## 4 Discussion  
In this study, the feasibility of retrieving LAI of saltmarsh vegetation using PROSAIL and high spatial resolution RapidEye and Sentinel-2 satellite images was demonstrated. Using the minimum RMSE criterion, we observed a good relationship between the field measured and estimated LAI values using the RapidEye (R\({}^{2}\) =0.50, RMSE = 1.27 m\({}^{2}\)/m\({}^{2}\) and NRMSE = 0.27) and Sentinel-2 data (R\({}^{2}\) = 0.55, RMSE = 0.87 m\({}^{2}\)/m\({}^{2}\) and NRMSE = 0.17) (Figure 7). Furthermore, the errors obtained decreased when multiple solutions were utilised from both LUTs (Table 3). The measured LAI from the field campaigns showed moderate variability (coefficient of variation= 0.42 and 0.40 for 2015 and 2016, respectively), hence moderate retrieval accuracies [110] were obtained for both years. Furthermore,  
Figure 8: Spatial distribution of LAI in Schiermonnikogo island, the Netherlands. The maps are produced through inversion of PROSAIL using best fitting spectra from RapidEye (2015) Sentinel-2 (2016) images, respectively. the sensible retrieval accuracies of LAI which were obtained using data from the two sensors can also be explained by the large sizes of the two LUTs (500,000 records) that permitted appropriate sample selection. The significance of appropriate sample selection in the LUT approach is highlighted for model optimisation and efficiency as well as for the proper understanding of the ecosystem conditions [101]. In vegetation canopies, biophysical variables (such as LAI) play a significant role in characterising the canopy reflectance [24; 111]. However, the relationships between these variables and canopy reflectance are complex, as the reflectance of the canopy is a function of leaf optical properties, canopy architecture, background soil as well as illumination and viewing geometry [24; 108; 112; 113]. PROSAIL has been successful in explaining these interactions mainly in nonwoody vegetation and for modelling LAI in various vegetation types [51]. Few studies have demonstrated the suitability of the model for Mediterranean grassland ecosystems utilising hyperspectral measurements [27; 30; 48; 95; 96]. Also recently the PROSAIL model and airborne hyperspectral data have been successfully used in the wetland for retrieval of leaf mass area [32]. However, results of the current study confirmed that the PROSAIL model could be used in combination with data from new multispectral sensors such a Sentinel-2 and RapidEye to study the biophysical properties of grasslands in a saltmarsh ecosystem. It is known that LAI plays a key role in the magnitude of canopy reflectance [31]. Likewise, the results of sensitivity analysis confirmed that the canopy spectral reflectance from both RapidEye and Sentinel-2 sensors were highly sensitive to LAI variations (Figure 4). In general, the variation of all PROSAIL input parameters influenced the simulated RapidEye (475-805 nm) and Sentinel-2 (490-2190 nm) reflectance except leaf water content (not shown). Therefore, prior knowledge from field observations of input parameters assisted in model parameterisation. The leaf water content did not show any influence on the RapidEye simulated reflectance. This was expected as water-sensitive features mainly exist within the NIR and SWIR spectral regions [111] which are absent in RapidEye spectral data. A relatively close match was observed between the measured and simulated reflectance of RapidEye and Sentinel-2 (Figure 5), particularly for the visible, red-edge and SWIR spectral bands (from Sentinel-2) where the AAEs were lower than 0.02 (Figure 6). Nevertheless, a spectral mismatch between the measured and simulated reflectance was noticeable at NIR spectral bands which are sensitive to LAI, nitrogen content, leaf structure and dry matter [10; 11; 30; 48]. These results are in agreement with earlier findings by [11; 30; 48] who observed that the spectral bands in the NIR spectral region (around 800-850 nm) demonstrate relatively high errors using hyperspectral data.