*3.2. Performance of Existing Vegetation Indices*

The relationships between CCC, MTA and the tested vegetation indices derived from four broadband satellites are presented in Table 3, including both the field-measured dataset and model simulations. In general, model-simulated dataset-derived VIs had stronger correlations with CCC than those of the field-measured dataset.


**Table 3.** Coefficient of determination (*R*2) between canopy chlorophyll content (CCC), leaf mean tilt angle (MTA) and tested vegetation indices.

The transverse line ("—") denotes the sensor without band to calculate corresponding vegetation index.

In field measurements, for the tested VIs calculated using Sentinel-2 bands, the CIred edge had the strongest correlation with CCC (*R*<sup>2</sup> CCC = 0.68) and the smallest influence from MTA (*R*<sup>2</sup> MTA = 0.05). In model simulations, the CIred edge had the strongest correlation with CCC (*R*<sup>2</sup> CCC = 0.90) and a weak correlation with MTA (*R*<sup>2</sup> MTA = 0.00). For the other three satellite sensors, in the field-measured dataset analysis, PSND produced the strongest correlations with CCC (*R*<sup>2</sup> CCC = 0.49–0.52) and the weakest correlation with MTA (*R*<sup>2</sup> MTA = 0.17–0.19). Model-simulated PSND presented a medium-strong correlation with CCC (*R*<sup>2</sup> CCC = 0.57–0.67) and a weak correlation with MTA (*R*<sup>2</sup> MTA = 0.00–0.01). In model simulations, TCARI/OSAVI had the strongest correlation with CCC (*R*<sup>2</sup> CCC = 0.87–0.88) and the weakest correlation with MTA (*R*<sup>2</sup> MTA = 0.01). This index had medium-strong correlations with both CCC (*R*<sup>2</sup> CCC = 0.29–0.33) and MTA (*R*<sup>2</sup> MTA = 0.37–0.41). MTA had the largest effect on EVI in both the field-measured dataset (*R*<sup>2</sup> MTA = 0.61–0.64) and model simulations (*R*<sup>2</sup> MTA = 0.31–0.36).

#### *3.3. Identification of New Indices*

In addition to the twelve tested vegetation indices, the potential of six two-band and five three-band new vegetation indices of predefined type were investigated for CCC estimation using the four satellite bands. In Figures A2 and A3, for the six two-band types of indices, the matrices of determinations of coefficients between CCC (*R*<sup>2</sup> CCC), MTA (*R*<sup>2</sup> MTA) and vegetation indices using all possible combinations of field-measured datasets based on RI, NDVI, DI, SAI, MSR, MSAI formulations are presented. The corresponding difference matrices between *R*<sup>2</sup> CCC and *R*<sup>2</sup> MTA based on the six formulations are presented in Figure 4. The three best band sets for the three-band indices identified using simulated satellite bands in the field-measured dataset are presented in Table 4. These identified best bands for the two-band and three-band indices and the corresponding *R*<sup>2</sup> CCC and *R*<sup>2</sup> MTA using the field-measured data are presented in Tables 4 and 5, respectively. The identified best indices were validated with PROSAIL model simulations, and the results are presented in Table 6.


**Figure 4.** Matrices of difference between *R*<sup>2</sup> CCC and *R*<sup>2</sup> MTA in all possible two band combinations for RI, NDI, DI, SAI, MSR and MSAI formulations. The color indicates different *R*<sup>2</sup> values, blank negative values.


**Table 5.** Best band configurations for the two-band indices in the field measured dataset for each simulated satellite.

**Table 6.** Performance of the best new indices of each type for the four simulated satellite sensors in model simulations.


In the Sentinel-2 bands, all the best new indices presented strong correlations with CCC (*R*<sup>2</sup> CCC = 0.74–0.80) and no correlation with MTA (*R*<sup>2</sup> MTA = 0.00–0.02). SAI (B6, B7), was identified as the best (*R*<sup>2</sup> CCC = 0.80 and *R*<sup>2</sup> MTA = 0.00) among all the new indices in the field-measured dataset (Figure 5). This combination was found to have a strong correlation with CCC (*R*<sup>2</sup> CCC = 0.95) and a weak correlation with MTA (*R*<sup>2</sup> MTA = 0.00) in the modelsimulated dataset (Figure 6), as shown in Table 6. In the simulated WorldView-2 data, the *R*2 CCC varied between 0.44 and 0.78 and *R*<sup>2</sup> MTA varied between 0.00 and 0.11. The identified new three-band of indices performed better (*R*<sup>2</sup> CCC = 0.58–0.78 and *R*<sup>2</sup> MTA = 0.0–0.10) than the two-band indices (*R*<sup>2</sup> CCC = 0.44–0.74 and *R*<sup>2</sup> MTA = 0.02–0.11). BSI-V (NIR1, Red, Red Edge) was identified as the best new index (*R*<sup>2</sup> CCC = 0.78 and *R*<sup>2</sup> MTA = 0.00). In the modelsimulated dataset, this combination was found to have a strong correlation with CCC (*R*<sup>2</sup> CCC = 0.90) and no correlation with MTA (*R*<sup>2</sup> MTA = 0.01). In the simulated RapidEye data, large variations on correlation were identified among the best new indices for CCC (*R*<sup>2</sup> CCC = 0.22–0.76) and MTA (*R*<sup>2</sup> MTA = 0.00–0.32). BSI-T (red edge, green, NIR) was the best-performing index (*R*<sup>2</sup> CCC = 0.76 and *R*<sup>2</sup> MTA = 0.00) and was found to have a strong correlation with CCC (*R*<sup>2</sup> CCC = 0.84) and no correlation with MTA (*R*<sup>2</sup> MTA = 0.00) in the model-simulated dataset. In the simulated GaoFen-6 data, the best new indices presented large variations in correlations with CCC (*R*<sup>2</sup> CCC = 0.14–0.78) and MTA (*R*<sup>2</sup> MTA = 0.00–0.23). DI (B6, B4) was identified as the best index (*R*<sup>2</sup> CCC = 0.78 and *R*<sup>2</sup> MTA = 0.00) and was found to have a strong correlation with CCC (*R*<sup>2</sup> CCC = 0.94) and almost no correlation with MTA (*R*<sup>2</sup> MTA = 0.04) in the model-simulated dataset.

**Figure 5.** Correlation between the best vegetation indices, and CCC (**top row**) and MTA (**bottom row**) in Sentienl−2 (**left column**), WorldView−2 (**second column**), RapidEye (**third column**) and GaoFen-6 (**right column**) in the field measured dataset.
