Dataset for “Satellite-Based Aquatic Plant Phenology: Quantifying Thermal Influences on Vegetation Dynamics in the Upo Wetland, South Korea (2022–2024)”

<b>This dataset contains all spatial datasets produced for the manuscript “Satellite-Based Aquatic Plant Phenology: Quantifying Thermal Influences on Vegetation Dynamics in the Upo Wetland, South Korea (2022–2024).”</b><br>The study investigates the spatiotemporal dynamics of aquatic plant phenology in the Upo Wetland using three years of Landsat 8/9 Level-2 imagery (2022–2024). The dataset includes grid-aggregated vegetation indices (NDVI, EVI), Landsat-derived surface water temperature, and phenology metrics extracted using a double logistic model.A 120 m × 120 m analysis grid was developed to integrate the thermal band resolution (100 m) with the optical band resolution (30 m). All vegetation indices and surface temperature values were spatially aggregated to this grid, which served as the fundamental analysis unit for phenology modeling.<b>Included data files:</b><br>The ZIP package contains seven GeoPackage (.gpkg) datasets:<br>(1) A full-period phenology metrics file (SOS, EOS, LGS from NDVI/EVI).<br>(2) Six date-specific datasets containing surface temperature, NDVI, and EVI for key observation dates in 2022, 2023, and 2024.<br><b>Methods Overview:</b>NDVI and EVI were computed using standard Landsat reflectance-based formulas.Surface water temperature was derived from the Landsat thermal infrared band.All values were aggregated to 120 m grid polygons.Phenology metrics (Start of Season, End of Season, Length of Growing Season) were extracted using a double logistic curve fitting approach.Analyses were conducted in R (version 4.4.3), using the <i>phenofit</i> and <i>sfdep</i> packages.<b>Coordinate Reference System:</b><br>All files use EPSG:32652 (UTM Zone 52N, WGS84), matching the CRS used in the manuscript’s analytical workflow.<br><b>Use Cases:</b><br>This dataset supports reproducible wetland phenology research, long-term monitoring of aquatic vegetation, climatic impact assessment, and validation of remote-sensing-based phenology models.