Articles | Volume 22, issue 10
https://doi.org/10.5194/hess-22-5341-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-5341-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global downscaling of remotely sensed soil moisture using neural networks
Seyed Hamed Alemohammad
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
Columbia Water Center, Columbia University, New York, NY, USA
Radiant Earth Foundation, Washington, DC, USA
Jana Kolassa
Universities Space Research Association, Columbia, MD, USA
Global Modelling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Catherine Prigent
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
Columbia Water Center, Columbia University, New York, NY, USA
Observatoire de Paris, 75014 Paris, France
Filipe Aires
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
Columbia Water Center, Columbia University, New York, NY, USA
Observatoire de Paris, 75014 Paris, France
Pierre Gentine
CORRESPONDING AUTHOR
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
Columbia Water Center, Columbia University, New York, NY, USA
Earth Institute, Columbia University, New York, NY, USA
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Discussed (preprint)
Latest update: 05 Dec 2024
Short summary
A new machine learning algorithm is developed to downscale satellite-based soil moisture estimates from their native spatial scale of 9 km to 2.25 km.
A new machine learning algorithm is developed to downscale satellite-based soil moisture...