Description
This dataset contains Level-3 Dynamic OPERA provisional surface water extent product version 1. The data are provisional surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.
Resources
| Name | Format | Description | Link |
|---|---|---|---|
| Download this dataset through a directory map | HTML | HTTPS endpoint for data browse and download | Open resource |
| View documentation related to this dataset | Product Visualization Guide | Open resource | |
| View an important notice for this dataset | Issue: Cloud dilation and cirrus cloud labeling may obscure valid water detection. <br> Description: The DSWx WTR layer inherits labels for cloud, cloud shadow, and adjacent to cloud from the input HLS Fmask. The current HLS implementation of Fmask does not distinguish cirrus from other cloud types and pixels labeled as clouds are dilated (buffered) by a large distance to conservatively remove reflectance values that might be errant. However, the DSWx algorithm often correctly distinguishes inundated land ‘under’ these masked areas. <br> Recommendation(s): If this issue is problematic and the application permits, users are advised to composite multiple days of DSWx WTR observations, taking the minimum non-zero value across all dates in the composite period. Should this prove inadequate or when specific dates are of interest, the DSWx WTR-2 layer is explicitly designed as a potential substitute for the WTR layer when snow/ice isn’t prevalent, but cloud masking is excessive. No cloud, cloud shadow, or adjacent to cloud masking is applied to create WTR-2. See the Product Specification and Algorithm Theoretical Basis Documents for more information on DSWx band characteristics and purposes. <br><br> Issue: Occasional erroneous snow/ice labels. <br> Description: The DSWx WTR layer inherits snow/ice classification directly from the input HLS Fmask. Occasionally, Fmask misclassification occurs over water with atypical coloration due to dissolved solids, high concentrations of sediment or where waves are breaking along coastline segments. <br> Recommendation(s): HLS Fmask based labels for snow/ice have not been applied to the DSWx WTR-2 layer. When snow/ice labeling is observed in the DSWx WTR layer and they are not expected, users are advised to check the DSWx WTR-2 layer and consider whether this layer or a combination of it and just cloud-related labels might be applicable to their study area and application. See the Product Specification Document for a description of applicable class values in the DSWx CLOUD layer. <br><br> Issue: Occasional unmasked clouds over ocean. <br> Description: The DSWx WTR layer inherits labels for cloud, cloud shadow, and adjacent to cloud from the input HLS Fmask. Occasionally, the DSWx algorithm will classify as ‘not water’, clouds present over water that Fmask failed to detect. This visual artifact does not impact DSWx performance over dominantly land areas. <br> Recommendation(s): If this artifact unduly affects product utility and simple masking of ocean areas are not appropriate, the user is advised to temporally composite DSWx, taking the lowest non-zero value in each composite period. See the Product Specification Document for a description of applicable class values in the DSWx CLOUD layer. | Open resource | |
| View this dataset's publications | HTML | Jones, John W. “Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests.” Remote Sensing, vol. 11, no. 4, 2019 374. doi: 10.3390/rs11040374 | Open resource |
| View this dataset's product usage | HTML | OPERA Calibration/Validation Jupyter Notebook Tutorials | Open resource |
| Web Page | HTML | Data Subscriber | Open resource |
| Get a related visualization through WORLDVIEW | HTML | OPERA DSWx-HLS in Worldview | Open resource |
| View this dataset's data citation policy | HTML | Data Use and Citation Policy | Open resource |
| This dataset's landing page | HTML | OPERA DSWx-HLS Provisional Product Version 1 | Open resource |
| Get a related visualization | PNG | Thumbnail | Open resource |
| Google Scholar search results | HTML | Search results for publications that cite this dataset by its DOI. | Open resource |
| Download this dataset through Earthdata Search | HTML | Browse granule search results in Earthdata Search | Open resource |
| View documentation related to this dataset | HTML | OPERA Mission Information Page | Open resource |
| View this dataset's product usage | HTML | OPERA DSWx Product Suite Information Page | Open resource |