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)

[
  "020:00"
]

{
  "fn": "Jay Christensen",
  "hasEmail": "mailto:christensen.jay@epa.gov"
}

[]

[
  "LSTM machine learning",
  "remote sensing",
  "sentinel time-series",
  "surface water storage"
]

[
  "020:000"
]

{
  "name": "U.S. EPA Office of Research and Development (ORD)",
  "subOrganizationOf": {
    "name": "U.S. Environmental Protection Agency",
    "subOrganizationOf": {
      "name": "U.S. Government"
    }
  }
}

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