The Fine Resolution Explorer for Salinity, Carbon and Hydrology (FRESCH) is presented. The science case and the mission objectives are discussed before presenting the mission concept. FRESCH is an L-band antenna array operated in beamforming mode providing data at a spatial resolution of 10-15 km to study the biogeochemical and physical phenomena taking place at the interfaces of ocean, land and ice. FRESCH has been submitted to the European Space Agency Earth Explorer 12 program.

The impact of the initial spatial resolution of soil moisture maps on the quality of downscaled maps by merging with a higher resolution dataset was addressed. Soil moisture maps acquired with airborne sensors in four different campaigns in different climate regions with resolutions of 500 m to 1 km were aggregated to 4-5 km, 8-10 km, 18-20 km and 36-40 km before applying a downscaling algorithm to compute 1 km maps. These maps were compared to the original maps at 1 km resolution. Using different quality metrics, it is shown that the downscaled maps are 30%-75% more accurate when the initial resolution is in the range of 5-10 km with respect to initial resolutions of 36-40 km.

This paper reports on the main results of SMOS-HR instrument technical study, a new interferometric radiometer dedicated to Soil Moisture and Ocean Salinity measurement from space. The instrument aims at providing enhanced spatial resolution with respect to SMOS and integrating robust RFI (RF Interference) mitigation technique. The instrument key performance requirements and the architecture features and trade-offs are exposed in this paper.

Recent applications of passive L-band observations from space are summarized for ocean, land surface and cryosphere applications. The main limitation of the measurements performed by the current generation of sensors is the spatial resolution. The need of a mission ensuring the continuation of L-band measurements from space with high spatial resolution (10-15 km) is discussed.

We propose a methodology combining a change detection approach with a neural network algorithm to monitor soil moisture. The methodology utilizes Sentinel-1 and Sentinel-2 data, incorporating various metrics such as radar signals (VV and VH polarization), surface soil moisture index (I_SSM), radar incidence angle, normalized difference vegetation index (NDVI), and VH/VV ratio. In situ data from the International Soil Moisture Network (ISMN) across diverse climatic contexts are used for testing. The results demonstrate improved soil moisture estimations using the hybrid algorithms.

The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m3/m3vs. 0.041 m3/m3, 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on SMOS and SMAP surface soil moisture (SSM) data. This paper demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated area at the seasonal scale and at a 25 km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5-10%). More importantly, the amplitude of the second SSM peak is significantly correlated to the rice cover fraction within 25×25 km2pixels (R=0.81 for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information.

Digital Beam Forming (DBF) and Synthetic Aperture Inter-ferometry (SAI) are signal processing techniques that mixes the signals collected by an antenna array to produce high resolution images. This study aims at comparing these two approaches with the aid of simulations conducted at microwaves frequencies within the frame of the Soil Moisture and Ocean Salinity (SMOS) mission which is providing for more than a decade systematic passive L-band measurements from space. Although the two techniques are using the same signals and sharing the same goal, there are few differences that deserve attention. This is the case of the reconstruction floor error whose level and angular signature are significantly lower with the DBF paradigm than with the SAI one.

International audience

Remote sensing observations of variables related to vegetation at microwave and optical/infrared wavelengths are presented over three regions in Europe in the Iberian peninsula, northern Finland and central Europe. They include the instrumented sites of Las Majadas, Sodankyla and Reusel. The final goal is to better constrain land carbon cycle models using the complementarities of vegetation optical depth derived at different frequencies from active and passive instruments (related to vegetation water content and biomass) as well as optical data of the fraction of absorbed photosynthetically active radiation or solar induced fluorescence, closely linked to photosynthesis. The first results confirm this complementarity. For instance, time series of different variables exhibit positive correlations in some areas and negative correlations in other areas.

International audience

Above ground biomass (AGB) maps were estimated directly from microwave brightness temperatures (TB) using a machine learning approach. The accuracy of AGB retrievals from Artificial Neural Networks (ANN) is explored using both a multi-angular (using TBs products from the SMOS mission) and multi-frequency approach (using multi frequency measurements from the AMSR-E mission), an additional ANN inversion including optical indexes (MODIS-NDVI) is also discussed. Higher incidence angles have been shown to provide more information during the inversion process for AGB estimates than lower angles. Retrievals from the multi-angular lower-frequency inversion (SMOS - 1.4GHz) performed better than any individual higher-frequency retrievals. The addition of multi-frequency TBs (AMSR-E) to lower-frequency multi-angular TBs improves the performance of ANN models (from$\mathrm{R}^{2}\approx 0.94$to 0.96). Adding MODIS-NDVI to the inversion process improves the performance in an additional ~0.05%.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420 - 1427 MHz) measurements from space with a spatial resolution of ~40 Km. Preliminary results of studies conducted within the frame of a High Resolution (HR) follow-on mission are presented. The SMOS-HR project has undergone a Phase 0 study by the French space agency. The aim of this contribution is to improve the spatial resolution capabilities of SMOS-HR from ~10 Km to ~4 Km with the aid of a swarm of nano-satellites orbiting close to the connected array selected for SMOS-HR. After SMOS and SMOS-HR, this third generation named SMOS-NEXT will be the very first one to perform aperture synthesis from space with both connected and unconnected interferometric measurements.

The last decade has witnessed a series of extreme droughts across the globe. The impacts of these droughts have been devastating for the ecosystem and human activities. In this paper we present the assessment of the drought events in the last decade from the remote sensing-based root zone soil moisture anomalies. The root zone soil moisture is obtained from the SMOS surface soil moisture. And the drought index is defined as the monthly anomaly of the root zone soil moisture. Our results show the distribution of droughts over the last decade in various regions across the globe.

Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the opacity of the vegetation layer. This study focuses on the behaviour of L-band VOD (L-VOD) retrieval algorithm over seasonally inundated areas, as previous observations have shown an unexpected decline in VOD during floods. The signal emitted by a mixed scene composed of soil and standing water was simulated, leading to an overestimation of the retrieved soil moisture (SM) and an underestimation of the retrieved L- VOD, typically by ~ 1 0% over flooded forests and up to 100% over flooded grasslands. We evaluated the induced underestimation of aboveground biomass (AGB) by 15/20 Mg ha-1 in the largest seasonal wetlands, which can represent more than 50% of the actual AGB of the savanna wetland, and up to higher values during exceptional years. Surface water seasonality needs to be taken into account in passive microwave retrieval algorithms to better estimate the global biomass.

Constructing long time records of soil moisture (SM) requires the merging of data derived from different instruments while insuring the removing of the bias from different sensors time series. For instance, the ESA Climate Change Initiative (CCI) for SM currently uses the GLDAS v2.1 model as the reference to re-scale active and passive microwave time series. This paper discusses the possibility to use data from an L-band sensor as the reference in order to remove model dependency. AMSR-2 SM time series were re-scaled using different SMAP and SMOS datasets and evaluated against in-situ measurements. The results show that L-band data can be used to re-scale other sensor data with good performances. In addition, using the 11-years SMOS SM times series, the optimal length of the reference time series was studied.

A method to retrieve surface soil moisture (SM), at global scale, from L-Band telemetry of SMOS satellite using artificial Neural Networks (NNs) is presented. The NNs are trained using in-situ SM measurements as reference data, and SMOS Level-3 Temperature Brightness (TB) values and other auxiliary information, like MODIS NDVI, soil texture, and Skin Temperature from ECMWF as input. The retrieval is done in three steps. First multiple NN s, one per available in-situ site, are trained. Then a “representative” reference SM dataset is defined by examining the statistical relationships which link measurements from individual insitu SM sites and the input data. Finally, this representative reference SM data set is used to train an artificial NN using SMOS TBs and other inputs over the period 2011–2014. The resulting NN is in turn applied to 2017 SMOS TBs and others input data to retrieve SM at a global scale. The NN predicted SM is compared against SMOS Level 2 SM products as well as ECMWF forecast and is found to well capture the temporal and spatial variability of SM.

The Soil Moisture and Ocean Salinity (SMOS) satellite is performing systematic L-band observations since 2009, allowing a large number of science and operational applications. Several recent studies have shown the need of the continuity of L-band observations, in particular with an increased angular resolution. In this contribution, two instrumental concepts are presented to reach native resolutions of 5–10 km. In addition, using airborne data, it is also shown that the accuracy of downscaling coarser resolution L-band data to 5–10 km using a high resolution auxiliary data set, is significantly lower than that of native high resolution observations.

In this paper we present L-band data usage for Numerical Weather Prediction applications and Emergency Services at the European Centre for Medium-Range Weather Forecasts (ECMWF).

Launched in 2009, the SMOS satellite provides measurement of Soil Moisture (SM) and Vegetation Optical Depth (VOD) at L-band over land at high temporal resolution. L-VOD is known for being a good proxy for biomass, and 10 years of measurements are now available to analyze the behaviour of biomass over the whole world. Here, we investigate the links between L- VOD and SM seasonality, together with other vegetation and climatic variables (LAI, precipitation, temperature, and insolation). We also present time series of climatic variables for specific areas, showing remarkable trends of the L- VOD over the 10 year period (2010-2019), linked to climate change.

After almost 10 years in operation (SMOS- Aquarius - SMAP) the very high potential of L band radiometry is clearly demonstrated. Several applications are already operational (assimilation at ECMWF, for hurricanes, for sea ice etc.) so it is crucial to maintain such measurements. To do so while satisfying the current missions specifications is also of prime importance. Degrading spatial resolution is thus a significant step back which will impact science and applications). These missions are now getting older and the goal of the study presented in this paper is to assess which planned mission could fulfill the requirements to ensure data continuity. For this purpose, an extensive users' requirements study was performed in 2018-2019 assessing what would be required in the near future as well as when L band radiometry was absolutely necessary to satisfy the requirements. From the gathered results a cluster analysis was performed and the only.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) has been providing the longest consistent data record of passive L-band (1.4 GHz) observations for more than ten years. SMOS, as well as the NASA missions SMAP and Aquarius have demonstrated the interest of L-band observations for land, ocean and cryosphere studies. The continuity of L-band observations must be assured taking into account that the spatial resolution (~ 40 km) of SMOS and SMAP is too coarse for some applications. Disaggregation strategies can be implemented but using airborne data, we show that the quality of the downscaled data cannot match that of an instrument with higher native resolution. The goal of the SMOS-HR (High Resolution) mission is to ensure the continuity of L-band observations while increasing the native resolution to 10 km. SMOS-HR will carry an array of ~ 230 antennas to perform aperture synthesis. The antenna distribution has been optimized to reduce the aliasing in the reconstructed images and SMOS-HR will incorporate advanced on-board Radio Frequency Interferences (RFI) mitigation techniques.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420−1427 MHz) measurements from space with a spatial resolution of ~50 Km. This contribution presents preliminary results of studies conducted for a High Resolution (HR) follow-on mission. The SMOS-HR project is currently undergoing a Phase 0 study by the French space agency. The goal is to ensure continuity of L-band measurements while increasing the spatial resolution to ~10 Km without degrading the radiometric sensitivity and keeping the revisit time of 3 days unchanged.

The vegetation optical depth measured at L-Band (LVOD) by the SMOS satellite provides a high temporal resolution information of the vegetation water content that can be linked to the total above ground biomass (AGB). Nevertheless, its coarse spatial resolution (~40 km) can be limiting for a number of applications. This study is devoted to the downscaling of the SMOS LVOD using high spatial resolution L-Band backscatter data from ALOS1 synthetic aperture radar. The goal is to improve the spatial resolution of the LVOD to estimate AGB at 1 km.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 9 years. The whole data currently being reprocessed (Version 721 for levels 1 and 2 and version 4 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 9 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

Neural networks were used to estimate the ability of different sets of predictors to capture the variability of above ground biomass (AGB). SMOS brightness temperatures (TBs) for only two incidence angles capture 85% of the AGB variance. Adding soil moisture or L-band vegetation optical depth (L-VOD) increase the ability to capture the AGB variance to 90 % and 92 %, respectively. With respect to using only TBs, L-VOD improves the AGB estimation in regions of low vegetation.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, ...), ocean (salinity, meso-scale phenomena, river plumes, high winds, ...) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1-10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d'Etudes Spatiales).

Tropical aboveground carbon changes during 2010–2016 were estimated by a newly developed vegetation optical depth (VOD) product retrieved from the low-frequency L-band (1.4 GHz) passive microwave observations from the Soil Moisture and Ocean salinity (SMOS) satellite. The aboveground carbon changes estimated by VOD in the tropical region during 2010–2016 indicate the tropical region acts as a net carbon source of 111 Tg C yr-1 during 2010–2016. The declines in tropical aboveground carbon were found mainly in eastern America, African drylands and Indonesia.

The ongoing ESA funded `SMOS + Vegetation' project combines a retrieval component that aims at further improving the SMOS VOD product with an assimilation component that aims at demonstrating the added value of this product in constraining simulated land surface fluxes of carbon dioxide. This contribution focuses on the project's modelling and assimilation component. We describe the construction of dedicated observation operators that link the state of the terrestrial biosphere model to simulated VOD and surface layer soil moisture. We present our carbon assimilation system around a terrestrial biosphere model and demonstrate its operation through simultaneous assimilation of the SMOS VOD product over seven sites covering a range of plant functional types.

After almost 9 years in orbit L band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. In some cases it has demonstrated its uniqueness for assessing key environmental variables and in many others its high impact.

After almost 9 years in orbit L-band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. However, so as to cover specific applications use of other sensors can prove very valuable. In particular use of altimetry, optical and thermal infrared measurements can offer new avenues. Many of them were tested with existing satellites at the time of SMOS launch, but with the Copernicus' Sentinel-3 mission's data now available, new applications and operational products can be envisioned.

The European Space Agency's (ESA) SMOS mission, in orbit since more than 8 years, carries a passive microwave interferometric radiometer measuring in L-Band and provides accurate global observations of emitted radiation originating from the Earth's surfaces since the atmosphere is almost transparent in this spectral range. In addition, over land the effect of vegetation on the measurements is smaller than for shorter wavelengths. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on the characterisation of ice and snow covered surfaces and the sea ice effect on ocean-atmosphere heat fluxes and dynamics, which affects large-scale processes of the Earth's climate system.

A set of Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) data assimilation (DA) experiments are presented. The SMOS soil moisture dataset used in this study was produced training a neural network (NN) using SMOS brightness temperatures as input and ECMWF H-TESSEL SM fields as reference for the training. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. SMOS NN SM DA experiments were compared to Advanced Scat-terometer (ASCAT) SM DA. In both cases, experiments with and without 2 metre air temperature and relative humidity DA are discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives similar results to the model open loop with no assimilation. The effect of the soil moisture analysis on the Numerical Weather Prediction (NWP) was evaluated using the analysed surface fields to perform atmospheric forecast experiments. In the Northern Hemisphere both with ASCAT and SMOS, the experiments using 2m air temperature and relative humidity improve the forecast in April-September. SMOS alone has a significant positive effect in July-September. Maps of the forecast skill with respect to the open loop experiment show that SMOS improves the forecast in North America and to a lesser extent in Northern Asia for up to 72 hours.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new L-Band (1.4 GHz) VOD dataset (SMOS-IC L-VOD) obtained from the SMOS (Soil Moisture and Ocean Salinity) satellite. SMOS IC L-VOD is evaluated through a comparison with several vegetation-related quantities such as tree height and above ground biomass (AGB) for different land cover types. SMOS L-VOD shows monotonic relationships with respect to the variables extracted from these different datasets without signs of saturation at high values. The relationships between L-VOD and AGB were also compared to those obtained using the Normalized Difference Vegetation Index (NDVI) and K/X/C-VOD (VOD measured at 19, 10.7, and 6.9 GHz). In contrast to NDVI and K/X/C-VOD, L-VOD shows a relationship to AGB that is closer to a linear one without significant signs of saturation. SMOS L-VOD is a very promising dataset for large scale monitoring of biomass, at coarse scale spatial resolution (~ 40 km), but with high temporal resolution and with an improved sensitivity with respect to higher-frequency VOD data.

This paper presents the Soil Moisture and Ocean Salinity (SMOS) mission data assimilation activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyse soil moisture for Numerical Weather Prediction (NWP) applications. Two different approaches are presented based on SMOS brightness temperature and SMOS neural network soil moisture data assimilation, respectively. For the first approach, SMOS brightness temperature data assimilation relies on forward modelling. Long term results, spanning the SMOS period, of SMOS forward modelling, monitoring and data assimilation are presented. They emphasize the relevance of SMOS data for monitoring and to support NWP model developments. For the second approach, a SMOS soil moisture product has been produced based on a Neural Network (NN) trained on ECMWF soil moisture. So, 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 purpose. This approach, initially tested for 2012 in a global scale stand alone approach, shows that SMOS NN data assimilation slightly improves the two-metre air temperature forecast in the short range at regional scale. For NWP applications this approach has been further developed with a near real time production of the SMOS-ECMWF NN soil moisture product, with the implementation of the SMOS NN data assimilation in the ECMWF Integrated Forecasting System (IFS), and with high resolution (9km) global scale testing compatible with the current ECMWF NWP system.

In 2017, the new SMOS-IC retrieval product of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) was developed. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information and was found to be accurate, making it very well-suited for application in agriculture, hydrology, climate and vegetation monitoring. In this communication we present recent improvements in the SMOS-IC retrieval algorithm and recent applications using the soil moisture or VOD retrievals from the SMOS-IC data set. SMOS-IC SM is available at the French CATDS center.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 7 years. The whole data set has been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 7 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0-5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NNinSitu) is evaluated against in situ data not used during the training phase and against maps of the SMOS level 3 SM product and ECMWF SM models. NNinSituSM values are closer to ECMWF values for wet areas. A method to use NNs as a tool to classify in situ sites representative of the remote sensing observations scale is briefly discussed.

This paper discusses a methodology to construct a synthetic dataset using realistic geophysical data and the L-MEB model to compute synthetic brightness temperatures (Tb's) and to train a Neural Network (NN) for global retrievals of soil moisture (SM). The trained NNs are applied to real Tb's measured by the Soil Moisture and Ocean Salinity (SMOS) satellite (L-MEB NN). The objective is twofold. First, to compare and provide feedback to the operational algorithm. Second, to evaluate this approach in the context of pre-launch algorithm development. The performance of the L-MEB NN dataset was evaluated by comparing with time series of in situ measurements in North America. The correlation, standard deviation and bias of NN SM and in situ SM are similar to those obtained with the SMOS L3 SM product and with ECMWF models. The L-MEB NN dataset was also compared globally to the SMOS Level 3 SM and SM from ECMWF models. The L-MEB NN dataset is in general wetter than SMOS L3 SM, closer to ECMWF models. Some possible reasons are briefly discussed.

In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 — 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015–08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.

The Soil Moisture and Ocean Salinity mission has been collecting data for 6 years. The whole data set has just been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS). This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 6 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events.

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures,$T_{b} $'s) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using$T_{b} $'s measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated againstin situmeasurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.

In this communication we present an update on the RFI detection used in the SMOS processing chain and some elements on quantified impact of RFIs on level 2 soil moisture products. The level 2 soil moisture algorithms which included since the beginning a screening mechanism to reject contaminated brightness temperatures is now stricter. New approaches at the level 1 processors are also emerging and will be operational at their next release in 2014. Despite these strengthen procedures, RFIs are still impacting strongly SMOS observations and examples of quantified deterioration are given.

A methodology to retrieve soil moisture (SM) from multiinstrument remote sensing data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness temperatures), active microwaves (C-band ASCAT backscattering coefficients), and visible and infrared observations by MODIS. The reference SM data used to train the NN are ECMWF model predictions or SMOS L3 SM. After determining the best configuration of input data to retrieve SM using a NN, the NN soil moisture product is evaluated with respect to other global SM products and with respect to in situ measurements. The NN is able to capture the spatial and temporal dynamics of SM, and the SM computed with NNs compares well with the other SM datasets.