2.4.3. ROI Selection

For the comparison of different sensors, the most common method is to compare the spectrum information or VI of each corresponding band by statistical regression [49]. In this experiment, four commonly used VIs were compared between Sequoia and P4M. In the spectrum information comparison, the reflectance of Sequoia cannot be directly compared with the DN value of P4M, so we used linear regression to characterize the spectral difference of these two sensors. Additionally, eight ground points were also measured by ASD, but the number of points was too limited to establish a fitting relationship between the ASD and the sensor. Therefore, we compared the NDVI between ASD

and sensor point by point. For this experiment, we selected some ROIs in the same location (between images of two sensors) and then compared the average value in each ROI [50]. The images of the study area were divided into 10 × 8 grids on average, and a ROI was selected from each grid. A total of 80 homogeneous ROIs (including vegetation and nonvegetation) were selected in the experiment. The selected ROIs were in flat terrain, properly sized, homogeneous, and almost identical (no other object features were included) [51]. The relation function between Sequoia and P4M was fitted using ordinary least square (OLS) regression. The goodness of fit was defined by the correlation coefficient (also written as R2) [52]. Root-mean-square error (RMSE) was used to measure the deviation degree between the Sequoia-NDVI or P4M-NDVI and ASD-NDVI, as shown in Equation (2).

$$\text{RMSE} = \sqrt{\frac{\sum\_{i}^{n} (y\_i - y\_i')^2}{n}} \tag{2}$$

where *yi* is the ASD-NDVI, *yi* is the average Sequoia-NDVI or P4M-NDVI, and *n* is the total number of ground points (*n* = 8).

#### 2.4.4. VI Selection

VIs can reflect the growth status of vegetation. Different VIs may have certain differences in reflecting vegetation characteristics [53]. In vegetation studies, among all the possible existent VIs, NDVI, green normalized difference vegetation index (GNDVI), optimal soil-adjusted vegetation index (OSAVI), and leaf chlorophyll index (LCI) are commonly used. These four VIs were compared in this experiment, as shown in Table 3. NDVI is currently the most widely used VI in the world. In agriculture, NDVI is one of the most important tools for crop yield estimation, biomass estimation, and so on [54]. Using the unique response characteristics of vegetation in the near-infrared band, NDVI combines the spectral values of the red band and near-infrared band to quantitatively describe the vegetation coverage in the study area.

**Table 3.** Four vegetation indices (VIs) used for the research.


NIR, R, G, RE: Reflectance of near-infrared, red, green, and red edge bands.

Compared with NDVI, GNDVI is more sensitive to the change in vegetation chlorophyll content [55]. It combines the spectral values of the green band and the near-infrared band. OSAVI can reduce the interference of soil and vegetation canopy [15]. It also combines the spectral values of the red band and the near-infrared band. LCI is a sensitive indicator of chlorophyll content in leaves and is less affected by scattering from the leaf surface and internal structure variation [56]. It combines the spectral values of the red band, red edge band, and the near-infrared band. Different VIs were selected so that their VI equations contained different bands (Figure 3).

#### **3. Results**

#### *3.1. Consistency of Spectral Values*

In order to get better experiment results, we compared the images with 5 and 10 cm spatial resolution using the scatter plots of the Sequoia and P4M spectral values for the approximately equivalent spectral bands (green, red, red edge, and near-infrared). In the experiment, Sequoia used the spectral reflectance and P4M used the DN value of the image.

In the first experiment (5 cm spatial resolution), the spectral values of Sequoia and P4M were highly correlated (Figure 7). The two sensors showed a high correlation in the approximately equivalent four bands, and the correlation coefficient of the fitting function was not less than 0.90. The two sensors had the highest correlation in the red band (R2 = 0.9709), followed by the green band (R<sup>2</sup> = 0.9699) and the red edge band (R<sup>2</sup> = 0.9208); the correlation for the near-infrared band was lower than those of the other three bands (R<sup>2</sup> = 0.9042). It was seen that the spectral values of Sequoia and P4M had an excellent correlation in the green and red bands, and the R<sup>2</sup> was greater than 0.96. Meanwhile, the correlation was low in the red edge and the near-infrared bands, and the R2 was slightly less than 0.92. Thus, these results showed that spectral values of these two sensors had a high correlation in the green and red bands and a low correlation in the red edge and near-infrared bands.

**Figure 7.** Scatter plots of Sequoia and P4M spectral values (5 cm) in the green band (**a**), red band (**b**), red edge band (**c**) and near infrared band (**d**). The solid lines show OLS regression of the Sequoia and the P4M data, and the dotted lines are 1:1 lines for reference.

In the second experiment (10 cm spatial resolution), the spectral values of Sequoia and P4M were also well correlated (Figure 8). In the four bands of these sensors, the correlation coefficient of the fitting equation was not less than 0.91, showing a strong correlation. As seen in the 5 cm spatial resolution results, the two sensors had the highest correlation in the red band (R2 = 0.9793), followed by the green band (R<sup>2</sup> = 0.9727) and the red edge band (R<sup>2</sup> = 0.9436); the correlation for the near-infrared band was lower than those of the other three bands (R2 = 0.9199). The spectral values of Sequoia and P4M were highly correlated in the green and red bands, and the R2 was greater than 0.97. Similarly, the correlations between the two sensors in the red edge and the near-infrared bands were low, and the R<sup>2</sup> was slightly less than 0.94. These results also showed that spectral values of these two sensors had a high correlation in the green and red bands and a weak correlation in the red edge and near-infrared bands.

**Figure 8.** Scatter plots of Sequoia and P4M spectral values (10 cm) in the green band (**a**), red band (**b**), red edge band (**c**) and near infrared band (**d**).

In short, the spectral values of Sequoia and P4M were highly correlated in both the green and red bands (R<sup>2</sup> > 0.96), but the correlation was slightly lower in the red edge and the near-infrared bands (R2 < 0.96). The correlation of spectral values for the two sensors at 10 cm spatial resolution was slightly higher than that of 5 cm. Thus, if we are interested in using both images at the same time, the 10 cm spatial resolution image may be the better choice. Although these two sensors were highly correlated, there was also a slight difference, which may be caused by a variety of mixing factors, including the difference in spectral response function (Figure 3). Among the compared bands, the center wavelength and the wave width of these two sensors were both close in the green and red bands. Although the center wavelength of these two sensors was close in the red edge band, there was a big difference in the wave width. In the near-infrared band, the center wavelength and the wave width of the two sensors were significantly different. This explains how the spectral values differ between Sequoia and P4M.

#### *3.2. Consistency of VI Products*

The VI products of Sequoia and P4M were highly correlated (Figure 9). Four VIs were compared in this paper, namely NDVI, GNDVI, OSAVI, and LCI, as shown in Figure 9. The results on the left were obtained with 5 cm spatial resolution image, and those on the right were obtained with 10 cm spatial resolution. The black dotted lines are the 1:1 reference lines, and the solid lines are the fitting functions of these sensor-derived VIs (using OLS regression).

Among the four VIs, NDVI had the highest correlation, followed by OSAVI, GNDVI, and LCI. In the comparison of 5 cm spatial resolution images, the correlation of NDVI was the highest (R2 = 0.9863), followed by OSAVI (R2 = 0.9859), while GNDVI and LCI were lower (GNDVI: R2 = 0.9595; LCI: R<sup>2</sup> = 0.9516). In the comparison of 10 cm spatial resolution images, the correlation of NDVI was still the highest (R<sup>2</sup> = 0.9842), followed by OSAVI (R2 = 0.9806), while GNDVI and LCI were lower (GNDVI: R<sup>2</sup> = 0.9518; LCI: R<sup>2</sup> = 0.9546).

**Figure 9.** Scatter plots of Sequoia and P4M vegetation indices, which corresponded to the NDVI, GNDVI, OSAVI and LCI with 5cm spatial resolution (**a**, **c**, **e** and **g**) and 10cm (**b**, **d**, **f** and **h**).

Sequoia-NDVI had better result than P4M-NDVI (most of the scattered points were distributed above the 1:1 line, and a very small part of the scattered points were located on or below the 1:1 line). The legend in Figure 5 also shows that Sequoia-NDVI was slightly higher than P4M-NDVI; the fitting results of GNDVI and LCI were similar to NDVI, and there were also some differences. In both resolutions, Sequoia-GNDVI was higher than P4M-GNDVI (all scattered points were distributed above the 1:1 line), but the distributions of points were more dispersed than those of NDVI. The fitting result of OSAVI was different from those of the previous three indices. In both resolutions, Sequoia-OSAVI was only partially higher than P4M-OSAVI (the scattered points were evenly distributed above, below, and on the 1:1 line).

Table 4 shows Sequoia and P4M VI transformation functions derived by OLS regression of the data shown in Figure 9. The transformation functions for the 5 and 10 cm spatial resolution images are listed separately, where S represents the VI of Sequoia and P represents the VI of P4M. Both sensors had good consistency in those four indices.

**Table 4.** Sequoia and P4M VI transformation functions derived by OLS regression of the data illustrated in Figure 9.


NDVI, GNDVI, OSAVI, and LCI were used for different combinations of surface reflectivity, so their values were partly determined by the reflectance of the green, red, red edge, and near-infrared bands. There was a certain difference in the spectral response functions of the sensors, which led to slight differences between the VI products. Although users cannot directly obtain the reflectance from P4M image, they can still obtain high-quality VI products. It was seen that P4M, which integrates aircraft, cameras, and data processing software, optimizes the user's experience and improves the working efficiency by providing good VI products.
