day. and maximum UAV–NDVIs increase from zone F1 to zone F5 and (v) the standard deviation values decrease from zone F1 to zone F5. These trends were based on the obtained UAV–NDVI values throughout the day.

*Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 7 of 15

**Figure 5.** Mean (**a**), minimum (**b**), maximum (**c**) and standard deviation values (**d**) of the UAV–NDVIs of the five zones (F1–F5) at different flight altitudes. **Figure 5.** Mean (**a**), minimum (**b**), maximum (**c**) and standard deviation values (**d**) of the UAV–NDVIs of the five zones (F1–F5) at different flight altitudes. **Figure 5.** Mean (**a**), minimum (**b**), maximum (**c**) and standard deviation values (**d**) of the UAV–NDVIs of the five zones (F1–F5) at different flight altitudes.

In order to further analyze the COP differences of the UAV–NDVIs in the field‐scale, the COP distribution curves of the UAV–NDVIs were analyzed under different FAs for fields B1–B5 (Figure 6). It could be seen that, on the whole, the higher the FA, the lower the COP, the smaller the NDVI value of the peak COP and the smaller the NDVI value overall. For instance, in B1 (Figure 6a), the peak COP NDVI values were 0.785, 0.775, 0.755 and 0.755 for 40 m, 60 m, 80 m, 100 m, respectively, and the COPs of these peak NDVI values were 7820, 3808, 2162 and 1596, respectively. The mean UAV–NDVIs of the FAs from 40 m to 100 m were 0.766, 0.756, 0.742 and 0.733. The same tendency could be observed in the other fields. In order to further analyze the COP differences of the UAV–NDVIs in the field-scale, the COP distribution curves of the UAV–NDVIs were analyzed under different FAs for fields B1–B5 (Figure 6). It could be seen that, on the whole, the higher the FA, the lower the COP, the smaller the NDVI value of the peak COP and the smaller the NDVI value overall. For instance, in B1 (Figure 6a), the peak COP NDVI values were 0.785, 0.775, 0.755 and 0.755 for 40 m, 60 m, 80 m, 100 m, respectively, and the COPs of these peak NDVI values were 7820, 3808, 2162 and 1596, respectively. The mean UAV–NDVIs of the FAs from 40 m to 100 m were 0.766, 0.756, 0.742 and 0.733. The same tendency could be observed in the other fields. In order to further analyze the COP differences of the UAV–NDVIs in the field‐scale, the COP distribution curves of the UAV–NDVIs were analyzed under different FAs for fields B1–B5 (Figure 6). It could be seen that, on the whole, the higher the FA, the lower the COP, the smaller the NDVI value of the peak COP and the smaller the NDVI value overall. For instance, in B1 (Figure 6a), the peak COP NDVI values were 0.785, 0.775, 0.755 and 0.755 for 40 m, 60 m, 80 m, 100 m, respectively, and the COPs of these peak NDVI values were 7820, 3808, 2162 and 1596, respectively. The mean UAV–NDVIs of the FAs from 40 m to 100 m were 0.766, 0.756, 0.742 and 0.733. The same tendency could be observed in the other fields.

**Figure 6.** Count of pixels (COP) distribution curves of the UAV–NDVIs of the B1–B5 (**a**–**e**) fields under **Figure 6.** Count of pixels (COP) distribution curves of the UAV–NDVIs of the B1–B5 (**a**–**e**) fields under different flight altitudes at the time of day (TOD) of 11 am–12 pm (M11). **Figure 6.** Count of pixels (COP) distribution curves of the UAV–NDVIs of the B1–B5 (**a**–**e**) fields under different flight altitudes at the time of day (TOD) of 11 a.m.–12 p.m. (M11).

#### *3.3. Effects of the Time of Day (TOD) on the NDVI Values 3.3. E*ff*ects of the Time of Day (TOD) on the NDVI Values*

different flight altitudes at the time of day (TOD) of 11 am–12 pm (M11).

*3.3. Effects of the Time of Day (TOD) on the NDVI Values* Figure 7 showed the trends of the UAV–NDVIs of the five zones (F1–F5) as a function of the TOD and FA. As can be seen in figure, the general trend of the mean UAV–NDVIs was nearly uniform across all zones and FAs. This suggests that the mean, minimum and maximum UAV–NDVIs were Figure 7 showed the trends of the UAV–NDVIs of the five zones (F1–F5) as a function of the TOD and FA. As can be seen in figure, the general trend of the mean UAV–NDVIs was nearly uniform across all zones and FAs. This suggests that the mean, minimum and maximum UAV–NDVIs were highest during the morning (TODs: M6–M9) and late afternoon (TODs: M13–M17) and lowest around Figure 7 showed the trends of the UAV–NDVIs of the five zones (F1–F5) as a function of the TOD and FA. As can be seen in figure, the general trend of the mean UAV–NDVIs was nearly uniform across all zones and FAs. This suggests that the mean, minimum and maximum UAV–NDVIs were highest during the morning (TODs: M6–M9) and late afternoon (TODs: M13–M17) and lowest around the

midday (TODs: M10 to M12). In contrast, the standard deviation values were higher around midday than at other times. the midday (TODs: M10 to M12). In contrast, the standard deviation values were higher around midday than at other times.

*Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 8 of 15

**Figure 7.** Mean, minimum, maximum and standard deviation of the UAV–NDVIs shown for different zones (F1–F5), times of day (TODs; M6–M17) and flight altitudes (FAs; 40, 60, 80 and 100 m). **Figure 7.** Mean, minimum, maximum and standard deviation of the UAV–NDVIs shown for different zones (F1–F5), times of day (TODs; M6–M17) and flight altitudes (FAs; 40, 60, 80 and 100 m).

#### *3.4. Dependence of the UAV–NDVIs on the Solar Zenith Angle (SZA) 3.4. Dependence of the UAV–NDVIs on the Solar Zenith Angle (SZA)*

As can be seen in Figure 8, two general trends can be observed in the SZA values of the study: i) the mean, minimum and maximum UAV–NDVI values generally increased as a function of increasing SZA and ii) the standard deviation values decreased as a function of increasing SZA. The trends were similar across all zones (F1–F5) and FAs. As the lowest SZAs occur around the solar noon this means that the UAV–NDVI values were lower around noon than in the morning or in the afternoon. The reverse was true for the standard deviation values. These results are consistent with those discussed in Section 3.3, which is logical, since the SZAs depend on the TOD. As can be seen in Figure 8, two general trends can be observed in the SZA values of the study: (i) the mean, minimum and maximum UAV–NDVI values generally increased as a function of increasing SZA and (ii) the standard deviation values decreased as a function of increasing SZA. The trends were similar across all zones (F1–F5) and FAs. As the lowest SZAs occur around the solar noon this means that the UAV–NDVI values were lower around noon than in the morning or in the afternoon. The reverse was true for the standard deviation values. These results are consistent with those discussed in Section 3.3, which is logical, since the SZAs depend on the TOD.

#### *3.5. E*ff*ects of the Growth Levels of Rice on the UAV–NDVIs*

The differences between the mean UAV–NDVI values of the five zones followed those acquired using the ground data (F1: lowest, F5: highest, see Figure 4). In contrast, the standard deviation values had a downward trend from zone F1 to zone F5. With some slight variation, these differences could be detected irrespective of the FA, TOD and SZA (see Figures 5, 7 and 8). Thus, better growth levels induced higher NDVI values and lower standard deviation values. Furthermore, as could be seen in Figure 6, the curves of each field (B1–B5) consistently show a right shift (toward higher UAV–NDVI values) and a compressed value distribution with increasing growth level (B1: lowest, B5: highest). Hence, the better the growth level, the denser the UAV–NDVI distribution at the pixel scale. *Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 9 of 15

**Figure 8.** Mean, minimum, maximum and standard deviation of the UAV–NDVIs shown for different zones (F1–F5), solar zenith angles and flight altitudes (40, 60, 80 and 100 m). **Figure 8.** Mean, minimum, maximum and standard deviation of the UAV–NDVIs shown for different zones (F1–F5), solar zenith angles and flight altitudes (40, 60, 80 and 100 m).

*3.5. Effects of the Growth Levels of Rice on the UAV–NDVIs* The differences between the mean UAV–NDVI values of the five zones followed those acquired using the ground data (F1: lowest, F5: highest, see Figure 4). In contrast, the standard deviation values had a downward trend from zone F1 to zone F5. With some slight variation, these differences could To further study the trends associated with the growth levels, the averaged range of the mean UAV–NDVIs are shown individually for the FAs, TODs and SZAs in Table 3. As can be seen, the maximum averaged range of the FAs was 0.031 and that of TODs and SZAs was 0.200 in zone F1. In zone 5, the range was from 0.019 to 0.085. Based on the resulted, shown on Table 3, indicating that the better growth level could reduce the effect of FAs, TODs and SZAs.

be detected irrespective of the FA, TOD and SZA (see Figures 5, 7 and 8). Thus, better growth levels induced higher NDVI values and lower standard deviation values. Furthermore, as could be seen in **Table 3.** Average range of means UAV–NDVIs of F1, F2, F3, F4 and F5 fields.


#### maximum averaged range of the FAs was 0.031 and that of TODs and SZAs was 0.200 in zone F1. In zone 5, the range was from 0.019 to 0.085. Based on the resulted, shown on Table 3, indicating that *3.6. Relative Importance of Di*ff*erent Impactors: FAs, SZAs*/*TODs, Growth Levels*

the better growth level could reduce the effect of FAs, TODs and SZAs. In order to further analyze the contribution of the above factors, the standardized regression coefficients (SRC) of the linear model are shown in Table 4. According to the positive relationship

**Table 3.** Average range of means UAV–NDVIs of F1, F2, F3, F4 and F5 fields.

TODs(h)/SZAs (°) 0.200 0.168 0.122 0.100 0.085

between the contribution and absolute values of the SRC, the descending contribution rank regarding the mean and STDEV UAV–NDVIs can be listed as: SZAs/TODs, growth levels and FAs. It can be found that the FAs affect the values less than the other parameters and the SZAs/TODs had a significant effect on the mean UAV–NDVIs and STDEV UAV–NDVIs.


**Table 4.** Standardized regression coefficients (SRC) of FAs, SZAs/TODs and growth levels.

Note: \*\* represents the *p* ≤ 0.01.

#### **4. Discussion**

Remote sensing has been widely used to assess crop growth in different environments. UAV-based imaging technology has the potential to provide high spatial resolution (up to centimeter-scale) maps for this end, providing instant feedback needed for crop management and decision making. In this context, increasing the understanding of how the flight parameters affect the NDVI values acquired using UAV-systems can help improve the image quality of the thus acquired data. In this study, we used a lightweight UAV equipped with a multispectral camera to collect NDVI values over a paddy rice under different flight parameters as well as different crop growth levels. The results suggest that the flight parameters and growth levels have a significant effect on the UAV-based data, thus highlighting the importance of careful flight planning. Nevertheless, it is essential to mention that the parameters and environmental factors (FA, TOD, SZA and growth level) discussed in this study do not fully account for the quality of the UAV-acquired data: Factors such as the flight speed can have a significant impact on the quality of the remote sensing data.

#### *4.1. Sensitivity of UAV–NDVIs to the Flight Altitude (FA)*

Our resulted, discussed in Section 3.2, suggest that the mean, minimum, maximum and standard deviation values of the UAV–NDVIs were highly related to the FA. This was particularly true for the COP of the peak UAV–NDVI values, shown in Figure 6. Additionally, as can be seen in the Appendix A Table A1 and Figure A1, under the same growth level, the higher the FA, the smaller the individual UAV–NDVIs and the narrower the range of the UAV–NDVIs. Regarding the effects of the FA on the NDVI values, previous studies have reported different results. Stow et al. [32] evaluated the effects of the FA on the NDVI values through field experiments, finding that the relationship between them was inconclusive. In studies by Rasmussen et al. [33] and Yu et al. [34], no significant associations were found between the FA and the NDVI values. Easterday et al. [35] concluded that of the NDVI values acquired under different FAs (30 m, 60 m, 100 m and 120 m), the lowest FAs were better for detecting water deficit in plants. Similar to our study, a report by Mesas-Carrascosa et al. [36] concluded that even though the mean and maximum UAV–NDVIs were negatively related to the FA, the reverse was true for the minimum and STDEV UAV–NDVIs.

Notwithstanding the relationship between the NDVIs and the FA, the selection of the FAs has important implications from the point of view of the spatial resolution of the remote sensing imagery. In the field of imaging technology, a higher GSD means that each pixel represents a larger area. However, with the resulting lower spatial resolution of the image, the spectral information included in each pixel becomes more mixed and hence, detecting objects and phenomena can become more difficult. At high FAs associated with low spatial resolutions, some pixels could be mixed, and the average NDVIs may become diluted [37].

Looking at the relationship between the FA and the number of pixels of this study, Figure A1 shows four subsets of the UAV–NDVI maps of the B2 field at different FAs at TOD M11. In this analysis, four subsets were selected to represent the FAs of 40, 60, 80 and 100 m. These FAs resulted in GSDs

values of 2.88, 4.32, 5.81 and 7.22 cm×pixel−<sup>1</sup> that had NDVI values between 0.5 and 0.86. As can be seen in Figure A1, increasing the GSD resulted in fewer pixels in the same sampled area and narrower COP distributions. Under normal circumstances, the higher the FA of the UAV platform, the larger the area represented by a single pixel, and thus, the lesser the ability of the UAV system to detect small features. In the case of a paddy rice field, despite their commonly high canopy densities, there are still many canopy gaps. These gaps have other elements such as soil and water, which, depending on the FA, can lead to different degrees of spectral mixing. This, in turn, can lead to overall smaller mean NDVI and standard deviation values as suggested by Figure 5a,b, respectively. Therefore, as far as the NDVI monitoring of rice is concerned, the average NDVI was inversely proportional to the FAs. This further supports the conclusion that the FA was an important factor affecting the image quality for the crops. In general, it can be concluded that FAs do impact the mean UAV–NDVIs, but it is not conclusive that the mean UAV–NDVIs will keep decreasing with increasing FA. A possible extreme flight altitude needs further exploration.

Another noticeable factor regarding the effect of FA on vegetation indexes was atmospheric effects. Yu et al. suggested normalized excess green index (ExG) and the normalized green–red difference index (NGRDI) were relatively susceptible to FAs than the NDVI values (FAs: 10 m, 30 m, 50 m and 100 m) considering of the atmospheric effects [34]. The atmospheric attenuation was small in the low-altitude atmospheres and the effect of path radiance in their model was negligible for red and infrared wavelengths. Thus, we did not make an analysis of the atmospheric effects upon NDVIs when the FAs were under 100 m. In fact, the atmospheric effects were more significant at higher FAs (manned aircrafts and satellites) [38].

#### *4.2. Influence of the time of Day (TOD) and Solar Zenith Angle (SZA) on the UAV–NDVIs*

Although the SZA values vary as a function of the time of day, date and location [39], in this study, SZAs and TODs were discussed independently, because there is the spatiotemporal difference. To the knowledge of the authors, our study is one of the first ones to report and discuss the effects of the TODs and SZAs on UAV-based NDVI values continuously from sunrise to sunset. Based on our results, the mean, minimum, maximum and standard deviation of the UAV–NDVIs are all highly related to the TODs and SZAs (see Sections 3.3 and 3.4). In previous studies, Ishihara et al. [28] suggests that the response of vegetation indices to the SZA was evident under a clear sky. Rahman [29] reported NDVIs from ground-based observations that decreased with decreasing SZA at a pasture site, but did not provide explanations to this finding. Their conclusions were directly based on the average assessment of ground-NDVIs, and hence, not remote sensing data. Although the results in this study show a similar phenomenon, we have further analyzed the UAV–NDVI distribution on a pixel-scale (including mean, minimum, maximum, standard deviation and COP of the UAV–NDVIs), an experimental setup that can reveal the relationships between the UAV–NDVIs and the flight parameters (SZAs/TODs) more clearly. Ishihara et al. suggested that surveys involving NDVI measurements be performed at a SZA of 60◦ to be effective for the accurate assessment of the canopy structure and function. However, our results, shown in Figure 8, suggest no obvious differences between the mean UAV–NDVIs and standard deviation values of the UAV–NDVIs with different growth levels at a SZA of 60◦ . Therefore, we do not follow the recommendation by Ishihara et al. to acquire UAV–NDVI values at a SZA of 60◦ . In practice, the TODs and SZAs were the direct reasons for changing the angle between the view direction and incident light. An important phenomenon in this context is the widely discussed hot spot effect, also called the hot spot directional signature in the backscattering direction [40]. In recent studies, this phenomenon was also called the bidirectional reflectance distribution function (BRDF) effects. Generally, the radiance of the reflecting medium will decrease with an increasing angle between the view direction and incident light because of the decreased probability of seeing illuminated particles [41]. Therefore, because of the vertical downward view of the RedEdge camera, the strongest radiance and relatively complete reflectivity from the rice canopy was collected when the SZAs were relatively small. By definition, crop reflectance is the ratio of the amount of light leaving the canopy to

the amount of incoming light. We suggest the reflectivity was relatively accurate at a smaller SZAs, in turn, resulting in reliable NDVI values.
