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Title: SMOS disaggregated soil moisture product at 1 km resolution: processor overview and first validation results

item MOLERO, B. - Collaborator
item MERLIN, O. - Collaborator
item MALBETEAU, Y. - Collaborator
item AL BITAR, A. - Collaborator
item CABOT, F. - Collaborator
item STEFAN, V. - Collaborator
item KERR, Y. - Collaborator
item BACON, S. - Collaborator
item Cosh, Michael
item BINDLISH, R. - Collaborator
item Jackson, Thomas

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2016
Publication Date: 7/1/2016
Citation: Molero, B., Merlin, O., Malbeteau, Y., Al Bitar, A., Cabot, F., Stefan, V., Kerr, Y., Bacon, S., Cosh, M.H., Bindlish, R., Jackson, T.J. 2016. SMOS disaggregated soil moisture product at 1 km resolution: processor overview and first validation results. Remote Sensing of Environment. 180:361-376. doi:10.1016/j.rse.2016.02.045

Interpretive Summary: A new methodology for disaggregating coarse spatial resolution soil moisture information provided by the Soil Moisture Ocean Salinity satellite was assessed. The approach utilizes high resolution soil temperature data. Performance was assessed by comparing the disaggregated and non-disaggregated SM (L3) datasets against the in situ measurements in both the spatial and temporal domains. Sites included study areas in the U.S. and Australia. The evaluation of this network brought to view that the algorithm needs to be improved to adapt to all types of soil. Future improvements should include more ancillary information. The methodology will be implemented for routine processing and use in hydrologic applications.

Technical Abstract: The SMOS (Soil Moisture and Ocean Salinity) mission provides surface soil moisture (SM) maps at a mean resolution of ~50 km. However, agricultural applications (irrigation, crop monitoring) and some hydrological applications (floods and modeling of small basins) require higher resolution SM information. In order to overcome this spatial mismatch, a disaggregation algorithm called Disaggregation based on Physical And Theoretical scale Change (DISPATCH) combines higher-resolution data from optical/thermal sensors with the SM retrieved from microwave sensors like SMOS, producing higher-resolution SM as the output. A DISPATCH-based processor has been implemented for the whole globe (emerged lands) in the Centre Aval de Traitement des Données SMOS (CATDS), the French data processing center for SMOS Level 3 products. This new CATDS Level-4 Disaggregation processor (C4DIS) generates SM maps at 1 km resolution. This paper provides an overview of the C4DIS architecture, algorithms and output products. Differences with the original DISPATCH prototype are explained and major processing parameters are presented. The C4DIS SM product is compared against L3 and in situ SM data during a one year period over the Murrumbidgee catchment and the Yanco area (Australia), and during a four and a half year period over the Little Washita and the Walnut Gulch watersheds (USA). The four validation areas represent highly contrasting climate regions with different landscape properties. According to this analysis, the C4DIS SM product improves the spatio-temporal correlation with in situ measurements in the semi-arid regions with substantial SM spatial variability mainly driven by precipitation and irrigation. In sub-humid regions like the Little Washita watershed, the performance of the algorithm is poor except for summer, as result of the weak moisture-evaporation coupling. Disaggregated products do not succeed to have and additional benefit in the Walnut Gulch watershed, which is also semi-arid but with well-drained soils that are likely to cancel the spatial contrast needed by DISPATCH. Although further validation studies are still needed to better assess the performance of DISPATCH in a range of surface and atmospheric conditions, the new C4DIS product is expected to provide satisfying results over regions having medium to high SM spatial variability.