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

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L.4_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. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less 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 root-mean-square error in Version 4 is 0.039 m(3)/m(3) for surface and 0.026 m(3)/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 m(3)/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) 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. Plain Language Summary Soil moisture is important because of its impact on the land surface water, energy, and nutrient cycles. The low-frequency 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 satellite observations into a numerical model of the land surface water and energy balance while considering the uncertainty of the observations and model estimates. The resulting L4_SM data product is publicly disseminated within 2.5 days from the time of observation and provides global estimates of surface and deeper-layer (or root zone) soil moisture at 3-hourly temporal and 9-km spatial resolution. This study presents an overview of recent updates in the L4_SM algorithm and an assessment of the quality of the resulting Version-4 soil moisture estimates. The algorithm updates reduce bias in L4_SM surface soil moisture and runoff estimates compared to previous versions while otherwise maintaining the product skill, which meets the accuracy requirement specified prior to SMAP's launch.