## Materials and Methods
### Spatial Drivers  
As spatial drivers, we investigated four factors expected to have a large effect on salt marshes patterns--flood duration, wave forcing, competition, and creep influence.  
Flood duration or inundation time is often estimated from elevation, and is expected to affect spatial patterns in salt marshes, likely because it causes stress in plants. Previous works has shown that flood duration is an important driver behind spatial patterns [2; 31; 32], and a small change in inundation time can cause large shifts in competitive interactions [26], which in turn can affect the reflectance of our study species. A map of flooding duration (resolution 20m) of the Westerschelde estuary was provided by Rijkswaterstaat [49]. This map is based on elevation data collected in 2016 using airborne laser altimetry, with an accuracy of \(\pm\)10 cm, in combination with tide modelling, the tide model has a maximum error margin of \(\pm\)3% (see [49]).We resampled this map to a 5 m resolution using bilinear interpolation.  
Wave forcing can shape salt marshes [36] and facilitate sediment resuspension [37]. Vegetation attenuates waves [33; 34; 35], decreasing their influence further inland, which may create a spatial pattern. To quantify wave mitigation, we used the distance to the bare mudflat (i.e., the seaward vegetation edge). This was calculated with the Euclidean distance tool in ArcGIS 10.1. The seaward vegetation edge was derived from the RapidEye image (see spaceborne data), a NDVI threshold of 0 was used to distinguish between marsh and unvegetated foreshore (i.e., the mudflat).  
Competition is also known to be an important driver for plant zonation in salt marshes [39]. Transplantation experiments indicated that facilitation and competition play an important role in determining spatial patterns in salt marshes. These experiments showed that species adapted to grow at low elevation (such as our study species) are competitively excluded from higher elevations, even though the high intertidal zone provides a more suitable habitat [2; 38]. The effect of inter-species competition was quantified using the distance to another major vegetation type. For this, a vegetation map of 2010 by Rijkswaterstaat was used [50], based on aerial photographs and field determination. The vegetation was recorded in vegetation types, but also included an estimated cover of our study species. An area was considered dominated by _Spartina anglica_ when more than half of the total vegetated surface area was covered by this species. The distance to non-_Spartina_ vegetation was calculated using the Euclidean distance tool in ArcGIS.  
Creeks are also known to be a shaping feature [27; 31; 40], with a significant influence on soil properties and sediment accretion rates [41], which in turn contributes to vegetation zonation [26]. The distance to the nearest Creek is often used to quantify their influence [31; 40; 41]. Creeks were manually traced from a high resolution (0.25 \(\times\) 0.25m) aerial photograph from 2016 provided by Rijkswaterstaat. Only creeks larger than 75 cm (3 pixels) were recorded. The distance to the nearest creek was calculated using the Euclidean distance tool in ArcGIS.  
The four maps representing the effects of the four spatial drivers all had a resolution of 5 m and were compared with the in situ measured vegetation properties to identify which vegetation characteristics were most affected by the spatial drivers. For this analysis, spatial driver information was extracted from the maps at the dGPS coordinates of the measured plots. Linear regression models, using the lm function in R) were used to compare the different spatial drivers with the in situ vegetation properties.