## 4 Discussion
It is anticipated that the spectral bands from this region (800-850 nm) are prominently sensitive to noise or are not well modelled by RTM. The relationship between the field measured and retrieved LAI using the smallest RMSE criterion was stronger employing the Sentinel-2 data (NRMSE = 0.17, RMSE = 0.87 m\({}^{2}\)/m\({}^{2}\), R\({}^{2}\) = 0.55) in comparison of using the RapidEye data (NRMSE = 0.27, RMSE = 1.27 m\({}^{2}\)/m\({}^{2}\) and R\({}^{2}\) = 0.50). These accuracies are lower than those obtained by [48; 95] who utilised hyperspectral data and the PROSAIL model with a LUT-based inversion for LAI estimation in grasslands (RMSE = 0.63 m\({}^{2}\)/m\({}^{2}\), 0.53 m\({}^{2}\)/m\({}^{2}\) and R\({}^{2}\) = 0.89, 0.91). The coefficient of determinations obtained in this study are also lower than those in our earlier studies where we used hyperspectral data in Mediterranean grassland to retrieve LAI from PROSAIL inversion (R\({}^{2}\) = 0.86) albeit with a higher RMSE (RMSE = 0.89 m\({}^{2}\)/m\({}^{2}\)) [27; 30]. The obtained errors from Sentinel-2 data are, however, comparable to those from [96] who used various hyperspectral measurements for LAI estimation in central Europe (RMSE = 0.86 m\({}^{2}\)/m\({}^{2}\), 0.74 m\({}^{2}\)/m\({}^{2}\)). Sentinel-2 data benefits from spectral bands at NIR and SWIR regions which are crucial for LAI studies [31; 44]. Therefore, the lower retrieval accuracy obtained using the RapidEye data partly is explained by the absence of these spectral bands. Nevertheless, RapidEye data has a very fine spatial resolution which makes the sensor unique and less prone to mixed pixels challenges. This importance was confirmed when spectral subset B of Sentinel 2 with central wavelengths similar to those from RapidEye were selected (490 nm, 560 nm, 665 nm, 705 nm, and 783 nm) for the inversion and returned very low retrieval accuracy for LAI in comparison to those from RapidEye (Table 4). To address and reduce the ill-posedness of model inversion some approaches are investigated in the literature such as choosing a proper spectral subset [30; 101], ecological constraints [11; 114], prior knowledge [100; 106] and spatial regularisation [75; 115]. In this study, the prior field information which was acquired during the two field campaigns were used to determine the range of model input parameters and to limit the implausible combinations. Moreover, the LAI was estimated using the mean of the first 10, 50 and 100 best solutions from the LUTs. The result of this analysis demonstrated that utilising the mean of the first 10 best solutions the retrieval accuracy of LAI would increase from both RapidEye and Sentinel-2 data (R\({}^{2}\) = 0.51, 0.59 NRMSE = 0.24, 0.16, respectively) (Table 3). The obtained results are in agreement with that of [30; 63; 48] who demonstrated that the use of multiple solutions results in more robust retrieval accuracy. In general, biophysical variables such as LAI, canopy architecture (e.g., leaf angle distribution) and biomass present major contributions to the canopy reflectance, mainly in near-infrared (NIR) and SWIR regions [10; 29; 31; 35; 44; 116]. This was confirmed in our study when NIR and SWIR spectral bands of Sentinel-2 were used as a spectral subset for the inversion (Table 4). Utilising different Sentinel-2' spectral subsets showed that the spectral subset C which contained the four spectral bands of NIR and SWIR (842 nm, 865 nm, 1610 nm, 2210 nm), in comparison to using other spectral subsets resulted in higher retrieval accuracy for LAI (R\({}^{2}\) = 0.44, NRMSE = 0.21). As such, [73] examined the LAI retrieval of crops using simulated Sentinel-2 data as well as various parametric and non-parametric models and found that SWIR bands (1610 nm, 2210 nm) in combination with the green band (490 nm) are important spectral bands for LAI estimation. Furthermore, [78] demonstrated that the red (665 nm) and NIR (842 nm) spectral bands of Sentinel-2 are important bands for LAI prediction of the potato crop.