## 4 Discussion  
The confusion matrix results show that _Spartina_-dominated patches were better classified than _Juncus_-dominated ones, where the base-year HLS classification for _Spartina_ had a 93.45% match with the WorldView classification compared to 55.66% for _Juncus_, which was often misclassified as _Spartina_. This is largely due to the fact that both species' NDVI timeseries are close in terms of absolute values, with a difference in that _Juncus_ shows a distinct NDVI peak in July, i.e., an increase in the early growing season, that is then followed by a browning period at the end of the season, while _Spartina_ is more constant over the year (Figure 3). The site is dominated by _Spartina_, where _Juncus_ patches are dispersed within _Spartina_-dominated areas. As shown in Figure 7b, most of the misclassified pixels for both _Juncus_ and _Spartina_ are present in the middle of the wetland (away from water edges). We suspect that these pixels present a true near-equal mix of both vegetations, which led to this apparent misclassification. The abundance of these misclassified pixels (yellow and green pixels in Figure 7b) is similar for both vegetation types in that central area, but since _Juncus_ is much less abundant than _Spartina_, they represent a large portion of the _Juncus_, leading to the high percentage of misclassified _Juncus_ pixels. As for misclassified open water pixels,  
Figure 5: US-LA3 aerenchyma flux simulated with the ELM over years 2016–2023, for which we compare the ELM results using a constant average vegetation cover (blue line) and HLS-derived yearly vegetation cover (orange line). Figure 6: Regression of modeled vs. observed total methane flux over the years 2021–2022 (where flux observations were available) for ELM simulations using average vegetation coverage (**left panel**) and HLS-classified vegetation cover. The black line represents the regression fit, and the red line represents the ideal 1:1 line. most were misclassified as _Spartina_ and were mostly present at the vegetation-water edge (Figure 7b). Our results correspond with those of several studies that made use of reflectance data to obtain NDVIs and use them as a tool for land cover classification [37; 38], with a special focus on vegetation classification in inundated areas [39; 40]. [PERSON] et al. [41] showed that the differences in saltmarsh communities' spectral characteristics is a function of their phenological stages, while [PERSON] et al. [42] has showed that spectral characteristics across seasons improve saltmarsh vegetation classification. Thus, the inclusion of NDVI timeseries instead of relying on NDVI point measurements plays an important role in improving wetland mapping [43]. A similar approach to our NDVI-based classification of within-wetland vegetation type was presented by [PERSON] et al. [44], where the HuanJing 1 (HJ-1) satellite was used to build monthly NDVI timeseries and classify saltmarshes in the coast of Jiangsu in east China, which resulted in accurate classifications of vegetation types, thus emphasizing the promising role that NDVI timeseries has in classifying within-wetland eco-hydrological patches [45]. Including the classification results as input in the ELM simulations led to substantial improvements in the plant-mediated methane flux, known as aerenchyma (Figure 5), whereby in including the yearly vegetation classification, the methane flux was then a function of the vegetation cover and the impact of the latter is shown in the ELM-output methane flux. _Spartina_ is more conductive to methane flux compared to _Juncus_[36]; thus, whenever more _Juncus_ is present on-site, a lesser total of aerenchyma flux should be emitted from the site. Note that this does not necessarily mean that more total methane flux would be emitted.