###### Abstract
VIs are also sensitive to soil background (e.g., grain-size, moisture content, or organic matter), chlorophyll content or spatial orientation of leaves [13], this broad sensitivity makes it difficult to establish how each characteristic is being affected, as the VI is a result of the combination of all of them. Physical models allow for a more in-depth analysis, and can be used to establish the effects of individual vegetation characteristics on reflectance. Physical models use properties of the object under study as parameters and apply the physical laws to simulate reflectance. Often these models are then inverted to estimate object properties from reflectance [13; 14; 15] (for a more extensive introduction on reflectance modelling and advantages over correlation models see Reference [12]). This modelling approach allows each modelled parameter to be studied separately and its individual effect size can be established, which is a major advantage over correlation studies based on simple VI's (see Reference [12]). As our in-depth study of the effects of biotic and abiotic processes on vegetation properties depends on isolating the effects of vegetation properties to study their effect on reflectance, we will apply the physical model ProSail (see the Methods Section for further details). Salt marshes provide valuable ecosystem services; they support local fisheries by providing a refuge for juvenile fish [16; 17; 18], economic services such as increased tourism [18; 19], reduced nutrient loading in coastal waters [18], and wave mitigation and flood protection [20; 21; 22]. The ecosystem services of tidal salt marshes have even be said to parallel those of mangrove forests [18], which are known to be extremely valuable [23; 24; 25]. Hence, understanding the processes that drive the development of spatial patterns in marshes will help us to safeguard these ecosystems and the services they provide. The effect of vegetation properties on spatial patterns in this valuable ecosystem is still poorly understood. Previous studies mainly focused on the effects of vegetation zonation on spatial patterns [2; 26; 27; 28; 29; 30]. These vegetation zonation studies reported various influential factors; highly influential factors are: Inundation time [2; 26; 31; 32], wave forcing [33; 34; 35; 36; 37], competition [2; 38; 39], and Creek influence [26; 27; 31; 40; 41]. The aim of this paper is to gain insight into which factors drive spatial patterns in a mono-specific saltmarsh vegetation. Our primary aim is to describe the relative effect of four landscape shaping processes, i.e., flooding duration, wave forcing, competition, and Creek formation, on reflectance of a single salt marsh species. Our secondary aim is to identify through which vegetation properties these effects propagate to reflectance, using a radiative transfer model to simulate vegetation reflectance. As we aimed to model how spatial drivers affect reflectance, we focused on a single wide-spread species _Spartima anglica_, which is a common pioneer grass. _S. anglica_ occurs nearest to the water, but is outcompeted at higher elevations [2; 38]. This species faces multiple stressors and hence is likely to manifest differences in biophysical characteristics (e.g., leaf area index or chlorophyll content). In addition, it occurs in sufficiently large areas to be observable from a spaceborne platform.