import solara @solara.component def Page(): with solara.Column(align="center"): markdown = """ GSWIS currently houses five global surface water datasets used to generate a processed, multi-band gridded dataset at 10-meter spatial resolution, with each band corresponding to one of the five datasets used. **Click on the Explore tab above to visualize the datasets interactively.** ### Datasets #### European Space Agency (ESA) WorldCover - **Description:** ESA WC is a 10-m Sentinel-based gridded global LULC dataset - **Website:** - **Reference**: Zanaga et al 2021. - **Year used in GSWIS:** 2020 - **Water Classes used in GSWIS:** Permanent water bodies [80], Herbaceous wetland [90], Mangroves [95] #### Environmental Systems Research Institute (ESRI) Global Land Cover(GLC) product - **Description:** ESRI GLC is a 10-m Sentinel-based gridded global LULC dataset - **Website:** - **Reference**: Karra et al 2021. - **Year used in GSWIS:** 2020 - **Water Classes used in GSWIS:** Water [1], Flooded vegetation [4] #### Joint Research Centre (JRC) Global Surface Water (GSW) - **Description:** GSW is a 30-m Landsat-based gridded global surface water extent dataset - **Website:** - **Reference**: Pekel et al 2016. - **Year used in GSWIS:** 2020 - **Water Classes used in GSWIS:** Seasonal water [2], Permanent water [3] from Yearly Water Classification History #### OpenStreetMap (OSM) Water Layers - **Description:** OSM is a 90-m gridded global surface water data generated by extracting surface water features from OpenStreetMap - **Website:** - **Reference**: Yamazaki et al 2019. - **Year used in GSWIS:** 2019 - **Water Classes used in GSWIS:** Ocean [1], Large Lake/River [2], Major River [3] #### HydroLakes - **Description:** HydroLAKES is vector global lake dataset derived from merged hydrography - **Website:** - **Reference**: Messager et al 2016. - **Year used in GSWIS:** HydroLAKES is not associated with any specific time or year - **Water Classes used in GSWIS:** Global Lakes with size of at least 10 ha **Note: Numbers indicate the class ID in the original dataset** """ solara.Markdown(markdown)