*3.3. Accuracy of NDVI*

Both the Sequoia-NDVI and P4M-NDVI had high accuracy, not only with a small deviation from ASD-NDVI but also with a good correlation (Figure 10). Two sets of spatial resolution data (5 and 10 cm) are compared in Figure 10: the left part shows the fitting scattered points of Sequoia-NDVI and ASD-NDVI, while the right part shows the fitting scattered points of P4M-NDVI and ASD-NDVI (blue dots correspond to 5 cm resolution and orange triangles correspond to 10 cm resolution). The Sequoia-NDVI was highly consistent with ASD-NDVI, and the correlation was high. In the comparative study of 5 cm spatial resolution images, RMSE = 0.0622 and R<sup>2</sup> = 0.8523; in 10 cm spatial resolution images, RMSE = 0.0684 ad R<sup>2</sup> = 0.8497. Similar to Sequoia, P4M-NDVI was also highly consistent with ASD-NDVI, maintaining a good correlation. In the comparative study, RMSE = 0.0886 and R<sup>2</sup> = 0.8785 for 5 cm spatial resolution images, while RMSE = 0.0842 and R<sup>2</sup> = 0.8785 for 10 cm spatial resolution images. This indicates that both Sequoia and P4M can provide NDVI products with high accuracy. Furthermore there was no big difference between the VI products obtained from these sensors.

**Figure 10.** Scatter plots of Sequoia-NDVI (**a**) and P4M-NDVI (**b**) with ASD-NDVI. The blue dotted lines show OLS regression of Sequoia-NDVI (P4M-NDVI) and ASD-NDVI data with 5 cm resolution. The orange dotted lines show OLS regression of Sequoia-NDVI (P4M-NDVI) and ASD-NDVI data with 10 cm resolution.

#### **4. Discussion**

#### *4.1. Di*ff*erences between Sequoia and P4M*

Different sensors may have different spectral response functions [59], and such differences will cause systematic deviations in the spectral values of the images. The consistency of spectral values between the two sensors studied showed a clear difference in the near-infrared band (Figures 7 and 8). The reason for this might be due to their different spectral response functions (Figure 3). Compared with the spectral response function of Sequoia, the spectral range of P4M in the near-infrared band was wider than that of Sequoia (Sequoia: 40 nm; P4M: 52 nm), and the positions of center wavelength in the near-infrared band were also different (Sequoia: 790 nm; P4M: 840 nm). The results also showed that there was some difference in the red edge band. Although the center wavelengths of these two sensors were close in the red edge band (Sequoia: 735 nm; P4M: 730 nm), there were big differences in the wave width (Sequoia: 10 nm; P4M: 32 nm). In contrast, the center wavelength and the wave width of these two sensors were both close in the green (Sequoia: 550 nm and 40 nm; P4M: 560 nm and 32 nm) and red bands (Sequoia: 660 nm and 40 nm; P4M: 650 nm and 32 nm). The different spectral response functions may explain the difference in the spectral values between Sequoia and P4M.

In addition, other factors such as the spectral reflection characteristics of the ground object, the nonuniformity of the ground surface, the observation time, and the solar elevation angle also increased the randomness and uncertainty of this systematic deviation [60–62]. In this experiment, the ground surface of the study area was uniform, and the observation time was similar for both sensors, as was the solar elevation angle. Therefore, the reason for the difference in spectral values was probably related to the reflection characteristics of ground target features. The spectral value of a single pixel may be influenced by both the spectral response function and the reflection characteristics of the target feature. Therefore, some objects in the image having high reflection characteristics in a specific spectral band may often be more affected by the difference in spectral response function.

The acquisition of the VI usually requires a series of conversion processes on the spectral values; thus, if there is a deviation in the spectral values, the VI may also be affected. In analysis of the consistency of the VIs between the two sensors, the four VI products of Sequoia and P4M were found to be highly correlated, but there were still some differences. These differences may have a great relationship with the differences in spectral values between the sensors. The reason for this difference in spectral values is also the same as explained above (due to spectral response function). Therefore, this difference between VIs may be caused by the spectral response function and the reflection characteristics of the target features. As we know, the VI is obtained by combining the spectral values of different bands, so using different combination methods of spectral values may also affect the quality of the VI.

#### *4.2. Sensitivity of VIs to Spectral Deviation*

The calculation of the VI involved spectral values of different spectral bands. NDVI, GNDVI, OSAVI, and LCI were compared in this study, and their calculation included the spectral values of red, green, red edge, and near-infrared bands. Therefore, small changes in the spectral values of each band may have a relatively big impact on the VI results. In addition, the band combination method may also change the sensitivity of the VI to small changes in the spectral values. The experimental results showed that although the spectral values of the Sequoia and P4M were significantly different in the near-infrared band, this difference did not show a significant impact on the VI products. The correlation coefficients of the VI products obtained by these two sensors were greater than 0.95. The NDVI products of the two sensors were also compared with the ASD-NDVI. The results showed that Sequoia-NDVI and P4M-NDVI both have high accuracy. The normalized calculation method of VI eliminated the influence of the difference in spectral values to a certain extent, thus reducing the sensitivity of the VI to such spectral deviations [63].

Poncet et al. [64] found that the error of VIs was correlated with different radiometric calibration methods. In this experiment, for Sequoia, we used a calibration target provided by the manufacturer to perform radiometric calibration in postprocessing. The calibration method might have affected the reflectance, which would have affected the VI.

#### *4.3. Selection of Optimal Spatial Scale*

The pixel is the smallest unit that constitutes the remote sensing digital image. It is an important symbol to reflect the features of the image and can be used to characterize the ground conditions in the study area. The pixel size determines the spatial resolution of a digital image and amount of information it can contain. After resampling from high spatial resolution to low resolution, the resultant image (low spatial resolution) will lose spectral information and spectral variation [65]. With the increase of remote sensing image scale, the spectral features of several different ground objects may appear simultaneously in a single pixel, resulting in the generation of a mixed pixel. At this time, the signal intensity of the ground object features in the pixel tends to be stable, and the pixel signals received by different sensors will tend to be similar.

The correlation between the spectral values of Sequoia and P4M in each band (green, red, red edge, and near-infrared) seemed to have a certain relationship with the image scale. When the image scale was small (5 cm), the correlation was low; when the image scale was large (10 cm), the correlation was high (Table 5). Fawcett et al. [66] found that the NDVI consistency of a multispectral sensor was similar at different spatial resolutions. Our results also show that the NDVI consistencies of Sequoia and P4M at 5 and 10 cm resolutions are similar (5 cm: R2 = 0.9863; 10 cm: R<sup>2</sup> = 0.9863).


**Table 5.** Sequoia and P4M spectral value transformation functions and the values of their correlation coefficients (R2).

S: Sequoia, P: P4M.

#### *4.4. Limitations*

The study was carried out on a single date in a single study area with uniform vegetation species; it would be better if different study areas with different vegetation species in different periods were used. When assessing the suitability of UAV sensors in determining VIs, it would be important to include agricultural land, preferably with different nutrient treatments or crop species. Thus, in the future, it would be better to include agricultural land with crop species while doing VI-related research. Similarly, to assess the accuracy of NDVI, eight ASD points were used, as the terrain was uniform; still, better results could be obtained if more points were used. The use of more than two multispectral minisensors could be more meaningful to analyze the consistency of spectral values, consistency of VI products, and accuracy of NDVI. Therefore, detailed research is needed in the future to obtain improved results and conclusions.

#### **5. Conclusions**

Different UAV multispectral minisensors have been developed for applications in various fields, but their experimental performance and consistency need to be determined before their application. As a preliminary work towards consistency evaluation, different UAV images from Sequoia and P4M sensors with multispectral bands were acquired and preprocessed for ROI creation and VI calculation. The main objective of this research was to experimentally evaluate different UAV multispectral

minisensors and compare them in terms of consistency. Using a combined method of consistency of spectral values, consistency of VI products, and accuracy of NDVI, we came to the following conclusions: First, the data acquisition capability of the Sequoia is similar to that of the P4M; both the spectral values and VIs of the two sensors have good correlation (R2 > 0.90). Second, the VI products obtained from both sensors have good precision, and they are suitable for vegetation remote sensing monitoring. Third, both sensors have similar characteristics, and they may be used interchangeably for large area coverage with high spatial resolution and for daily time series science and applications.

**Author Contributions:** H.L., T.F., and L.D. designed and developed the research idea. H.Z. conducted the field data collection. H.L. and T.F. processed all remaining data. H.L. performed the data analysis and wrote the manuscript. H.L., T.F., P.G., and L.D. contributed to results and data interpretation, discussion, and revision of the manuscript. All the authors revised and approved the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research & Development Program of China (No. 2018YFC0706004).

**Acknowledgments:** The authors are very thankful for the valuable support of Zou Han Yue, Qiao Dan Yu, and Chen Yong.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
