Global downscaling of remotely sensed soil moisture using neural networks

Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) 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 mana...

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Bibliographic Details
Published in:Hydrology and earth system sciences 2018-10, Vol.22 (10), p.5341-5356
Main Authors: Alemohammad, Seyed Hamed, Kolassa, Jana, Prigent, Catherine, Aires, Filipe, Gentine, Pierre
Format: Article
Language:English
Subjects:
Agricultural management
Analysis
Astrophysics
Atmospheric boundary layer
Boundary layers
Estimates
Evaluation
Heterogeneity
Missions
Neural networks
Observations
Physics
Remote sensing
Resolution
Satellites
Soil
Soil management
Soil moisture
Soil research
Spatial resolution
Technology application
Vegetation index
Weather forecasting
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Summary:Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) 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 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km 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 9 km soil moisture estimates.
ISSN:1607-7938
1027-5606
1607-7938
DOI:10.5194/hess-22-5341-2018