# Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Spectral Index**} & \multicolumn{3}{c}{**Simulations**} & \multicolumn{3}{c}{**Measurements**} \\ \cline{2-7}  & **MTA** & **LAI** & **Fcover** & **MTA** & **LAI** & **Fcover** \\ \hline NDVI & \(-0.59\) & 0.71 & 0.96 & \(-0.53\) & 0.58 & 0.78 \\ EVI & \(-0.83\) & 0.52 & 0.93 & \(-0.75\) & 0.50 & 0.87 \\ EVI2 & \(-0.82\) & 0.54 & 0.93 & \(-0.76\) & 0.50 & 0.88 \\ OSAVI & \(-0.78\) & 0.58 & 0.95 & \(-0.71\) & 0.51 & 0.86 \\ MTVI2 & \(-0.81\) & 0.54 & 0.93 & \(-0.76\) & 0.50 & 0.88 \\ WDRVI & \(-0.59\) & 0.71 & 0.96 & \(-0.53\) & 0.58 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: [PERSON]’s correlation coefficient (\(R_{\mathrm{s}}\)) between spectral indices and canopy structural variables. In our study, the correlation between Fcover and WDRVI was the strongest with the \(R_{\rm s}\) values close to the value of 0.96 reported by [PERSON] [55] in the simulated dataset. They also proposed the following equation for calculating Fcover from remote sensing data:  
\[\text{Fcover}=80.838\text{WDRVI}+34.021 \tag{6}\]  
(Equation (2) in [55]). The proposed equation was more accurate for empirical (RMSE = 0.22, bias of mean = 0.18) than model-simulated (RMSE = 0.38, bias of mean = 0.37) data with some overestimation in both datasets. For measured data, the best-performing indexes for estimating Fcover were EVI2 and MTVI2, with \(R_{\rm s}=0.88\). In agreement with previous studies, we found that a new independent variable Fcover, a nonlinear combination of LAI and MTA, is the key driver of variation in the commonly used VIs. Hence, all the VIs studied here can be efficiently used to predict this structural parameter. The slightly better performance of EVI2 and MTVI2 can be attributed to the capability of the [PERSON]'s \(R\) to ignore the nonlinearity with Fcover. When the more traditional Pearson's \(R\) is used (despite the non-normality of the data), the index most strongly correlated with Fcover is OSAVI (\(R=0.85\)) with a marginal difference to EVI2 and MTVI2 (\(R=0.83\)). Similarly to this study, however, some nonlinearity and dependence on illumination conditions is expected [56] under varying conditions. The results corroborate earlier findings, especially those by [PERSON] et al. [33] and [PERSON] [55]. As we have demonstrated empirically, even for homogeneous crop canopies, NDVI or its more sophisticated counterparts, EVI or EVI2, depend not only on LAI, but also on the second structural parameter, MTA. Furthermore, the dependence of the most widely used VIs on MTA can be stronger than on LAI with the actual effects of the two canopy parameters depending on their actual range of variation. Therefore, in order to map LAI from multispectral data, effective methods need to be developed to remotely measure leaf angles. This can be achieved, for example, using canopy reflectance in the red edge [22]. Although Earth observation data in this spectral region is scarce, it is covered by some systems, most notably Sentinel 2. We hope that future investigations into the application of this sensor into MTA estimations will improve the mapping of MTA, and thus also LAI.