Version 4 of the SMAP level-4 soil moisture algorithm and data product

Authors: REICHLE, R. - Goddard Space Flight Center; LIU, Q - Goddard Space Flight Center; KOSTER, R. - Goddard Space Flight Center; Crow, Wade; DELANNOY, G. - Leuven University; KIMBALL, J. - University Of Montana; ARDIZZONE, J. - Goddard Space Flight Center; Bosch, David; COLLIANDER, A. - Jet Propulsion Laboratory; Cosh, Michael; KOLASSA - Goddard Space Flight Center; MAHANAMA, S.P. - Goddard Space Flight Center; MCNAIRN, H. - Agriculture And Agri-Food Canada; Prueger, John; Starks, Patrick; WALKER, J. - Monash University

Submitted to: Journal of Advances in Modeling Earth Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/15/2019
Publication Date: 9/2/2020
Citation: Reichle, R., Liu, Q., Koster, R., Crow, W.T., Delannoy, G., Kimball, J., Ardizzone, J., Bosch, D.D., Colliander, A., Cosh, M.H., Kolassa, Mahanama, S., McNairn, H., Prueger, J.H., Starks, P.J., Walker, J. 2020. Version 4 of the SMAP level-4 soil moisture algorithm and data product. Journal of Advances in Modeling Earth Systems. 11:3106-3130. https://doi.org/10.1029/2019MS001729.
DOI: https://doi.org/10.1029/2019MS001729

Interpretive Summary: Soil moisture is important because of its impact on the land surface water, energy, and nutrient cycles. Furthermore, it represents a key target variable for efforts to monitor agricultural drought. Microwave observations collected by the NASA Soil Moisture Active Passive (SMAP) satellite are suitable for estimating soil moisture globally. Their sensitivity, however, is limited to the top few centimeters of the soil, and observations are only available every other day depending on location. The SMAP Level-4 Soil Moisture (L4_SM) data product addresses these limitations by merging the SMAP observations into a numerical model of the land surface water and energy balance while considering the uncertainty of the observations and model estimates. In doing so, it produces an enhanced representation of soil moisture dynamics. This study presents an overview of recent updates in the L4_SM algorithm and an assessment of soil moisture estimates produced following these updates. The updated version of the L4_SM algorithm described (and evaluated) here will eventually be used by the USDA and others to globally monitor the extent, duration and severity of agricultural drought.

Technical Abstract: The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially-distributed ensemble Kalman filter. Version 4 of the L4_SM modeling system includes a reduction in the upward recharge of surface soil moisture from below under non-equilibrium conditions, resulting in reduced bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias-adjusted RMSE in Version 4 is 0.039 m3 m-3 for surface and 0.026 m3 m 3 for root-zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near-global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m3 m-3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm year 1) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.