## 4 Discussion  
The field data used in this analysis had some inherent natural limitations. For example, the leaf chlorophyll content (Cab) and green LAI are often closely related [41], which was also the case for the field data used in the study (Figure 4). As the application of nitrogen increases the chlorophyll content [42], the level of fertilization has an impact on the performance of LAI-sensitive VIs if these also depend on Cab. Furthermore, a similar indirect influence of Cab on the studied VIs is possible if the Cab values are dominated by between-species differences. In addition to natural correlations, the experimental design of the study was not fully driven by the objectives of this research. We used the field data available from numerous crop management experiments carried out in the area covered by airborne IS data. We accounted for the imbalanced nature of the field data as much as possible and used crop reflectance simulations of uniformly distributed input parameters for generalization. Our results, both computer simulated and those retrieved from field data, are generally consistent with the numerous published findings, which state that the selected VIs can indeed be utilized for measuring LAI with remote sensing (e.g., [23]): \(\tau_{k}\) was between 0.34 and 0.64 for all the selected VIs. However, the relationship was nonlinear [20; 43], and some indices (e.g., NDVI) saturated at high LAI values [44]. In both field-measured and simulated data, correlation coefficients between VIs and LAI were low (\(\tau_{k}\) was between 0.34 and 0.64), even though the selected indices were clearly sensitive to LAI. This is in agreement with other studies [26; 45; 46], which have found a wide range of coefficients of determination (\(0.05<R^{2}<0.66\)) between VIs and LAI. It is known that differences between crop species affect the goodness of fit more than the vegetation indices used [47]. Evidently, the coefficients were affected by the large volume of simulated data and the range of species with different characteristics in the true data. Both datasets included sufficient structural and biochemical variation to blur the relationships between LAI and VIs. Estimating the LAI of heterogeneous vegetated areas (with subpixel heterogeneity) from remote sensing data is hence not as reliable as estimation of the LAI of homogeneous fields. This is demonstrated by Figure 7 and Table 5, where the correlations improved and correlation coefficients increased from the range of 0.38-0.64 to 0.72-0.93 when a structural parameter, MTA, was fixed. Other studies have also shown the relationship between VIs and LAI to vary across vegetation types (canopy architecture) and the correlations to improve when analyzing the relationship between VIs and LAI for each vegetation type separately [48; 49]. The leaf angle distribution, and thus MTA, affects the spectral properties of a canopy [50] to a degree that confuses LAI estimation algorithms based on simple VIs [50]. Figure 8: Correlation between vegetation indices and the leaf area index (LAI) for a fixed MTA of 57\({}^{\circ}\) and different leaf chlorophyll contents (Cab = 25–30, 55–60, 95–100 \(\upmu\)g cm\({}^{-2}\)): (**a**) BNDVI, (**b**) GARI, (**c**) GNDVI, (**d**) MSAVI, (**e**) MSR\({}_{705}\), (**f**) NDVI, (**g**) SAVI, (**h**) SR. Canopy reflectance simulated with PROSAIL. Based on its performance in both field-measured and model-simulated data, the best index was BNDVI. It was only slightly sensitive to MTA, especially for low LAI values (Figure 7a), and insensitive to Cab (Figure 8a). Two indices (GARI, GNDVI) (\(\tau_{k}\) = 0.50) performed slightly better than BNDVI (\(\tau_{k}\) = 0.48) in the field study and were insensitive to MTA (Figure 7b,c). Unfortunately, both indices were sensitive to Cab (Figure 8b,c).