*4.3. E*ff*ect of the Growth Level on the UAV–NDVIs*

It can also be seen in the data that the application rate of fertilizers can have a significant impact on the NDVI values (Figure 4). In the early stages of rice growth or in the case of weak growth that results from inadequate fertilization, the canopy is distributed sparsely and there are more gaps with water between seedlings [42]. These potential gaps would influence the NDVI values. For instance, looking at the image acquired at M11 (11 a.m.–12 p.m.) at 40 m (Figure A1), the image has a relatively high GSD of 2.88 cm at a FA of 40 m. Due to the resulting high spatial resolution, the image contains many details, including those related to non-foliar areas, and thus, small NDVI values that result from pixels with soil or water. Similarly, areas with lesser amounts of fertilizers and thus, less vigorous growth levels will have more gaps with smaller NDVI values and higher standard deviation values than result from spatial heterogeneity (vegetation, water and soil). The higher the flight altitude, the lower the spatial resolution and the lesser the amount of details in the image. Therefore, in the UAV image acquired at 100 m, the standard deviation is lower and the range of NDVI values is narrower than in the image that was acquired at 40 m (Figures 7 and A1). On the other hand, the main limitation in our growth level analysis was the lack of observations to cover entire growth stages. Such an analysis would have provided a more comprehensive quantitative framework to interpret the interrelationships between the growth levels and the flight parameters. In fact, in the early stages of rice growth, due to the sparse rice canopy, aerial remote sensing will collect a large area of ground water surface (mirror reflectance), which is a growth period that is not suitable for UAV remote sensing monitoring. Hence, the available growth stages for reliable UAV remote sensing are limited.

#### **5. Conclusions**

In this study, a paddy rice field was used as a test area to assess the effects of FA, TOD, SZA and growth level on the UAV-acquired NDVI values. Based on the SRC values of the linear regression resulted, all of the former parameters had a significant effect on the UAV–NDVIs. More specifically, our results suggest that (1) the SZA/TOD had the largest impact on the mean, minimum, maximum and STDEV of UAV–NDVI values, followed by the growth level and the FA; (2) the mean, maximum and STDEV of UAV–NDVIs were inversely proportional to the FA (≤100 m) and the minimum of UAV–NDVIs decreased with increasing FA; (3) the mean, minimum and maximum of UAV–NDVIs were proportional to the SZA, but it was contrary with the STDEV of UAV–NDVIs; (4) the effect of TOD on UAV–NDVIs could refer to the SZA;(5) Furthermore, according to our results, the UAV–NDVIs close to the smaller SZAs show the highest signal-to-noise ratios which we infer to reflect the most realistic growth status values. We expect that our resulted and the recommendations could provide a reference to the operating parameters of UAVs in the context of precision agriculture.

**Author Contributions:** R.J. was responsible for the framework design of the entire system in this research and arranged tests, conducted several verification tests and wrote the article. P.W. and Y.X. assisted in collected all the test data and checked the study carefully. Z.Z. proposed the main plans, ideas and guidance for the work and reviewed the study, as well as acquired the funding. X.L., Y.L., G.Z., A.S.-A. and K.L. provided guidance and advice. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China (31871520), the National Key R&D Program of China (2018YFD0200301), Science and Technology Plan of Guangzhou of China (201807010111), Science and Technology Plan of Guangdong Province of China (2017B090903007) and Innovative Research Team of Agricultural and Rural Big Data in Guangdong Province of China (2019KJ138).

**Acknowledgments:** We would like to thank the flying permission of the administration of Ningxi Teaching and Research Bases, at the South China Agricultural University, Guangzhou, China. Furthermore, the authors would like to thank the reviewer for their helpful comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **Appendix A Appendix A**

B5

*Remote Sens.* **2020**, *12*, x FOR PEER REVIEW 13 of 15

like to thank the reviewer for their helpful comments. We thank AJE (https://secure.aje.com/) for its linguistic

**Figure A1.** Subset of the B2 NDVI map at 40, 60, 80, 100 m with TOD of M11 (11 a.m.–12 p.m.).


**Figure A1.** Subset of the B2 NDVI map at 40, 60, 80, 100 m with TOD of M11 (11 am–12 pm). **Table A1.** Minimum, maximum and range of UAV–NDVIs of the B1–B5 fields.

80 0.77 0.93 0.16 100 0.78 0.92 0.14
