{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T07:38:05Z","timestamp":1775979485239,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-SC0023084"],"award-info":[{"award-number":["DE-SC0023084"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-SC0021067"],"award-info":[{"award-number":["DE-SC0021067"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["80NSSC22K1527"],"award-info":[{"award-number":["80NSSC22K1527"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"NASA FINNEST Fellowship","doi-asserted-by":"publisher","award":["DE-SC0023084"],"award-info":[{"award-number":["DE-SC0023084"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"NASA FINNEST Fellowship","doi-asserted-by":"publisher","award":["DE-SC0021067"],"award-info":[{"award-number":["DE-SC0021067"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"NASA FINNEST Fellowship","doi-asserted-by":"publisher","award":["80NSSC22K1527"],"award-info":[{"award-number":["80NSSC22K1527"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"name":"USGS South Central Climate Adaptation Science Center, USGS LandCarbon Program and USGS Ecosystems Mission Area","award":["DE-SC0023084"],"award-info":[{"award-number":["DE-SC0023084"]}]},{"name":"USGS South Central Climate Adaptation Science Center, USGS LandCarbon Program and USGS Ecosystems Mission Area","award":["DE-SC0021067"],"award-info":[{"award-number":["DE-SC0021067"]}]},{"name":"USGS South Central Climate Adaptation Science Center, USGS LandCarbon Program and USGS Ecosystems Mission Area","award":["80NSSC22K1527"],"award-info":[{"award-number":["80NSSC22K1527"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model\u2019s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.<\/jats:p>","DOI":"10.3390\/rs16060946","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T03:52:11Z","timestamp":1709869931000},"page":"946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6073-0617","authenticated-orcid":false,"given":"Theresia","family":"Yazbeck","sequence":"first","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9209-9540","authenticated-orcid":false,"given":"Gil","family":"Bohrer","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5702-6285","authenticated-orcid":false,"given":"Oleksandr","family":"Shchehlov","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH 43210, USA"},{"name":"Ukrainian Hydrometeorological Institute of the State Emergency Service of Ukraine and the National Academy of Science of Ukraine, 03028 Kyiv, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5047-5464","authenticated-orcid":false,"given":"Eric","family":"Ward","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Robert","family":"Bordelon","sequence":"additional","affiliation":[{"name":"School of Geosciences, University of Louisiana at Lafayette, Lafayette, LA 70504, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1130-9401","authenticated-orcid":false,"given":"Jorge A.","family":"Villa","sequence":"additional","affiliation":[{"name":"School of Geosciences, University of Louisiana at Lafayette, Lafayette, LA 70504, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7091-8456","authenticated-orcid":false,"given":"Yang","family":"Ju","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH 43210, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"ref_1","unstructured":"Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D.J., Mauritsen, T., Palmer, M.D., and Watanabe, M. 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