The U.S. Geological Survey Oregon Water Science Center, in cooperation with The Klamath Tribes initiated a project to understand changes in the surface-water extent of Klamath Marsh, Oregon and changes in groundwater levels within and surrounding the marsh. The initial phase of the study focused on developing datasets needed for future interpretive phases of the investigation. This data release documents the creation of a geospatial dataset of January through May maximum surface-water extent based on a model developed by John Jones (2015; 2019) to detect surface-water inundation within vegetated areas from satellite imagery. The Dynamic Surface Water Extent (DSWE) model uses Landsat at-surface reflectance imagery paired with a digital elevation model to classify pixels within a Landsat scene as one of the following types: “not water”, “water – high confidence”, “water – moderate confidence”, “wetland – moderate confidence”, “wetland – low confidence”, and “cloud/shadow/snow” (Jones, 2015; Walker and others, 2020). The model has been replicated by Walker and others (2020) for use within the Google Earth Engine (GEE, https://code.earthengine.google.com/) online geospatial processing platform. The GEE version of the DSWE model enables users who have limited computer processing power to access DSWE datasets. The JavaScript-based interface enables the selection of specific timeframes for analyzing surface water extent as well as creating composite scenes of maximum surface water extent (MSWE) over a specified timeframe. The GEE platform was used to create MSWE datasets showing maximum surface water inundation within the Klamath Marsh for the month of January through May during 1985 – 2021. The dataset presented here includes a summary file of maps and figures (.pdf), surface area calculations of January through May MSWE in tabular (.csv) format, study area polygon in vector (.shp) format, and 37 January through May MSWE scenes in raster (.tif) and vector (.shp) format. References Cited Jones, J.W., 2015, Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network. Remote Sensing, 7, 12503–12538. Jones, J.W., 2019, Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests. Remote Sensing, 11, 374. https://doi.org/10.3390/rs11040374 Walker, J.J., Petrakis, R.E., and Soulard, C.E., 2020, Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps: U.S. Geological Survey data release, https://doi.org/10.5066/P9LH9YYF.