# Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops
## 4 Discussion  
Clearly, the observed correlation with the leaf inclination angle in the field crop canopy (quantified by MTA) cannot be ignored when estimating other canopy biophysical parameters. Although it is possible that the spectral effect is caused by some other parameter strongly correlated with MTA, no other parameter has been identified in earlier modeling studies [(29)]. Thus far, the influence of LAD on VIs has not been adequately investigated with empirical data. In this study, we attempted to bridge this gap for six commonly used multispectral VIs characterizing canopy greenness. The field-measured set of vegetation parameters covers a wide range of values covering a large majority of natural variations in both LAI and MTA. This allowed us to use a simple comparison of correlation coefficients, which depend on the level of variation included in each of the studied variables. Since the measured MTA and LAI ranges are representative of their natural variation, a comparison of the values of \(R_{s}\) tells us about the role of each vegetation parameter in determining the studied VIs. According to both PROSAIL model simulations and empirical data, all studied VIs depended on MTA with NDVI being the least affected. This supports the findings of previous studies by [PERSON] et al. [(33)]. WDRVI is a nonlinear transformation of NDVI performed identically with NDVI when measured with [PERSON]'s R, however, the more common Pearson's \(R^{2}\)-value was slightly higher for WDRVI (0.25 and 0.30 for measured NDVI and WDRVI, respectively). NDVI and WDRVI were also the two indices with the strongest correlations with LAI in both the modeled and measured data (Table 2). However, the relatively strong (compared with other indices studied here) correlation between LAI and NDVI is compromised by the well-known saturation of the VI's sensitivity to LAI evident in the variation of the distance between the lines in Figure 1a. The same non-linearity is present in WDRVI (Figure 1f). EVI, EVI2 and MTVI2 were designed to be more sensitive to the variations inside the canopy by minimizing background influence, and working at large LAI values without saturation. In our measured dataset, the indices saturate less, with the lines in Figure 2b-e nearly parallel and more closely equidistant. Unfortunately, these VIs were more strongly correlated with MTA (\(R_{\mathrm{s}}<-0.71\)) than NDVI and WDRVI. The specific strength of the correlation between MTA and NDVI and WDRVI may somewhat depend on the spectral properties of soil. Using the sample curves in Figure 1b, a narrow-leafed lupin (MTA = 18\({}^{\circ}\)) canopy with LAI = 1 would have EVI \(\approx\) 0.5. A similar EVI value is produced by an oat (MTA = 58\({}^{\circ}\)) canopy of LAI = 4. Our results demonstrate empirically that it is very difficult to quantify LAI using remotely sensed VIs when the MTA is unknown [(49)]. EVI and EVI2 are widely used for tracing the dynamics of vegetation gross primary production (GPP) and phenology inter- or inner-annually [(50; 51; 52; 53)]. As MTA had a stronger effect on these VIs than LAI, caution should be used when applying them to estimate LAI across structurally potentially different canopies, e.g., seasonality studies or application over multiple crop species. Depending on cover type and LAI values, it may be more optimal to use NDVI or WDRVI, which produced relatively high \(R_{\mathrm{s}}\) with LAI in both model simulations and empirical data: the \(R_{\mathrm{s}}\) between the LAI and NDVI was 0.71 and 0.58 in simulated and measured data, respectively. This also agrees quantitatively with the previous finding by [PERSON] et al. [(54)] of \(R^{2}=0.33\) for data pooled over five vegetation types (soybean, corn, vineyards, poplar plantation and deciduous forest).