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Linked remote sensing and Long Short-Term Memory (LSTM) models reveal how surface water storage dynamics influence river discharge
Linked remote sensing and Long Short-Term Memory (LSTM) models reveal how surface water storage dynamics influence river discharge. This dataset is not publicly accessible because: It belongs to external collaborators. It can be accessed through the following means: The DOI for the final data release will be: (https://doi.org/10.5066/P14WYWSY). Format: The data will be housed at USGS's sciencebase.gov with an FGDC metadata .xml file as well as a csv file have been included along with model results for both remote sensing at the SWAT model subbasins. A link should be included on ScienceHub that will direct users to USGS's sciencebase. The DOI for the final data release will be: (https://doi.org/10.5066/P14WYWSY)
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Jay Christensen",
"hasEmail": "mailto:christensen.jay@epa.gov"
}
|
| description | Linked remote sensing and Long Short-Term Memory (LSTM) models reveal how surface water storage dynamics influence river discharge. This dataset is not publicly accessible because: It belongs to external collaborators. It can be accessed through the following means: The DOI for the final data release will be: (https://doi.org/10.5066/P14WYWSY). Format: The data will be housed at USGS's sciencebase.gov with an FGDC metadata .xml file as well as a csv file have been included along with model results for both remote sensing at the SWAT model subbasins. A link should be included on ScienceHub that will direct users to USGS's sciencebase. The DOI for the final data release will be: (https://doi.org/10.5066/P14WYWSY) |
| distribution |
[]
|
| identifier | https://doi.org/10.23719/1532080 |
| keyword |
[
"LSTM machine learning",
"remote sensing",
"sentinel time-series",
"surface water storage"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html |
| modified | 2025-03-11 |
| programCode |
[
"020:000"
]
|
| publisher |
{
"name": "U.S. EPA Office of Research and Development (ORD)",
"subOrganizationOf": {
"name": "U.S. Environmental Protection Agency",
"subOrganizationOf": {
"name": "U.S. Government"
}
}
}
|
| references |
null
|
| rights |
null
|
| title | Linked remote sensing and Long Short-Term Memory (LSTM) models reveal how surface water storage dynamics influence river discharge |
