Data assimilation to extract soil moisture information from SMAP observations

Authors: KOLASSA, J. - Goddard Space Flight Center; REICHLE, R. - Goddard Space Flight Center; LIU, Q. - Goddard Space Flight Center; Cosh, Michael; Bosch, David; CALDWELL, T. - University Of Texas; COLLIANDER, A. - Jet Propulsion Laboratory; Holifield Collins, Chandra; Jackson, Thomas; Livingston, Stanley; MOGHADDAM, M. - University Of Michigan; Starks, Patrick

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2017
Publication Date: 11/17/2017
Citation: Kolassa, J., Reichle, R., Liu, Q., Cosh, M.H., Bosch, D.D., Caldwell, T., Colliander, A., Holifield Collins, C.D., Jackson, T.J., Livingston, S.J., Moghaddam, M., Starks, P.J. 2017. Data assimilation to extract soil moisture information from SMAP observations. Remote Sensing. 9(11):1179. https://doi.org/10.3390/rs9111179.
DOI: https://doi.org/10.3390/rs9111179

Interpretive Summary: The Soil Moisture Active Passive (SMAP) Level 4 soil moisture product provides an estimate of soil moisture in the top meter of soil, which is more useful for agricultural science than the normal surface soil moisture product produced by SMAP (Level 2). This requires a soil moisture model to extrapolate soil moisture through the soil column with estimates of soil properties. But these models require a lot of land surface information and assumptions.One method of mediating all of these competing parameters is to assimilate new data into the existing model to provide a correction to the soil moisture model as it proceeds. This data assimilation was applied to the Level 4 product using a catchment model and satellite temperature brightness to correct the estimates through time. Improvements were observed, making the resulting Level 4 product more valuable for agricultural monitoring and prediction.

Technical Abstract: This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network(NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against SMAP core validation site (CVS) in situ measurements by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m3 m-3 and 0.001 m3 m-3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m-3, but increased the root zone bias by 0.014 m3 m-3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from uncertain satellite information.