The SMOS (Soil Moisture and Ocean Salinity) satellite was launched in November 2009. This ESA led mission is dedicated to provide soil moisture over continental surface (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. These geophysical features are important as they control the energy balance between the surface and the atmosphere. Their knowledge at a global scale is of interest for hydrology and water cycle, climatic and weather researches, and in particular in improving forecasts. However, thanks to its high quality and angular measurements SMOS data can also be used to infer vegetation water content. The SMAP (Soil Moisture Active Passive) satellite was launched in January 2015. This NASA mission has similar characteristics to those of SMOS once the radar failed shortly after launch.
Both SMOS and SMAP now deliver systematic passive L-band (1.4 GHz) measurements from space with a spatial resolution of ~ 40 km. giving -for the first time with SMOS- access to accurate and absolute soil moisture fields. It has been demonstrated that when using the same retrieval algorithm for SMOS and SMAP the resulting soil moisture fields at 9 km spatial sampling were quasi identical. It is then possible to merge the two data sets and obtain – in almost near real time - daily observations of soil moisture over most of the globe opening new paths for applications.
Actually, amongst the various applications of surface soil moisture ranging from assimilation in NWP to flood forecasting or rainfall estimates over land, agriculture benefits from accurate estimates of both soil moisture and vegetation water content through the vegetation optical depth (VOD). The first use is to derive root zone soil moisture and then thanks to the now increasing long term data set –more than 9 years – efficient drought indices. Applications in agriculture concern also yield forecasting and uses in food security programs. using disaggregation techniques, it is also possible to infer soil moisture at much finer scales (typically several hundred meters) and use the resulting information for irrigation monitoring or even phytosanitary risks assessment.
During the presentation we will present the two data sets (SMOS and SMAP) and how they are merged into one together with the different application results obtained. We will also present several option for ensuring the continuity of such measurements.

As defined, the primary scientific objectives of the satellite salinity missions such as the ESA/SMOS and NASA/Aquarius missions were to improve seasonal to inter-annual [ENSO] climate predictions, ocean Rainfall Estimates and Global Hydrologic budgets as well as to monitore the Large Scale Salinity Events associated to climate change.
In this presentation, we shall review how the satellite SSS missions such as SMOS, Aquarius and SMAP helped better monitoring SSS relationships with climate variability (ENSO,Indian Ocean Dipole, Maden Julian Oscillation,etc.) since end 2009. In particular, we shall present how these missions help filling significant SSS observing system gaps by resolving spatio-temporal scales and regions not/inadequately sampled by in-situ platforms. In addition, Ocean model salinity are affected by:
•significant Evaporation-Precipitation forcing error;
•use of river discharge climatology;
•relaxation to SSS climatology;
•errors in model physics (e.g., advection & mixing) & numerics
Satellite SSS have the potential alleviate these limitations. We will review how assimilation of these data into ocean circulation model help improving the forecast of ocean state (e.g. ENSO, MLD, ocean currents).
In that context, the ESA CCI-salinity project started mid 2018 and aims to intercalibrate SMOS, Aquarius and SMAP SSS data and to best merge the individual sensor data to produce the first consistent and merged longest time serie of global satellite SSS data.
It also aims at demonstrating the benefit of such satellite-based time series for climate research and application. Specifically, the project investigates how useful are recent remotely sensed SSS spatio-temporal coverage versus long-term (~100 years) but coarse spatial resolution in situ reconstructions for improving climate predictions.
We will present the first results of the CCI-salinity project and the planned analyses of specific science case studies. We will show that systematic monitoring of SSS features from space with smaller scales and shorter periods than what the global in situ systems can currently sample are important for cross-scale interactions, that more uniform spatiotemporal sampling improves the ability to estimate horizontal SSS gradients which is of key importance for frontal genesis, eddy-mean flow interaction, and biogeochemistry and that global coverage is important for studying teleconnections & land-sea linkages.

The rapidly warming Arctic with the accompanying retreat and thinning of sea ice fortifies the need for better ocean-ice monitoring and forecast capabilities. One outstanding key parameter is the sea-ice thickness with great importance for diagnostics and predictions. To determine seasonal and interannual variations of the sea-ice thickness has been the main objective of ESA's Earth Explorer mission CryoSat. ESA's second Earth Explorer mission SMOS provides data that can also be used to derive the thickness of sea ice although this was not its primary objective.
Both missions are highly complementary in terms of spatio-temporal sampling and their sensitivities with respect to different ice thickness regimes. How to make best use of the available and future satellite observations in various operational application domains is a valuable research goal. The optimal choice of a certain data product or assimilation approach depends on different requirements including the time scales under consideration.

In this presentation we review different application examples including

1) The assimilation of SMOS data in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Monitoring Forecasting Centre (ARC MFC) coupled ocean and sea ice forecasting system TOPAZ (Xie et al., 2016).

2) The synergistic use of SMOS and CryoSat2 data with an optimal interpolation method for the generation of a weekly Arctic sea-ice thickness product, named CS2SMOS (Ricker et al., 2017).

3) The assimilation of the merged CS2SMOS product in the operational ensemble forecasting system at the ECMWF and its impact on seasonal sea-ice forecasts.

4) The assimilatiion of the merged CS2SMOS product in the CMEMS TOPAZ system (Xie et al., 2018).

5) The way towards direct assimilation of SMOS brightness temperatures (Richter et al., 2018).



References:

Richter, F., Drusch, M., Kaleschke, L., Maaß, N., Tian-Kunze, X., and Mecklenburg, S.: Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models, The Cryosphere, 12, 921-933, https://doi.org/10.5194/tc-12-921-2018, 2018.

Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607-1623, https://doi.org/10.5194/tc-11-1607-2017, 2017.

Xie, J., Counillon, F., and Bertino, L.: Impact of assimilating a merged sea ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-101, accepted for The Cryosphere, 2018.

Xie, J., Counillon, F., Bertino, L., Tian-Kunze, X., and Kaleschke, L.: Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system, The Cryosphere, 10, 2745-2761, https://doi.org/10.5194/tc-10-2745-2016, 2016.

This paper presents an overview of the Soil Moisture and Ocean Salinity (SMOS) mission data assimilation activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF). SMOS brightness temperature data is used operationally in the ECMWF Integrated Forecasting System (IFS) for monitoring purposes. Long term monitoring based on observed and reanalysed forward SMOS brightness temperatures show a consistent improvement in the SMOS observations quality through the SMOS lifetime. In 2018, a new Neural Network (NN) SMOS soil moisture product was developed, which was trained on ECMWF soil moisture. The processor was implemented to produce near real time soil moisture from SMOS brightness temperature data. The SMOS-ECMWF NN soil moisture product captures the SMOS signal variability in time and space, while by design its climatology is consistent with that of the ECMWF soil moisture, which makes it suitable for data assimilation purposes. SMOS NN soil moisture data assimilation was implemented in the land surface analysis of the IFS cycle 46r1, which will be operational in 2019. SMOS data assimilation is shown to slightly improve root zone soil moisture and independent verification against aircraft humidity profiles also shows that SMOS data assimilation improves humidity in the northern hemisphere during summer. These results illustrate the value of the SMOS soil moisture observations for operational NWP. Perspectives for exploring the potential of SMOS data to improve fire and flood forecasting, in the context of ECMWF activities with the Copernicus Emergency Management Services (CEMS) will be discussed.

The vegetation attenuation effect on remotely sensed passive microwave radiometers is usually quantified as vegetation optical depth (VOD), proportionally linked to the vegetation water content (kg m−2). The higher the microwave frequency, the stronger the vegetation attenuation effect. Historical satellite passive microwave sensors such as AMSR-E and SSMI were all operating at relatively high frequencies (>6 GHz), which tends to suffer from saturation effects for dense forest canopies. Being the first mission operating at a low-frequency L-band (1.4 GHz), SMOS has continuously collected data across the globe since 2010. The SMOS L-VOD product has much less saturation effects and is thus able to more accurately estimate vegetation water content in both sparsely vegetated drylands and densely forested areas with large volumes of woody stems and branches. Here, we analyze the temporal dynamics at intra- and inter-annual scales in the retrieved L-VOD product and summarize the information gained on plant hydraulics and carbon balance.
Specifically, we attribute the intra-annual dynamics in L-VOD to variations in ecosystem-scale plant water storage and compare this seasonal trend with leaf area development (i.e., LAI). We find that seasonal variations in plant water storage are highly synchronous with leaf phenology for boreal and temperate forests, but asynchronous for tropical woodlands where the seasonal development of plant water storage lags behind leaf area by up to 180 days. Contrasting patterns of the time lag between plant water storage and terrestrial groundwater storage are also evident in these ecosystems. A comparison of the water cycle components in seasonally dry tropical woodlands reveals a buffering effect of plant water storage on the seasonal dynamics of water supply and demand.
Since plant water storage is directly determined by biomass-carbon stocks, we converted the annual L-VOD product to the biomass-carbon unit over sub-Saharan Africa through a calibration with existing static carbon maps. We analyzed the inter-annual trend during 2010-2016 and demonstrated that the SMOS L-VOD product does not saturate over densely vegetated areas. The overall net change in drylands (53% of the study area) was −0.05 petagrams of carbon per year (Pg C yr−1) associated with a drying trend, and a net change of −0.02 Pg C yr−1 was observed in humid areas. The trend reflects a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) and about half of the gross losses were observed to occur in drylands. These results highlight the importance of the carbon pool of dryland savannahs for the inter-annual variability of global carbon balance, despite its relatively low carbon stock per unit area.