2017 mangrove forest extent mapping data products at 20m resolution for 11 countries in West Africa created using a combination of Sentinel-2 and Sentinel-1 satellite imagery (Senegal, The Gambia, Guinea Bissau, Guinea, Sierra Leone, Liberia, Cote D’Ivoire, Ghana, Togo, Benin, and Nigeria). This dataset includes 9 GeoTIFF files (Senegal and The Gambia combined, Togo and Benin combined).

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their structure is poorly mapped due to the use of expensive and time intensive methods. Bathymetric mapping from space has sought to alleviate this but has remained dependent upon in situ water-based measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine geospatial cloud platform, to create wall-to-wall high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three common Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10-15%) when compared with in situ NOAA DEM data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for a fully space-borne approach to map nearshore bathymetry.
Here we provide the Sentinel-2 mosaics, ICESat-2 bathymetric profiles and Satellite-Derived Bathymetry (SDB) models

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their structure is poorly mapped due to the use of expensive and time intensive methods. Bathymetric mapping from space has sought to alleviate this but has remained dependent upon in situ water-based measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine geospatial cloud platform, to create wall-to-wall high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three common Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10-15%) when compared with in situ NOAA DEM data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for a fully space-borne approach to map nearshore bathymetry.
Here we provide the Sentinel-2 mosaics, ICESat-2 bathymetric profiles and Satellite-Derived Bathymetry (SDB) models