## 5 Conclusions  
In this study, we implemented a within-wetland eco-hydrological patch classification using the NDVI-based technique using NASA's Harmonized Landsat-Sentinel open-source dataset. The classification was applied on a coastal wetland in Louisiana, a brackish marsh dominated by intermittent _Juncus_ and _Spartina_ vegetation with sparse open water patches. The classification results were then used as input for the E3 SM Land Model to provide the model with necessary information about the wetland's vegetation cover over the simulated years and used to improve methane flux estimations from the site. Classification showed very good results for _Spartina_ and open water that were mostly, successfully classified, while _Juncus_ had a lower performance due to the close range of the NDVI compared to that of _Spartina_. The ELM simulations showed that accounting for the observed vegetation cover change over the years improved the representation of methane flux dynamics, while reducing the uncertainty of the total methane flux estimations. The error introduced by resampling WorldView data to the HLS resolution showed to have minimal effect on the classification and, consequently, the methane flux results. The NDVI-based classification approach offers a substitute for ground observations of vegetation patch-type distribution in wetlands worldwide. Using the processed remote sensing observations of vegetation patch-types will allow us to account for vegetation distribution impacts on the plant-mediated methane flux from wetlands, usually responsible for the significant methane emissions from coastal wetlands. Our study shows promising results for integrating the remote sensing-based mapping of land cover change into land surface models, which would significantly improve their performance and lead to more accurate predictions. This manuscript was written and prepared by [PERSON] and [PERSON] under the supervision of [PERSON]. The NDVI-based classification was originally conceived by [PERSON] and [PERSON]; [PERSON], [PERSON] and [PERSON] contributed to the field data measurements and the validating of the results of US-LA3. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the U.S. Department of Energy awards DE-SC0023084 and DE-SC0021067 and NASA FINNEST Fellowship 80 NSSC22K1527. Funding for eddy covariance data collection was provided by the USGS South Central Climate Adaptation Science Center, USGS LandCarbon Program and USGS Ecosystems Mission Area. Apache Louisiana Minerals provided access to the site of this study. HLS data can be found at [[https://lpdaac.usgs.gov/products/hlsl3](https://lpdaac.usgs.gov/products/hlsl3)]([https://lpdaac.usgs.gov/products/hlsl3](https://lpdaac.usgs.gov/products/hlsl3)) 0v002/ (accessed on 1 August 2023) and [[https://lpdaac.usgs.gov/products/hlss30v002/](https://lpdaac.usgs.gov/products/hlss30v002/)]([https://lpdaac.usgs.gov/products/hlss30v002/](https://lpdaac.usgs.gov/products/hlss30v002/)) (accessed on 1 August 2023), for the Landsat and Sentinel datasets, respectively. Our site falls within tile 16 RBT. The WorldView-3 multi-spectral image was requested through the Commercial SmallSat Data Acquisition (CSDA) Program as Pansharpened (level 2A). The Image ID is 10400100408C8100. The link to the CSDA program is as follows: [[https://www.earthdata.nasa.gov/esds/csda](https://www.earthdata.nasa.gov/esds/csda)]([https://www.earthdata.nasa.gov/esds/csda](https://www.earthdata.nasa.gov/esds/csda)) (accessed on 1 August 2023). The ELM version used in this study can be found at the following github repository: [[https://github.com/theresiayazbeck/ELM_WelfandLandunit.Perlmutter](https://github.com/theresiayazbeck/ELM_WelfandLandunit.Perlmutter)]([https://github.com/theresiayazbeck/ELM_WelfandLandunit.Perlmutter](https://github.com/theresiayazbeck/ELM_WelfandLandunit.Perlmutter)) (accessed on 1 August 2023).