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Discussion papers | Copyright
https://doi.org/10.5194/hess-2017-680
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 Feb 2018

Research article | 08 Feb 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

Global Downscaling of Remotely-Sensed Soil Moisture using Neural Networks

Seyed Hamed Alemohammad1,2, Jana Kolassa3,4, Catherine Prigent1,2,5, Filipe Aires1,2,5, and Pierre Gentine1,2,6 Seyed Hamed Alemohammad et al.
  • 1Department of Earth and Environmental Engineering, Columbia University
  • 2Columbia Water Center, Columbia University
  • 3Universities Space Research Association, Columbia, MD
  • 4Global Modelling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD
  • 5Observatoire de Paris
  • 6Earth Institute, Columbia University

Abstract. Characterizing soil moisture at spatio-temporal scales relevant to land surface processes (i.e. of the order of a kilometer) is necessary in order to quantify its role in regional feedbacks between 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 days repeat time); however, their finest spatial resolution is 9km. NASA's Soil Moisture Active Passive (SMAP) satellite estimates soil moisture at two different spatial scales of 36km and 9km since April 2015. In this study, we develop a neural networks-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25km spatial resolution. Our approach uses mean monthly Normalized Differenced Vegetation Index (NDVI) as an ancillary data to quantify sub-pixel 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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