## 4 Discussion
### ProSail  
To establish how flood duration affects reflectance, we used the physical model ProSail. This model combines information on leaf and canopy levels to simulate reflectance spectra. The performed analysis showed that vegetation density and cover, represented by LAI, is the most influential in the reflectance spectra. Therefore, the drivers seem to produce large scale spatial patterns by affecting local values of LAI. LAI is one of the main driving variables and can affect the entire spectrum [12], however, measuring LAI can be challenging [63]. A recent global review of ProSail showed that ProSail is often used to model LAI, however, there are several problems associated with it [12]. The inversion of LAI can strongly depend upon the number of satellite bands used in agricultural situations [64] and can saturate at higher LAI values [12; 64]. This saturation occurred around LAI values of 6 [64], which are unlikely to ever occur in European salt marshes. Another problem associated with ProSail in agricultural areas is that at earliest and late growth stages with very low LAI, the background reflectance dominates the spectral signal [12]. This is reported at the earliest growth stages [12], As the measured LAI and the vegetation cover show the soil is not completely covered hence soil reflectance does contribute to the reflectance spectra. The LAI estimate could be improved by including in situ soil spectra in the model. In this study LAI was estimated with an inversion of the ProSail model, and compared with in situ LAI and cover measurements. This showed a significant relation between modelled and measured LAI and cover, but also indicated the inversion contains significant noise and has a relatively high RMSE. We found a strong correlation between NDVI and our LAI estimate. NDVI is one of the most often used methods of describing spatial variation in vegetation. The high correlation with NDVI shows that the inverted LAI represent the spatial patterns well. We would caution against using this LAI inversion in an application where the absolute LAI values are of great importance. This analysis was performed with RapidEye, which has a multispectral satellite sensor with five spectral bands. This satellite constellation is often used for vegetation studies, however the estimation of Leaf Area Index might be improved by using a satellite with more spectral bands. A neural networking approach showed that, for LAI estimations, seven bands is optimal [64]. A better LAI estimate may be obtained with a satellite sensor with both a better spectral and radiometric resolution. RapidEye has a 12-bit digitization with low readout noise [65]. Its dynamic range of 12-bits/pixel is comparable to other broad-band sensors with a similar spatial resolution, such as Pleiades and SPOT 6/7, but is worse than, for example, KompSat and Sentinel-2 [66]. On a large spatial scale the effects of leaf properties (chlorophyll a+b, caroten) could not be detected. The canopy properties (leaf area index and dry/wet soil fraction) largely overwrite the effects of the leaf properties. ProSail is mainly used in agricultural settings [12; 13; 67; 68; 69; 70], often used to study chlorophyll [67; 71; 72]. We used ProSail to simulate the effect of chlorophyll on reflectance at the leaf scale and found that the range of chlorophyll contents among the leaves observed in the study site was not enough to explain large-scale variations in reflectance, suggesting that chlorophyll content of the leaves does not play a major role in large scale spatial patterns in the pioneer zone of salt marshes. It is likely that a wider range of values (e.g., in chlorophyll content of the leaves) will occur when multiple seasons are measured. However, we only studied a single species during a single season (i.e., _Spartina anglica_ during mid spring). As a consequence, the chlorophyll content at the leaf level had very limited variability and so it had a limited effect on reflectance.