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Anthropogenic climate change is now considered to be one of the main factors causing an increase in both the frequency and severity of wildfires. These fires are prone to release substantial quantities of CO2 into the atmosphere and to endanger natural ecosystems and biodiversity. Depending on the ecosystem and climate regime, fires have distinct triggering factors and impacts. To better analyse this phenomenon, we investigated post-fire vegetation anomalies over different biomes, from 2012 to 2020. The study was performed using several remotely sensed quantities ranging from visible–infrared vegetation indices (the enhanced vegetation index (EVI)) to vegetation opacities obtained at several passive-microwave wavelengths (X-band, C-band, and L-band vegetation optical depth (X-VOD, C-VOD, and L-VOD)), ranging from 2 to 20 cm. It was found that C- and X-VOD are mostly sensitive to fire impact on low-vegetation areas (grass and shrublands) or on tree leaves, while L-VOD depicts the fire impact on tree trunks and branches better. As a consequence, L-VOD is probably a better way of assessing fire impact on biomass. The study shows that L-VOD can be used to monitor fire-affected areas as well as post-fire recovery, especially over densely vegetated areas.

The aim of this presentation is to report on recent advances concerning the satellite based soil moisture validation done through the ESA project “Fiducial Reference Measurement for Soil Moisture (FRM4SM)”. The main objective of this two years project (May 2021 - May 2023) is to study the means to inform on the confidence in soil moisture data products for the whole duration of a satellite mission. Composed of three international partners (AWST, CESBIO and TU WIEN), it aims at the identification and creation of standards for independent, fully characterized, accurate and traceable (i.e., fiducial) in situ soil moisture reference measurements with corresponding independent validation methods and uncertainty estimations for a satellite mission. The ground reference data is drawn from the International Soil Moisture Network (ISMN). New quality indicators are created to better characterize the aptness of ISMN measurements for satellite soil moisture validation, and protocols provided to identify a select set of fiducial reference data. The satellite part, in charge of independent validation methods, focuses efforts towards the Soil Moisture Ocean Salinity (SMOS) mission from ESA. Finally, the easy-to-use interface for the comparison of satellite soil moisture data against land surface models and in situ data, the Quality Assurance for Soil Moisture (QA4SM), targets to implement all created FRM protocols from ground measurement to validation methods created within the FRM4SM project.

To this day, in situ soil moisture data is viewed as ground truth by the satellite soil moisture (SSM) community. In general, little is still commonly known regarding the traceability of ground measurement uncertainty and their overall in uncertainty budget, which can impact satellite SSM product quality assessments.Within ESA’s “Fiducial Reference Measurement for Soil Moisture (FRM4SM, May 2021 - May 2023)” project, objectives are set towards building fully characterized and traceable (i.e., fiducial) in situ measurements following community-agreed guidelines from the GEOS/CEOS Quality Assurance for Soil Moisture (QA4EO) framework. These so called “fiducial reference data” (FRM) should have associated Quality Indicators (QI) attached to evaluate their fitness for purpose building upon agreed reference standards (SI if possible). Moreover, such data should be easily and openly accessible, validation case studies should demonstrate their utility and reliability, and protocols and procedures should be established for the usage of such FRM datasets to make scientific studies intercomparable and reproducible.As part of the FRM4SM project, the following questions were addressed using the International Soil Moisture Network (ISMN) as a ground reference database and the Soil Moisture and Ocean Salinity (SMOS) mission as an example satellite product:(1) What makes “fiducial reference data” fiducial?(2) Is the creation of a globally-representative FRM subset already feasible for SSM?(3) What are the current limitations of in situ observations that limit fiduciality?(4) What is needed to create a full traceability chain from in situ point measurements to the satellite footprint scale?In this presentation, we will discuss these questions in detail and report on related findings of the FRM4SM project.

Passive microwave observations at different frequencies suffer extinction effects of the different vegetation components (branches, leaves, trunk) across the canopy of the soil’s microwave emission. These effects are often represented as a frequency-dependent variable called the Vegetation Optical Depth (VOD), which has been used (recently) to estimate Above-Ground Biomass (AGB). Low frequency observations, more particularly at L-band (1.4 GHz), have been shown to be sensitive to the woody components of plants (and thus to AGB), hence the growing interest in their use to monitor carbon stocks evolution.In this study, and thanks to the multi-angle capabilities of the SMOS mission, a new approach to estimate AGB maps directly from multi-angular passive L-band Brightness temperatures (TBs) is proposed, thus surpassing the dependence on intermediate variables like the VOD. Biomass estimates are produced from Artificial Neural Networks (ANN), using as reference the three AGB maps of the Climate Change Initiative (CCI) for the years 2010, 2017 and 2018; the SMOS multi-angle TBs for the same years were selected as inputs. The best set of predictors for ANNs and the optimal learning data-set configuration to estimate AGB are proposed based on a sensitivity analysis; the use of TBs in both Vertical and Horizontal polarization, plus a polarization ratio provided the closest biomass estimates to the reference AGB maps.ANNs trained from a purely data-driven approach explained 76% of AGB variability globally (incidence angles >35º showed high synergies with AGB); a hybrid approach (coupling ANN with variables derived from physically based models) slightly increased this value (+3%). However, when the trained models are applied to datasets from years different than those used during the training stage, a decrease in retrieval’s quality was observed; a new training scheme based on multi-year training sets is presented, results showed more stability from this kind of training schemes for temporal analyses.Finally, ANN- and VOD-based estimates were compared with respect to different AGB reference maps, the former outperformed the latter in all evaluation metrics. VOD-based inversions tend to underestimate AGB due to their quick saturation (around 200 Mg/ha) on densely forested regions. Additionally, a strong simplification of the spatial variations of AGB was observed; maps produced from this methodology present abrupt transitions between densely and sparsely vegetated areas, a characteristic that was not observed in the reference maps. When using VOD-derived maps these limitations should be considered, especially when employing them to study the temporal evolution of carbon stocks. The ANN methodology here proposed proves to be a promising technique for the estimation of global AGB maps, with robust results both in the spatial representation and in the temporal reproduction of AGB maps.

Quality assessment is an integral part of creating climate data records. Producers of satellite based records want to evaluate whether their products fulfill certain quality requirements, such as the ones set by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO) or by the Committee on Earth Observation Satellites (CEOS). Users of these data, on the other hand, are usually interested in their fitness-for-purpose in terms of specific applications, temporal/spatial subsets, and how different data sets of the same variable compare to each other.Quality Assurance for Soil Moisture (QA4SM) is an online validation service for (inter)comparing soil moisture records and assessing their quality, incorporating best practices, in a standardized, traceable way via an easy-to-use graphical user interface. The processing chain includes automatic preprocessing (filtering, temporal/spatial matching, scaling) of input data and computation of a set of quality metrics (e.g., correlation, bias, signal-to-noise-ratio). It provides an open and flexible framework in which users can upload their own data for comparison to state-of-the-art records that are already integrated in the service. These include reference data from the International Soil Moisture Network (ISMN), reanalysis data from ERA5 and GLDAS Noah, and various satellite based records such as SMOS, SMAP, Sentinel-1, ESA CCI, and C3S. In this presentation we give insight into the scientific and technical background of developing a cloud-based validation service and its current capabilities. We explain the advantages a service like this has, and how it can benefit users of climate data records with minimal effort.The service was launched as part of the Quality Assurance for High Spatial and Temporal Resolution Soil Moisture Data (QA4SM-HR) project through the Austrian Research Promotion Agency (FFG) and is currently developed within the framework of the European Space Agency’s Fiducial Reference Measurement for Soil Moisture (FRM4SM) project. It can be accessed at: https://qa4sm.eu

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) and European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 $\text{m}^{3}/\text{m}^{3}$ target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band (wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top $\sim 10$ -cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

We address the optimization problem of antenna placement on satellite-mounted interferometric synthetic-aperture instruments. In classic designs, the antennas on satellites are aligned regularly on the satellite’s frame. Inspired by methods proposed for the placement of antennas in astronomical interferometers, such as ALMA or SKA, we explore irregular layouts and show that they are a valid alternative in terms of spatial resolution and reconstruction error. We formalize mathematically the optimization problem of irregularly placed antennas and we show that this kind of arrays can still be calibrated with the same methods used for regular arrays. Finally, this strategy is evaluated in the context of soil moisture and ocean salinity (SMOS) follow-up concepts, such as SMOS-HR (high resolution), for which the new optimized irregular configurations are compared to the regular ones.

International audience; The northern land biosphere is believed to be the main global sink of CO2, but the contribution of Europe is uncertain. While bottom-up estimates and inverse atmospheric transport studies based on atmospheric CO2 observed in situ or from space by OCO-2 point to a moderate rate of uptake, some other inversions based on remotely sensed atmospheric CO2 from GOSAT/SCIAMACHY and biomass estimates from passive microwave satellite data point to a large sink of around 1 Gt C/yr. We present results from combining both approaches in a data assimilation framework, inverting a biosphere model against in situ atmospheric CO2 and passive microwave measurements. When assimilating all observations, we estimate a European carbon sink of 0.303 +/- 0.083 Gt C/yr for 2010-2015. The result agrees with other bottom-up studies and atmospheric inversions using in situ CO2 or OCO-2 observations pointing to potential data problems when using observations from GOSAT or SCIAMACHY to estimate the European CO2 sink.

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.

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.

The SMOS mission, launched in 2009, has been followed by Aquarius and SMAP, but follow-on missions are still in the preliminary phases. One of the main expected improvements is on the spatial resolution, for which a 10-fold increase is needed. Regardless of the choice on acquisition principle (real aperture or interferometry) such a massive improvement cannot be addressed with current technology. But interferometry has an advantage here in the sense that it can be distributed over multiple satellites. The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements. Such a massive improvement cannot be addressed with current technology and requires a major revisit of the acquisition of interferometric measurements. Of course, technological advances targeted by this mission concept is of far wider interest than L-band interferometry. The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements.

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.

After almost 12 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, food security, hurricanes, and natural risks, 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 now is to ascertain the achievements done during the last 10 years or so, and to prepare the next generation so as to ensure data continuity for these unique measurements.

International audience

Soil Moisture and Ocean Salinity (SMOS) mission has successfully contributed to global soil moisture products since 2009. Validation and calibration activities were conducted worldwide, yet some of the validation results do not fulfill the targeted accuracy of ±0.04 $\text{m}^{3}\text{m}^{-3}$ . This paper presented the site-specific calibration of the V620 retrieval algorithm with in situ data collected at selected agricultural sites in the humid tropical regions, Malaysia. This set of data has been validated where low accuracy of SMOS soil moisture products was found. To improve the SMOS soil moisture retrieval, calibration of SMOS soil moisture retrieval algorithm based on the L-band Microwave Emission and Biosphere model and SMOS Level 1C $\text{T}_{\mathrm {B}}$ products, considering the local parameters was conducted. The calibration proves that these site-specific parameters improve the product’s accuracy. Validation of SMOS Level 2 product with in situ data showed bias, root-mean-square error (RMSE), and unbiased RMSE (ubRMSE) ranging from 0.050 to 0.118 $\text{m}^{3}\text{m}^{-3}$ , 0.068 to 0.142 $\text{m}^{3}\text{m}^{-3}$ , and 0.069 to 0.103 $\text{m}^{3}\text{m}^{-3}$ , respectively. The soil moisture retrieval based on the calibrated model showed an improved bias of 0.020–0.056 $\text{m}^{3}\text {m}^{-3}$ and RMSE of 0.026–0.065 $\text{m}^{3}\text{m}^{-3}$ . The ubRMSE ranges from 0.017 to 0.034 $\text{m}^{3}\text{m}^{-3}$ . Recently released SMOS-IC V105 product was also validated, where small improvements were noticed when compared to the accuracy of SMOS Level 2. This paper shows the importance of local parameters in retrieving soil moisture with higher accuracy compared to the use of global generalized parameters that are used in the original SMOS soil moisture retrieval algorithm.

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products. ispartof: REMOTE SENSING OF ENVIRONMENT vol:224 pages:289-303 status: published

This contribution presents the main findings of a recently published review on high-resolution satellite soil moisture applications (https://doi.org/10.1016/j.rse.2020.112162). The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This presentation summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. This presentation also discusses the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of soil moisture remote sensing are discussed, providing guidance for the further development of operational soil moisture products and for bridging the gap between the soil moisture user and supplier communities.The published review is:Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M.H., Crow, W.T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M.W.J., de Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y.H., Lovergine, F., Mahecha, M.D., Marzahn, P., Mattia, F., Musial, J.P., Preuschmann, S., Reichle, R.H., Satalino, G., Silgram, M., van Bodegom, P.M., Verhoest, N.E.C., Wagner, W., Walker, J.P., Wegmüller, U., & Loew, A. (2021). A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements. Remote Sensing of Environment, 252, 112162

The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in 2009 by ESA, has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space with a spatial resolution of ~ 40 km. SMOS data are an essential component of the ESA Climate Change Initiative (CCI) for ocean salinity and soil moisture and they are used by the CCI biomass. A specific SMOS neural network soil moisture product is assimilated operationally at the European Centre for Medium Range Weather Forecasts (ECMWF). L-band surface SM measurements have also been used to estimate root zone soil moisture, to derive drought indices, to enable food security monitoring and to improve satellite precipitation estimates. SMOS data have also been used to detect frozen soils, thin ice-sheets over the ocean and ice melting in Antarctica and Greenland.Different studies on scientific and operational applications of L-band radiometry have shown the need of the continuity of L-band observations with an increased resolution with respect to the current generation of sensors. Resolutions from 1 km to 10 km would be a breakthrough for many applications over ocean, land and ice. One approach to obtain those resolutions could be downscaling coarse resolution data using an auxiliary dataset with higher resolution. However, using airborne data, we will show that the accuracy of the data downscaled to 1 km decreases significantly when the initial native resolution is 40 km with respect to downscaling from initial resolutions of 5-10 km. We will present two instrumental concepts to reach native resolutions of 5-10 km.The SMOS-HR mission project, completed the Phase 0 study at the French Centre National d’Etudes Spatiales (CNES) with contributions from Airbus Defence & Space and CESBIO. The goal was to ensure the continuity of L-band measurements while increasing the spatial resolution to ~10 km, which requires a typical antenna size of ~18 meters. Taking into account the difficulty of deploying a real aperture of this size in space and the successful alternative approach used by SMOS, SMOS-HR will perform aperture synthesis using an array of 230 small antennas distributed in a cross with four 12 m arms. During the Phase A study (ongoing at CNES) a mission concept with a central carrier surrounded by a swarm of nanosatellites will also be studied.

Merging data from different instruments is required to construct long time data records of soil moisture (SM). This is the goal of projects such as the ESA Climate Change Initiative (CCI) for SM (Gruber et al., 2019), which uses both active and passive microwave sensors. Currently, the GLDAS v2.1 model is used as reference to re-scale active and passive time series by matching their Cumulative Density Function (CDF) to that of the model. Removing the dependency on models is important, in particular for data assimilation applications into hydrological or climate models, and it has been proposed (Van der Schalie et al., 2018) to use L-band data from one of the two instruments specifically designed to measure SM, ESA Soil Moisture and Ocean Salinity (SMOS) and NASA Soil Moisture Active Passive (SMAP) satellites, as reference to re-scale other time series.To investigate this approach, AMSR-2 SM time series obtained from C1-, C2- and X-band observations using LPRM (Land Parameter Retrieval Model) were re-scaled by CDF-matching (Brocca et al., 2011) using different SMAP and SMOS official (SMAP L2 V005, SMOS L3 V300, SMOS NRT V100&V200) and research (SMOS IC V103) SM products as well as the SMAP and SMOS LPRM v6 SM data used by the ESA CCI. The time series re-scaled using L-band remote sensing data were compared to those re-scaled using GLDAS and were evaluated against in situ measurements at several hundred sites retrieved from the International Soil Moisture Network (Dorigo et al., 2011). The results were analyzed as a function of the land cover class and the Koppen-Geiger climate classification.Overall, AMSR-2 time series re-scaled using SMAP L2, SMAP LPRM and SMOS IC data sets as reference gave the best correlations with respect to in situ measurements, similar to those obtained by the time series re-scaled using GLDAS and slightly better than those of the original AMSR-2 time series. These results imply that different SMAP and SMOS products could actually be used to replace GLDAS as reference for the re-scaling of other sensors time series within the ESA CCI. However, one must bear in mind that this study is limited to the re-scaling of AMSR-2 data at a few hundred sites.For a more detailed assessment of the L-band data set to be used for a global re-scaling, it is necessary to investigate other effects such as the spatial coverage or the time series length. SMAP spatial coverage is better than that of SMOS in regions affected by radio frequency interference. In contrast, the length of SMAP time series can be too short to capture the long term SM variability for climate applications in some regions. The CDF of SMOS time series computed from the date of SMAP launch is significantly different to those of the full length SMOS time series in some regions of the Globe. Possible ways of using a coherent SMAP/SMOS L-band data set will be discussed.

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multibeam Radiometer (PPMR) at 740 MHz to evaluate the response of the P-band radiometer over a realistic range of surface conditions at the field scale. A preliminary framework of P-band Microwave Emission of the Biosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarised PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

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

Predicting the course of an epidemic is difficult, predicting the course of a pandemic from an emerging virus even more so. The validity of most predictive models relies on numerous parameters, involving biological and social characteristics often unknown or highly uncertain. COVID-19 pandemic brings additional factors such as population density and movements, behaviours, quality of the health system. Data from the COVID-19 epidemics in China, Japan and South Korea were used to build up data-driven deterministic models. Epidemics occurring in selected European countries rapidly evolved to overtake most Chinese provinces, to overtake South Korean model for France and even Hubei in the case of Italy and Spain. This approach was applied to other European countries and provides relevant information to inform disease control decision-making.

ESA’s SMOS mission is celebrating 10 years of measurements in 2020 and is still producing soil moisture data of interest for many applications. One of the successes of this mission is its unexpected applications of soil moisture, such as thin ice sheets over the ocean, above ground biomass and carbon stocks, crop yields or rainfall estimation. We believe that knowledge of soil moisture time series contains information that are closely related to the functioning of the hydrosphere (infiltration, evaporation, groundwater recharge) and the biosphere (vegetation development, crop yield, carbon storage). These two compartments are traditionally studied using models forced by precipitation rates and atmospheric variables. However, beyond the difficulty of measuring the precipitation rate accurately from space, a non-negligible portion of rain does not infiltrate the soil either because it is intercepted by vegetation or because of the surface runoff.In this study, we assume that SMOS retrieved soil moisture dynamics (0-5 cm) can inform us on much deeper soil horizons. Given that the water that reaches the root zone (0-200cm) and groundwater necessarily transits at some point through the surface, we can hypothesize that surface soil moisture dynamics intrinsically contains information on water dynamics in deeper layers.To test this idea, we used Richards' 1D model and forced the first layer of the model with 5-cm in-situ soil moisture measurements from the AMMA-CATCH observatory sites in West-Africa. A variation of soil moisture at the surface generates moisture variations in the deeper layers according to the hydrodynamic parameters of the model: soil conductivity at saturation (Ks), shape parameters of the retention curve (α and m), soil porosity (θsat). For highly permeable soils, water rapidly infiltrates the soil column and creates a groundwater table with its seasonal dynamics. For more impermeable soils, water remains close to the surface and there is no groundwater recharge. This approach satisfyingly compares with in-situ measurements concerning both root zone soil moisture profiles and water table dynamics.In a second step, the proposed methodology was applied to measurements derived from the SMOS satellite over the whole of Africa. To substitute in situ measurements, the GRACE satellite gravity data is used to compare with simulated soil water variations. This comparison allows to reject a lot of hydrodynamic parameters, and to select the best combination of the 4 parameters. Finally, the method makes it possible to produce maps of water table depths and their temporal dynamics at the scale of the African continent from information on surface soil moisture from SMOS (0-5cm) and soil water content from GRACE satellite.

Downscaling of L-Band microwave using Sentinel-3 land surface temperatureA large number of agricultural and water management applications require sub-kilometric frequent revisit surface Soil Moisture (SM) observations. L-band passive radiometer acquisitions are especially suited for soil moisture retrieval since they are less susceptible to attenuation by vegetation than active methods and are less sensitive to surface roughness than C or X – bands. However, while providing a 3 days global coverage for ascending and descending orbits with the currently available missions (SMOS/SMAP) the spatial resolution of the space-borne L-band radiometers is of ~40 km. Downscaling technics have been extensively used to increase the resolution of the SM products by combining data from optical (Merlin et al. 2012) and SAR sensors (Tomer et al. 2015). Here, we use land surface temperature data from the Sentinel-3 sensors to disaggregate the SMOS SM product into the DISPATCH algorithm. DISPATCH is based on the link between the evaporative efficiency and the SM (Merlin et al. 2010). The exercices is applied over Italy and compared to in-situ SM observations and model outputs over two sites in Northern and southrn Italy (Chiese and Capitanata). The algorithm is run using MODIS and the Sentinel-3 data for a comparative results. The potential of the combined use of Sentienl-3/MODIS and SMOS/SMAP is also investigate. The current study extends the application of an existing algorithm to new operational data from the Copernicus programe while accessing the advantages and ceavates.

Numerous surface soil moisture (SSM) products are available from remote sensing, ranging different spatial and temporal resolutions. Varying techniques are employed to retrieve SSM and different spatial scales highlight different distributions. Notwithstanding this variety between the available data, all of them should be coherent with the recorded rainfall and irrigation.In this work we have crossed recorded precipitations with a number of SSM products deriving from remote sensing: Soil Moisture Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, European Space Agency Climate Change Initiative (ESA-CCI) products, Copernicus Global Land Operations product, a Neural Network SSM retrieval algorithm and AMSR-2 data.All the dataset products have been compared with recorded precipitation from on-ground stations over two agricultural sites in Italy: one in the north, near Lake Garda (Chiese Irrigation Consortium) and the other in the south-east in the Apulia region (Capitanata Irrigation Consortium).In both cases, a first SSM-rain comparison through well-established indexes (Pearson and Spearman correlations) has not yielded encouraging results.Then, a methodology has been developed to determine whether the variation of SSM is consistent with the presence/absence of precipitation. An Agreement Index (AI) has been derived as a way to measure the coherency between SSM and precipitation. Any time a measure of SSM is available, a positive or negative value for the AI is recorded, according to the rainfall registered since the previous measurement. During the irrigation season (March through September), the presence of this artificial input of water into the system is also taken into account. For every year, the proportion between “coherent” SSM-rainfall pairings (positive AIs) and “non-coherent” pairings (negative AIs) has been computed.This method is applied to all SSM products in the dataset, and results are compared. When aggregating the results for all the pixels within the irrigation consortia, all seem to align to a similar proportion between “coherent” and “non-coherent” SSM-rainfall pairings, notwithstanding the wide variety of data types, spatial resolutions and retrieval methods. However, even if the overall performances of the products are similar, each shows different spatial distributions, as each product is influenced differently by the physical features of the different areas.

L-band passive microwave remote sensing is currently considered a robust technique for global monitoring of soil moisture. However, soil roughness complicates the relationship between brightness temperature and soil moisture, with current soil moisture retrieval algorithms typically assuming a constant roughness parameter globally, leading to a potential degradation in retrieval accuracy. This current investigation established a tower-based experiment site in Victoria, Australia. P-band (~40-cm wavelength/0.75 GHz) was compared with L-band (~21-cm wavelength/1.41 GHz) over random and periodic soil surfaces to determine if there is an improvement in brightness temperature simulation and soil moisture retrieval accuracy for bare soil conditions, due to reduced roughness impact when using a longer wavelength. The results showed that P-band was less impacted by random and periodic roughness than L-band, evidenced by more comparable statistics across different roughness conditions. The roughness effect from smooth surfaces (e.g., 0.8-cm root-mean-square height and 11.1-cm correlation length) could be potentially ignored at both P- and L-band with satisfactory simulation and retrieval performance. However, for rougher soil (e.g., 1.6-cm root-mean-square height and 6.8-cm correlation length), the roughness impact needed to be accounted for at both P- and L-band, with P-band observations showing less impact than L-band. Moreover, a sinusoidal soil surface with 10-cm amplitude and 80-cm period substantially impacted the brightness temperature simulation and soil moisture retrieval at both P- and L-band, which could not be fully accounted for using the SMOS and SMAP default roughness parameters. However, when retrieving roughness parameters along with soil moisture, the ubRMSE at P-band over periodic soil was improved to a similar level (0.01‐0.02 m3/m3) as that of smooth flat soil (0.01 m3/m3), while L-band showed higher ubRMSE over the periodic soil (0.03‐0.04 m3/m3) than over smooth flat soil (0.01 m3/m3). Accordingly, periodic roughness effects were reduced by using observations at P-band.

This paper investigated the sensitivity of passive microwave L-band soil moisture (SM) retrieval from multiangle airborne brightness temperature data obtained under morning and afternoon conditions from the National Airborne Field Experiment conducted in southeast Australia in 2006. Ground measurements at a dryland focus farm including soil texture, soil temperature, and vegetation water content were used as ancillary data to drive the retrieval model. The derived SM was then in turn evaluated with the ground-measured near-surface SM patterns. The results of this paper show that the Soil Moisture and Ocean Salinity target accuracy of 0.04 $\text{m}^{3}\cdot \text{m}^{-3}$ for single-SM retrievals is achievable irrespective of the 6 A.M. and 6 P.M. overpass acquisition times for moisture conditions $\le 0.15~\text{m}^{3}\cdot \text{m}^{-3}$ . Additional tests on the use of the air temperature as proxy for the vegetation temperature also showed no preference for the acquisition time. The performance of multiparameter retrievals of SM and an additional parameter proved to be satisfactory for SM modeling—independent of the acquisition time—with root-mean-square errors less than 0.06 $\text{m}^{3}\cdot \text{m}^{-3}$ for the focus farm.

ElectroMagnetic (EM) reasons resulting in temperature dependence of L-band Vegetation Optical Depth (L-VOD) are currently overlooked in remote sensing products. Discrepancies in retrievals of geophysical surface properties over vegetated areas can result from this incompleteness. This perception motivated to explore EM considerations in how temperature drives L-VOD of a boreal forest. Thereto, a novel physics-based model is developed and evaluated to assess L-VOD sensitivities to canopy temperature and some other model parameters. The L-VOD model is compared to L-VOD derived from close-range L-band brightness temperatures measured through the tree canopy at the Finnish Meteorological Institute's Arctic Research Center (FMI-ARC) in Sodankyla (Finland) during a 4-week and a 1-day period in 2019. Furthermore, the model's ability to explain L-VOD retrieved from brightness temperatures of the “Soil Moisture and Ocean Salinity” (SMOS) satellite over the “Sodankyla grid cell” is investigated. Experimental L-VOD are maximal at around 0 °C and decrease when canopy temperature is moving away from zero degree Celsius. This temperature response, observed at different temporal- and spatial scales, is captured by the proposed L-VOD model and explained by freezing tree sap-water and the dependence of water permittivity on temperature. The demonstrated EM-induced temperature dependence suggest caution with interpreting satellite-based L-VOD, because increased L-VOD around the freezing point is not solely due to increased biomass or rehydration of the vegetation. Further, our study can find future application to compensate L-VOD for EM-induced temperature sensitivity. This potentially leads to improved explanatory power of temperature normalized L-VOD for characterization of forest phenology. Furthermore, we suggest examining the presence and strength of the demonstrated L-VOD temperature response as a practical L-VOD retrieval quality assessment method under steady forest phenology.

The basic observables of an imaging interferometer by aperture synthesis are the complex visibilities. Under some conditions, they can be simulated with the aid of the van Cittert–Zernike theorem. However, owing to underlying assumptions, some important effects that may alter them cannot be taken into account. This paper is devoted to the numerical simulation of complex visibilities without any reference to the van Cittert–Zernike theorem, in such a way that these effects can be taken into account. The emission from an extended source is modeled using a linear superposition of random waves emitted by a collection of point sources, which are all assumed to behave like black bodies at thermal equilibrium. These random waves are numerically generated with the aid of white noises filtered in such a way that their power spectral densities follow the shape of Planck distributions at the temperature of the point sources over a wide range of frequencies. The radio signal is then transported to the antennas, where the voltage patterns are taken into account as well as the filters response of the bandpass receivers. It is, therefore, sent to the correlator unit for being cross-correlated. From emission to correlation, perturbing effects can be introduced at any time. To illustrate this modeling method, numerical simulations are carried out in the L-band around 1413.5 MHz in reference to the SMOS- next project led by the French Space Agency. The results are discussed and compared with the estimates provided by the van Cittert–Zernike theorem. Owing to the amount of calculations to be performed, massive parallel architectures like that found in GPU have been required.

Global rainfall information is useful for many applications. However, real-time versions of satellite-based rainfall products are known to contain errors. Recent studies have demonstrated how the information about rainfall intrinsically contained in soil moisture data can be utilised for improving rainfall estimates. That is, soil moisture dynamics are impacted for several days by the accumulated amount of rainfall following within a particular event. In this context, soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used in this study to correct rainfall accumulation estimates provided by satellite-based real-time precipitation products such as CMORPH, TRMM-3B42RT or PERSIANN. An algorithm based on the SMOS measurements data assimilation is tested in two land-surface models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a more sophisticated state-of-the-art land-surface model (SURFEX ( Surface Externalisee )). We show how the assimilation technique, based on a particle filter method, generally leads to a significant improvement in rainfall estimates, with slightly better results for the simpler (and less computationally demanding) API model. This methodology has been evaluated for six years at ten sites around the world with different land use and climatological features. The results also show the limitations of the methodology in regions highly affected by mountainous terrain, forest or intense radio-frequency interference (RFI), which can notably affect the quality of the retrievals. The satisfactory results shown here invite the future operational application of the methodology in near-real time on a global scale.

International audience; L-band radiometer measurements collected over the Dome C area from 2010 to 2015 indicated that the brightness temperature (T B) was relatively stable at vertical (V) polarization (standard deviation lower than 1 K at annual scale), while it was slightly more variable at horizontal (H) polarization. During the 2014-2015 austral summer, an exceptional situation was recorded by both the DOMEX ground radiometer and the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite. From November 2014 to March 2015, T B H showed a progressive and significant increase until 20 March 2015 when it sharply decreased by about 5 K (at 52.5 o incidence angle) within a few days. In parallel to the increase in T B H, glaciological and meteorological in situ measurements showed a wind speed that was lower than usual and a low-density snow layer being progressively set up on the surface. This was consistent with the exceptional hoar event observed, as well as with snow accumulation on the surface. On the other hand, the decrease in T B H was related to the passing over Dome C of a storm that removed or compacted the layer of light snow on the surface. The WALOMIS (Wave Approach for LOw-frequency MIcrowave emission in Snow) snow-emission model was used with in situ measurements of the snowpack as inputs for evaluating the effect of changes observed on the snow surface in T B H. The simulations indicated that the surface snow density variations were sufficient for predicting the increasing and decreasing trends of the T B H. However, the thickness variations of the superficial layer were essential so as to obtain a better agreement with the SMOS observations. This result confirmed that the L-band T B H was affected by the snow properties of the top centimeters of the snowpack, in spite of the large penetration depth (hundreds of meters). Both the surface snow density and the thickness of the superficial layer were relevant, due to coherent interference effects.

Roughness effect parameterization is critical to accurately simulate brightness temperature (Tb) signals observed by a radiometer over bare soil surface. However, current roughness parameterization schemes usually suffer from severe error, which dominates the error budget in current Tb modeling over bare soil surface. In this study, uncertainty of soil roughness parameterization schemes is comprehensively assessed using data set collected during 2004 to 2006 at the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) bare soil experimental site. To reduce uncertainty from sampling depth mismatch, the soil moisture profile with a 1 cm thickness from a calibrated Hydus-1D (H1D) model is utilized to determine the optimal soil moisture inputs to soil emission model. Uncertainties of 15 literature-based roughness effect parameterization schemes developed for L-band Tb modeling are inter-compared. The “Q/H” model is further calibrated against multi-angle and dual-polarization Tb observations at the SMOSREX bare soil site under different roughness conditions. Our results show that: (1) soil moisture sampling depth varies with soil moisture content and roughness condition. When soil is drier and rougher, the soil moisture sampling depth gets deeper. (2) The 15 roughness schemes generally perform better at vertical polarization than at horizontal polarization and better when soil surface is relative smooth than when soil surface gets rougher. The 15 roughness correction schemes have their own advantages and disadvantages with diverse error and bias characteristics. None of them has a superior performance at all conditions in terms of roughness, polarizations and incident angles. (3) A non-zero Q configuration is preferred in parameter retrieval experiments and the observed linear relationship between ΔN and root-mean-square height (σ) or σ2/LC can only be reproduced when Q is non-zero in parameter retrieval. (4) The effective roughness parameters (Q, Np and h) generally increase when soil get rougher. The calibrated Q, Nh and Nv show exponential dependence on the effective parameter h. The calibrated h still shows dependence on surface soil moisture after accounting the impact from soil sampling depth and also shows strong power-law dependence on Tb at incident angle of 40°. The non-zero-Q fitting models have comparable performance in Tb modeling with zero-Q models but may be more physically realistic.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is devoted to the monitoring of soil moisture and ocean salinity at a global scale from L -band space-borne radiometric observations obtained with a two-dimensional interferometer. SMOS was launched in November 2, 2009, and is still collecting the interferometric data used to retrieve the brightness temperature of the Earth surface. Several external sources of brightness within the field of view of SMOS contaminate these measurements and their contributions should be removed prior to image reconstruction. One of these sources is the direct solar radiations when the sun is either below the antenna plane array (i.e., in front of the satellite) and seen by the front-lobes antenna patterns or above it (i.e., in the back of the satellite) and seen by the back-lobes antenna patterns. In the case of SMOS, the direct solar radiations are accounted for only when the sun is in front of the satellite. In the second case, their impact is considered negligible. In this paper, we will show evidence of non-negligible direct solar radiations in the retrieved temperatures when the sun is in the back of SMOS. We will also show that the sun correction algorithm implemented in the SMOS level 1 operational processor can be extended efficiently to remove such radiations.

This paper evaluates the performance of different soil moisture products from AMSR2 and SMOS against the most representative stations within the Yanco study area in the Murrumbidgee catchment, in southeast Australia. AMSR2 Level 3 (L3) soil moisture products retrieved from two versions of brightness temperatures using the Japanese Aerospace eXploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithm were included. For the LPRM algorithm, two different parameterization methods were applied. Furthermore, two versions of SMOS L3 soil moisture product were assessed. The results are contrasted against the use of “random” stations. Accounting for all versions, frequencies and overpasses, the latest versions of the JAXA (JX2) and LPRM (LP3) products were found to surpass the earlier versions (JX1, LP1 and LP2). Soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used but not X-band. However, X-band retrievals (r: 0.71, MAE: 0.07, RMSD: 0.08 m 3 /m 3 ) were found to perform better than C-band (r: 0.68–0.70, MAE: 0.07–0.09 m 3 /m 3 , RMSD: 0.09–0.10 m 3 /m 3 ). Moreover, an intercomparison between different acquisition times (morning and evening) of AMSR2 X-band products found a better performance from evening overpasses (1:30 pm; r: 0.69–0.77) as opposed to morning overpasses (1:30 am; r: 0.47–0.66). In the case of SMOS, morning (6:00 am; r: 0.77) retrievals were found to be superior over evening (6:00 pm; r: 0.69) retrievals. Overall, both versions of JAXA products, the second and third versions of LPRM X-band products, and two versions of SMOS products were found to meet the mean average error (MAE) goal accuracy of the AMSR2 mission (MAE 3 /m 3 ) but none of the products achieved the SMOS goal of RMSD 3 /m 3 . Furthermore, performance of the products differed depending on the statistic used to evaluate them. Consequently, considering the results in this study, JX2 products are recommended if both absolute and temporal accuracy of the soil moisture product is of importance, whereas LP3 X products from evening observations and SMOS version 3.00 (SMOS2) products from morning overpasses are recommended if temporal accuracy is of greater importance.

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d’études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013 – 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R > 0.80) over the Oznet sites and worst (R = 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R= 0.51 and R= 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

International audience; The remote signatures measured at microwave frequency above land surfaces are strongly dependent on the permittivity of the soil, which is linked to its moisture content. Thus, soil permittivity is a key parameter when algorithms are developed for the retrieval of hydrologic parameters from remote sensing data. Soil permittivity measurements are generally carried out in the laboratory because in-situ measurements are more difficult to obtain. The study presents the development of two probes (N and SMA probes) for in situ soil permittivity measurements (i.e. measurements of dielectric properties). They are based on the end effect phenomenon of a coaxial waveguide and so are called end effect probes in this paper. Results obtained on well-known materials (water and polytetrafluoroethene) are compared with corresponding data obtained by laboratory approaches (Von Hippel’s method and resonant cavity) and show good agreement from 0.5 GHz up to ∼3.5 GHz and 6 GHz for N and SMA probes respectively. Then measurements made on concrete and mineral soil are reported to underline the efficiency of end effect probes for in-situ dielectric measurements. Finally, through work undertaken in the framework of the European Space Agency’s SMOSHiLat project, we demonstrate the applicability of the two probes for measurements performed within these frequency ranges in complex material such as organic soil horizons.

Melt occurrence in Antarctica is derived from L-band observations from the Soil Moisture and Ocean Salinity (SMOS) satellite between the austral summer 2010/11 and 2017/18. The detection algorithm is adapted from a threshold method previously developed for 19 GHz passive microwave measurements from Special Sensor Microwave Imagers (SSM/I, SSMIS). The comparison of daily melt occurrence retrieved from 1.4 GHz and 19 GHz observations shows an overall close agreement, but a lag of few days is usually observed by SMOS at the beginning of the melt season. To understand the difference, we performed a theoretical analysis using a microwave emission radiative transfer model that shows that the sensitivity of 1.4 GHz signal to liquid water is significantly weaker than at 19 GHz if the water is only present in the uppermost tens of centimeters of the snowpack. Conversely, 1.4 GHz measurements are sensitive to water when spread over at least 1 m and when present at depth, up to hundreds of meters. This is explained by the large penetration depth in dry snow and by the long wavelength (21 cm). We conclude that SMOS and higher frequency radiometers provide interesting complementary information on melt occurrence and on the location of the water in the snowpack.

The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by Radio Frequency Interference (RFI) that jeopardizes part of its scientific retrieval in certain areas of the world. Areas affected by RFI experience data loss or underestimation of soil moisture and ocean salinity retrieval values. Close to the 10th anniversary of SMOS launch, this paper provides an overview of SMOS RFI activities during these years, the evolution of the RFI scenario worldwide and the lessons learned. The main challenges faced in the RFI detection and geo-location will be introduced, with some interesting examples of RFI cases detected. Another topic addressed in this paper is the impact of the RFI contamination from a scientific perspective, with some estimations of the data percentage that has to be discarded due to interference.

Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal. In this study, we developed a short-latency (i.e., 2–3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration of satellite SM observations with in-situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long latency datasets. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are close to each other but SMAP observations show a warmer TB bias (about 0.64 K: V pol and 1.14 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures (SMOS-SMAP) were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. The SMOS-SMAP soil moisture retrievals compared with in situ observations show a retrieval accuracy of less than 0.04 m3/m3. Results from the development and validation of the integrated soil moisture product will be presented.

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).

Since 2009, the SMOS mission has been acquiring brightness temperature measurements to derive soil moisture and ocean salinity. With a revisit time of three days everywhere on the globe, it has become one of the first mission to provide global assessment of these two parameters in near real time. The only instrument carried by SMOS satellite is a two dimensional radiometric interferometer, operating between 1400 and 1420 MHz. Despite this being a protected band, it has been obvious since day one that man-made emissions, either close and powerful or directly in-band, were contaminating the measurements. This was foreseen to some extent and some filtering algorithms had been designed prior to the launch. Although quite efficient, the very high diversity of RFI source characteristics made it very difficult to identify reliably all contaminations. Thus, since then, it has been a constant effort to try to identify better all these sources and assess their impact on SMOS measurements.

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.