Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product

• Published in: Journal of advances in modeling earth systems Vol. 11; no. 10; pp. 3106 - 3130
• Main Authors: Reichle, Rolf H, Liu, Qing, Koster, Randal D, Crow, Wade T, Lannoy, Gabrielle J M De, Kimball, John S, Ardizzone, Joseph V, Bosch, David, Colliander, Andreas, Cosh, Michael, Kolassa, Jana, Mahanama, Sarith P, Prueger, John, Starks, Patrick, Walker, Jeffrey P, McNairn, Heather
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center American Geophysical Union 01.10.2019; John Wiley & Sons, Inc; American Geophysical Union (AGU)
• Subjects: Algorithms; Bias; Brightness temperature; Catchment land surface model; Climate; Data assimilation; Earth Resources And Remote Sensing; Estimates; In situ measurement; Kalman filters; Land surface models; L‐band passive microwave; Precipitation; Radiometers; remote sensing; rhizosphere; Root zone; Runoff; satellite surveys; Soil; Soil analysis; Soil moisture; Soil Moisture Active Passive; soil surface layers; Soil surfaces; soil water content; soil water recharge; Stream discharge; Stream flow; Streamflow measurements; Surface radiation temperature; Uncertainty; Watersheds; Rmse
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2019MS001729

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 m(exp 3) m(exp -3) for surface and 0.026 m(exp 3) m(exp -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 m(exp 3) m(exp -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 (exp -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.