# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 3 Results
### CCC Estimation Using the Simulated Dataset  
The univariate linear regressions between CCC and the models are shown in Figure 6. The R\({}^{2}\) was selected to assess the ability of each model to prevent an ill-posed problem. By comparing Figure 6b-f with Figure 6a, it can be observed that the scatter plots for MTCI \(\times\) LAI-Vis are more compact than for the MTCI, and the MTCI \(\times\) LAI-VIs plots also feature more linear trends. MTCI \(\times\) MTVI2 showed the best performance, with an R\({}^{2}\) value (0.90) higher than that for the MTCI (0.69).  
Figure 4: Sensitivity of spectra to LAI.  
Figure 5: Sensitivity of MTCI to CCC. (**a**) ALA is spherical and the soil background is soil3. (**b**) LCC = 40 \(\upmu\)g cm\({}^{-2}\), ALA is spherical. (**c**) LCC = 40 \(\upmu\)g cm\({}^{-2}\), soil background is soil3.  
The binary linear regressions were employed in Matlab and the results were compared in Table 6. The R\({}^{2}\) values were used to evaluate the predictive ability of each model and demonstrated a strong correlation between MTCI and LAI-VIs, and CCC. The binary regression models based on the MTCI and LAI-VIs were more representative than the univariate regression model using the MTCI alone. MTCI and RDVI (R\({}^{2}\) = 0.82), and MTCI and MTVI2 (R\({}^{2}\) = 0.80) had similar results, with excellent performance. However, the overall results for the binary regression models (Table 6) indicate reduced performance compared to the univariate regression models (Figure 6).  
The random forest approach was also tested, using the MTCI and LAI-VIs as inputs, for retrieving the CCC. The R\({}^{2}\) was selected to assess the predictive ability of each model, as shown in Table 7, where it is apparent that the CCC exhibits strong correlations with the MTCI and LAI-VIs with the RFR approach. The R\({}^{2}\) values are higher than for the two types of linear regression.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Regression Equation** & **R\({}^{2}\)** \\ \hline MTCI & y = 67.7x \(-\) 60.32 & 0.69 \\ MTCI and NDVI & y = 58.1088x\({}_{1}\) + 201.1010x\({}_{2}\) - 191.7325 & 0.74 \\ MTCI and LNDVI & y = 53.5792x\({}_{1}\) + 171.5196x\({}_{2}\) - 145.0627 & 0.76 \\ MTCI and SNDIVI & y = 57.9619x\({}_{1}\) + 98.1736x\({}_{2}\) - 151.1732 & 0.74 \\ MTCI and RDVI & y = 55.8884x\({}_{1}\) + 209.7858x\({}_{2}\) - 145.4328 & 0.82 \\ MTCI and MTVI2 & y = 57.8158 x\({}_{1}\) + 381.2731x\({}_{2}\) - 238.9448 & 0.80 \\ \hline \hline \end{tabular} \(\overline{x_{1}}\) = MTCI; \(x_{2}\) = LAI-VIs.  
\end{table}
Table 6: Binary linear regression retrieval model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictor** & **R\({}^{2}\)** & **Predictor** & **R\({}^{2}\)** \\ \hline MTCI & 0.69 & MTCI and SNDIVI & 0.96 \\ MTCI and NDVI & 0.95 & MTCI and RDVI & 0.98 \\ MTCI and LNDVI & 0.96 & MTCI and MTVI2 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model evaluation for random forest algorithm.  
Figure 6: The relationships between CCC and (**a**) MTCI, (**b**) MTCI \(\times\) NDVI, (**c**) MTCI \(\times\) LNDVI, (**d**) MTCI \(\times\) SNDIVI, (**e**) MTCI \(\times\) RDVI, and (**f**) MTCI \(\times\) MTVI2 for the simulation dataset, respectively. The color bar represents the dot density.