This dataset is associated with the following article published in Remote Sensing Applications: Society and Environment, which can be accessed here: https://doi.org/10.1016/j.rsase.2024.101392 Shortly after publishing version 2, errors in the map projections were identified and corrected. Please use version 3 instead of version 2. Updates to version 2 were as follows: Includes watersheds in Texas that were not included in Version 1. A color map (using ArcPro) was added for improved interpretation. A sub-pixel scale offset in the change type map, related to map projection errors, was corrected. Mapping Coastal Wetland Changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat Time Series and National Wetland Inventories Courtney A. Di Vittorio1, Melita Wiles2, Yasin W. Rabby2, Saeed Movahedi2, Jacob Louie1, Lily Hezrony1, Esteban Coyoy Cifuentes1, Wes Hinchman1, Alex Schluter1 1Department of Engineering, Wake Forest University, Winston-Salem, North Carolina, USA. 2Department of Statistics, Wake Forest University, Winston-Salem, North Carolina, USA. Abstract The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), the widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution, and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses Google Earth Engine and the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing imagery with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The average overall accuracy of the algorithm is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsat-derived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the magnitude of severe changes matches that of the existing inventory and the magnitude of the moderate changes matches that of the more comparable product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.