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| Type: | Resource | |
| Storage: | The size of this resource is 4.0 MB | |
| Created: | Mar 16, 2024 at 4:55 p.m. | |
| Last updated: | May 21, 2024 at 4:41 p.m. | |
| DOI: | 10.4211/hs.9d60389f55b648149a788a2ff7bc3766 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 340 |
| Downloads: | 59 |
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Abstract
Advances in data availability, Earth observation technologies, and geospatial sciences have transformed our ability to map Global Surface Water Extents (GSWE). However, traditional GSWE mapping has been limited to static estimates, with more recent efforts focusing on annual averages and temporal attributes like frequency and occurrence of long-term variations. We harnessed remotely sensed Sentinel-2 based near real-time Dynamic World land cover product to produce the first public, routinely available 10-meter resolution global surface water datasets. Our key contribution is an Open Science operational framework to rapidly extract the latest available Dynamic World products every 2-5 days, run geospatial analytics, and create actionable water information for educators, researchers, and stakeholders at any scale of practical interest.
This dataset has been developed by the Hydrology & Hydroinformatics Innovation Lab at the University of Texas at Arlington, United States.
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.txt
Use the Data_Directory_Map.pdf for detail description of the folder contents.
Refer to the following reference for metadata:
Khare, A., Gupta, B.C., Rajib, A., Vanderhoof, M., and Wu, Q. (in review). Estimates of Global Surface Water Dynamics Harnessing Near Real-time Land Cover Observations and Open Science Geospatial Capabilities. Nature Scientific Data.
Related Resources
| The content of this resource is derived from | Brown, C.F., Brumby, S.P., Guzder-Williams, B., Birch, T., Hyde, S.B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Nature Scientific Data 9. https://doi.org/10.1038/s41597-022-01307-4 (2022). |
| The content of this resource references | Rajib, A., Khare, A., Golden, H.E., Gupta, B.C., Wu, Q., Lane, C.R., Christensen, J., Zheng, Q., Dahl, T.A., Ryder, J.L. and McFall, B.C. A call for consistency and integration in global surface water estimates. Environmental Research Letters, 19(2). https://doi.org/10.1088/1748-9326/ad1722 (2024). |
| The content of this resource references | Pickens, A.H., Hansen, M.C., Hancher, M., Stehman, S.V., Tyukavina, A., Potapov, P., Marroquin, B. and Sherani, Z. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sensing of Environment, 243. https://doi.org/10.1016/j.rse.2020.111792 (2020). |
| The content of this resource references | Pekel, JF., Cottam, A., Gorelick, N. et al. High-resolution mapping of global surface water and its long-term changes. Nature 540. https://doi.org/10.1038/nature20584 (2016). |
| This resource is described by | Khare, A., Gupta, B.C., Rajib, A., Vanderhoof, M., and Wu, Q. Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities. Nature Scientific Data. (in review) |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Science Foundation | CyberTraining for Open Science in Climate, Water, and Environmental Sustainability | 2336630 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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