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NASAs Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAPs radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core calval sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 cu m/cu m at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 cu m/cu m. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P soil moisture data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.

The landscape freeze/thaw (FT) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and Soil Moisture and Ocean Salinity (SMOS) L-band passive microwave measurements using a polarization ratio-based algorithm for landscape FT monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: tundra, boreal forest, and prairies. The reference data include in situ measurements of soil temperature (Tsoil) and air temperature (Tair), and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between Lband FT detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using Tair as the reference. Aquarius radiometer 2 (incidence angle of 39.6) data gives the best accuracies (90.8), while for SMOS the best results (87.8 of accuracy) are obtained at higher incidence angles (55- 60). The FT algorithm identifies both freeze onset and end with a delay of about one week in tundra and two weeks in forest and prairies, when compared to Tair. The analysis shows a stronger FT signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the polarization ratio) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar FT patterns over Canada. This study shows the potential of using L-band spaceborne observations for FT monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth.

The launch of the Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009 marked a milestone in remote sensing for it was the first time a radiometer capable of acquiring wide field of view images at every single snapshot, a unique feature of the synthetic aperture technique, made it to space. The technology behind such an achievement was developed, thanks to the effort of a community of researchers and engineers in different groups around the world. It was only because of their joint work that SMOS finally became a reality. The fact that the European Space Agency, together with CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnológico e Industrial), managed to get the project through should be considered a merit and a reward for that entire community. This paper is an invited historical review that, within a very limited number of pages, tries to provide insight into some of the developments which, one way or another, are imprinted in the name of SMOS.

The Soil Moisture and Ocean Salinity (SMOS) satellite mission is routinely providing global multi-angular observations of brightness temperature (TB) at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture (SM). To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multi-angular and multi-polarization top of atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity (VIC) LSM coupled with the Community Microwave Emission Modelling platform (CMEM) for simulating SMOS TB observations over the Upper Mississippi basin, USA. For a period of 2 years (2010-2011), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin averaged bias of 30K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After CDF matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30K to less than 5K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.