Global, 3-hourly, 9-km resolution soil moisture estimates are available with a mean latency of ~2.5 days from the NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product. These estimates are based on the assimilation of SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. Routine monitoring of the L4_SM system's assimilation diagnostics revealed occasionally large observation-minus-forecast Tb differences across eastern central Australia that resulted in large analysis increments (or adjustments) of the model forecast soil moisture. Because this region lacks in situ soil moisture measurements, we developed an alternative approach to assess the veracity of the soil moisture analysis increments in the L4_SM system. Using regional gauge-based precipitation data, we demonstrate that the L4_SM soil moisture increments are correlated with errors in the L4_SM precipitation forcing, suggesting that the SMAP Tb observations contribute valuable information to the L4_SM soil moisture estimates.

The Gravity Recovery and Climate Experiment (GRACE) mission provided monthly global estimates of the vertically integrated terrestrial water storage with about 300-400-km horizontal resolution between 2002 and 2017. Since 2009, the Soil Moisture and Ocean Salinity (SMOS) mission observes global L-band brightness temperatures, which are sensitive to near-surface soil moisture, with a revisit time of 1-3 days at a nominal 43-km spatial resolution. This work investigates if the multi-sensor assimilation of these observations into the Catchment land surface model can improve the estimation of 0-5 cm "surface" soil moisture, 0-100 cm "root-zone" soil moisture, and shallow (unconfined) groundwater levels. Single-sensor GRACE or SMOS assimilation and multi-sensor GRACE + SMOS assimilation experiments were performed over the continental U.S. for 6 years (July 2010 June 2016). GRACE data assimilation mostly improves estimates of shallow groundwater, whereas SMOS data assimilation mainly improves estimates of surface soil moisture. The benefits introduced by the single-sensor assimilation are merged in the multi-sensor assimilation experiment, suggesting that better and more consistent soil moisture and groundwater estimates can be achieved when multiple observation types are assimilated. Interestingly, in the multi-sensor GRACE + SMOS experiment, the water storage increments introduced by the GRACE analysis and the SMOS analysis are anti-correlated. That is, when the GRACE assimilation increments remove water from the overall profile storage, the SMOS assimilation increments add water to it, and vice versa. This anti-correlation could be caused by the SMOS analysis trying to undo the increments from the GRACE analysis.

Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in pre-storm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the NASA Soil Moisture Active Passive Level-4 soil moisture product in combination with streamflow and precipitation observations, we demonstrate that land surface models (LSMs) generally underestimate the strength of the positive rank correlation between pre-storm soil moisture and event runoff coefficients (i.e., the fraction of rainfall accumulation depth converted into stormflow runoff during a storm event). Underestimation is largest for LSMs employing an infiltration-excess approach for stormflow runoff generation. More accurate coupling strength is found in LSMs that explicitly represent sub-surface stormflow or saturation-excess runoff generation processes.

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m3m-3, 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m3m-3, 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

SMAP (Soil Moisture Active and Passive) radiometer observations at ~40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9-km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to the SMAP assimilation can increase the spatio-temporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3-hourly, 9-km surface and root-zone soil moisture simulations with in situ measurements from 9-km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel-1 assimilation consistently improved surface soil moisture, whereas root-zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performed best, demonstrating the complementary value of radar and radiometer observations.

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) "reference pixels'' at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m(3) m(-3) or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m(3) m(-3) (0.030 m(3) m(-3)) at the 9-km scale and 0.035 m(3) m(-3) (0.026 m(3) m(-3)) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m(3) m(-3) (0.032 m(3) m(-3)). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm‐scale runoff ratio (i.e., total streamflow divided by total rainfall accumulation in depth units) and prestorm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite‐based, L band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting streamflow response to future rainfall events.

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. These observations are sensitive to surface(0-5 cm) soil moisture. Several of the key applications targeted by SMAP, however, require knowledge of deeper-layer, root zone (0-100 cm) soil moisture, which is not directly measured by SMAP. The NASA Global Modeling and Assimilation Office (GMAO) contributes to SMAP by providing Level 4 data, including the Level 4 Surface and Root Zone Soil Moisture(L4_SM) product, which is based on the assimilation of SMAP Tb observations in the ensemble-based NASA GEOS-5 land surface data assimilation system. The L4_SM product offers global data every three hours at 9 km resolution, thereby interpolating and extrapolating the coarser- scale (40 km) SMAP observations in time and in space (both horizontally and vertically). Since October 31, 2015, beta-version L4_SM data have been available to the public from the National Snow and Ice Data Center for the period March 31, 2015, to near present, with a mean latency of approx. 2.5 days.