Update text

pages/00_home.py

@@ -7,53 +7,23 @@ def Page():
         markdown = """
         ## Global Surface Water Information System (GSWIS)
         
-        ### Introduction
-
-        We have used five global datasets viz., ESA, ESRI, JRC, OSM and HydroLAKES, 
-        to generate a processed multi-band gridded GSWE dataset at 10 m spatial 
-        resolution, with each band corresponding to one of the five datasets used. 
-        The dataset comprises of a total of 5153 grids, each grid having a 2°×2° dimension. 
-        **Click on the GSWIS tab above to visualize the datasets interactively.**
-
-        ### Datasets
-
-        #### ESA
-        - **Description:** ESA Worldcover is a 10 m Sentinel-based global LULC dataset available in gridded format with 11 land cover classes
-        - **Website:** <https://esa-worldcover.org/en/data-access>
-        - **Year:** 2020
-        - **Water Classes:** Permanent water bodies [80], Herbaceous wetland [90], Mangroves [95] 
-        - **Band Name:** `esa`
-
-        #### Esri
-        - **Description:** ESRI Global Land cover product is a 10 m Sentinel-based dataset available in gridded format with 10 land cover classes
-        - **Website:** <https://livingatlas.arcgis.com/landcove>
-        - **Year:** 2020
-        - **Water Classes:** Water [1], Flooded vegetation [4] 
-        - **Band Name:** `esri`
-
-        #### JRC
-        - **Description:** Landsat-based JRC Yearly Water Classification History is a 30 m surface water extent dataset classified using year-by-year occurrence values from 1984-2021 (Pekel et al., 2016)
-        - **Website:** <https://esa-worldcover.org/en/data-access>
-        - **Year:** 2020
-        - **Water Classes:** Seasonal water [2], Permanent water [3]
-        - **Band Name:** `jrc`
-
-        #### OSM
-        - **Description:** OSM Water Layers is a 90 m gridded global surface water data generated by extracting surface water features from OpenStreetMap (Yamazaki et al., 2019)
-        - **Website:** <https://hydro.iis.u-tokyo.ac.jp/~yamadai/OSM_water>
-        - **Year:** 2019
-        - **Water Classes:** Ocean [1], Large Lake/River [2], Major River [3], Canal [4], Small stream [5]
-        - **Band Name:** `osm`
-
-        #### HydroLakes
-        - **Description:** HydroLAKES is vector Global Lake dataset derived from merged hydrography (Messager et al., 2016)
-        - **Website:** <https://www.hydrosheds.org/>
-        - **Year:** N/A
-        - **Water Classes:** Global Lakes with size of atleast 10 ha
-        - **Band Name:** `hydrolakes`
+        The Global Surface Water Information System (GSWIS) brings the emerging regional and global surface water extent datasets under one framework. This global platform allows users to instantaneously and interactively visualize and compare different datasets, understand their variations, and therefore select datasets that may be suitable for user-specific needs. With the FAIR (Findable, Accessible, Interoperable, and Reusable) open science principle at its core, GSWIS aims to maximize our understanding of the world’s surface water resources. 
 
+        ![](https://i.imgur.com/VowaLnd.gif)
 
+        ### Suggested Citations
 
+        - Rajib et al. 2023. A call for consistency and integration in global surface water estimates. *Environmental Research Letters*. <https://doi.org/10.1088/1748-9326/ad1722  >
+        - Rajib A, Khare A, Wu Q and Gupta B. 2023. Global Surface Water Information System (GSWIS). Available at: <https://gswis.gishub.org>
+
+        ### Acknowledgements
+
+        This work was conducted through funding support from the U.S. Department of Defense (grant # W912HZ2020071) and National Science Foundation (grant # 2336630). 
+
+        ![](https://i.imgur.com/yqFimrG.png)
+
+
+        Last update: December 2023.
         """
 
         solara.Markdown(markdown)

pages/01_datasets.py

@@ -0,0 +1,52 @@
+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:** <https://esa-worldcover.org/en/data-access>
+        - **Reference**: Zanaga et al 2021. <https://doi.org/10.5281/zenodo.5571936>
+        - **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:** <https://livingatlas.arcgis.com/landcove>
+        - **Reference**: Karra et al  2021. <https://doi.org/10.1109/IGARSS47720.2021.9553499>
+        - **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:** <https://global-surface-water.appspot.com/download>
+        - **Reference**: Pekel et al 2016. <https://doi.org/10.1038/nature20584>
+        - **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:** <https://hydro.iis.u-tokyo.ac.jp/~yamadai/OSM_water>
+        - **Reference**: Yamazaki et al 2019. <https://doi.org/10.1029/2019WR024873>
+        - **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:** <https://www.hydrosheds.org/products/hydrolakes>
+        - **Reference**: Messager et al 2016. <https://doi.org/10.1038/ncomms13603 >
+        - **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)