Towards a surface soil moisture product at high spatio-temporal resolution: temporally-interpolated spatially-disaggregated SMOS data

Towards a surface soil moisture product at high spatio-temporal resolution:... AbstractHigh spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed DisPATCh (DISaggregation based on Physical And Theoretical scale Change) algorithm provides 1-km resolution surface soil moisture by downscaling the 40-km SMOS (Soil moisture Ocean Salinity) soil moisture using MODIS (MODerate-resolution Imaging Spectroradiometer) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated analyzed soil moisture and ground measurement from 0.53 to 0.70, whereas the mean ubRMSE slightly decreased from 0.07 m3 m−3 to 0.06 m3 m−3 compared to the open-loop force-restore model. The proposed assimilation scheme has significant potential for large scale applications over semi arid areas, since the method is based on data available at global scale together with a parsimonious land surface model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Towards a surface soil moisture product at high spatio-temporal resolution: temporally-interpolated spatially-disaggregated SMOS data

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
D.O.I.
10.1175/JHM-D-16-0280.1
Publisher site
See Article on Publisher Site

Abstract

AbstractHigh spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed DisPATCh (DISaggregation based on Physical And Theoretical scale Change) algorithm provides 1-km resolution surface soil moisture by downscaling the 40-km SMOS (Soil moisture Ocean Salinity) soil moisture using MODIS (MODerate-resolution Imaging Spectroradiometer) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated analyzed soil moisture and ground measurement from 0.53 to 0.70, whereas the mean ubRMSE slightly decreased from 0.07 m3 m−3 to 0.06 m3 m−3 compared to the open-loop force-restore model. The proposed assimilation scheme has significant potential for large scale applications over semi arid areas, since the method is based on data available at global scale together with a parsimonious land surface model.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Dec 1, 2017

References

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