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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 10 | Copyright
Hydrol. Earth Syst. Sci., 22, 5341-5356, 2018
https://doi.org/10.5194/hess-22-5341-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 17 Oct 2018

Research article | 17 Oct 2018

Global downscaling of remotely sensed soil moisture using neural networks

Seyed Hamed Alemohammad1,2,3, Jana Kolassa4,5, Catherine Prigent1,2,6, Filipe Aires1,2,6, and Pierre Gentine1,2,7 Seyed Hamed Alemohammad et al.
  • 1Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA
  • 2Columbia Water Center, Columbia University, New York, NY, USA
  • 3Radiant Earth Foundation, Washington, DC, USA
  • 4Universities Space Research Association, Columbia, MD, USA
  • 5Global Modelling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 6Observatoire de Paris, 75014 Paris, France
  • 7Earth Institute, Columbia University, New York, NY, USA

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9km soil moisture estimates.

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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...
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