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The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises. [ABSTRACT FROM AUTHOR]

In the near future, the SMOS (Soil Moisture and Ocean Salinity) mission will provide global maps of surface soil moisture (SM). The SMOS baseline payload is an L-band (1. 4 GHz) two dimensional interferometric microwave radiometer which will provide multi-angular and dual-polarization observations. In the framework of the ground segment activities for the SMOS mission an operational SMOS Level 2 Soil Moisture algorithm was developed. The principle of the algorithm is to exploit multi-angular data in order to retrieve simultaneously several surface parameters including soil moisture and vegetation characteristics. The algorithm uses an iterative approach, minimizing a cost function computed from the differences between measured and modelled brightness temperature (T B) data, for all available incidence angles. In the algorithm, the selected forward model is the so-called L-MEB (L-band Microwave Emission of the Biosphere) model which was the result of an extensive review of the current knowledge of the microwave emission of various land covers. This model is a key element in the SMOS L2 algorithm and could be used in future assimilation studies. There is thus a strong need for a reference study, describing the model and its implementation. In order to address these needs a detailed description of soil and vegetation modelling in L-MEB is given in this study. In a second step, the use of L-MEB in soil moisture retrievals is evaluated for several experimental data sets over agricultural crops. Calibrations of the soil and vegetation L-MEB parameters are investigated for corn, soybean and wheat. Over the different experiments, very consistent results are obtained for each vegetation type in terms of calibration and soil moisture retrievals. [Copyright &y& Elsevier]

Realistic parameterization of land surface processes must take into account heterogeneities in the land surface. In the case of a sparse canopy, interpretation of remotely sensed measurements is very difficult and somewhat questionable in attempts to relate the vegetation indices (VIs) to fractional vegetation cover information. This paper provides an intercomparison of satellite observations at different scales for the purpose of assessing and monitoring vegetation changes at a regional scale. It is designed (1) to evaluate the level of association that can be expected from a model relating basic tools such as spectrally derived VIs from AVHRR and green biomass data for a set of heterogeneous surfaces in a representative semi-arid region and (2) to determine the best strategy for using satellite imagery in that context. The quantitative relationships between radiation data collected in space and characteristics of land surfaces are investigated in the context of the HAPEX-Sahel study over the Niger. A north-south vegetation gradient was accurately located and documented. Corresponding SPOT data, acquired on the same day for the same test site, at 20m spatial resolution were then resampled to the plate carree projection for comparison with National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) HRPT data (1km spatial resolution). This processing helped in the description and full interpretation of the evolution of various vegetation indices derived from NOAA AVHRR data on these semi-arid regions. One outcome of the data processing is that the resulting relationship between spectral indices and the effective biomass is found to be nonlinear within our low biomass range. When this scheme is applied to NOAA AVHRR data, the Normalized Difference Vegetation Index (NDVI), the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Global Environment Monitoring Index (GEMI) appear to provide detailed information about biomass evolution. However, the accuracy is somewhat different depending on the fractional vegetation cover value. Strategies to estimate information on green biomass in semi-arid regions are different depending on the vegetation index used. In order to use the NDVI or MSAVI properly at the surface level, we have no choice but to perform carefully prepared atmospheric corrections. This data preprocessing is not necessary for the GEMI, which is computed without the need for any atmospheric corrections. [ABSTRACT FROM AUTHOR]

The monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters. [ABSTRACT FROM AUTHOR]

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 > 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. [ABSTRACT FROM AUTHOR]

Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m 3 /m 3 ), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m 3 /m 3 for AMSR-reg and 0.084 m 3 /m 3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations. [ABSTRACT FROM AUTHOR]

This research aims to test data obtained by level 2 retrieval algorithm of SMOS over land, in order to provide information regarding vegetation and soil moisture over forested areas. Results presented in this paper were obtained using the last 620 version of the algorithm. The correlation between the new vegetation optical depth (VOD) product and the height of the forest estimated by ICES at GLAS lidar on a global scale is investigated. Over South American and African forests a good correspondence between the two variables is observed, with saturation occurring above about 30 m height. Moreover, the comparison between the VOD and the height of the forest shows good spatial and temporal stability, and the r 2 correlation coefficient is within a 0.59–0.69 range. Conversely, discrepancies are observed in some Indonesian islands, particularly New Guinea. Over specific areas, the trends vs. forest height obtained with SMOS VOD are compared with the corresponding trends of AMSR-E VOD. Results are also validated at country-level scale. To this aim, accurate estimates of forest biomass derived from airborne lidar over selected forests of Peru, Columbia and Panama are used. Finally, the soil moisture retrieved over forests is investigated, reporting continental maps for Tropical areas and comparisons with ground measurements in selected forests of the US. Continental maps obtained with the new level 2 V620 algorithm cover almost all forest areas, and show seasonal variations which are dependent on climatic zones. Comparisons between soil moisture retrievals in forests and ground measurements of the US SCAN network produce worse RMSE values with respect to low vegetation areas. Significant improvements however are achieved after averaging among close nodes of the ground network. [ABSTRACT FROM AUTHOR]

In the Lake Chad basin, the quaternary phreatic aquifer (named hereafter QPA) presents large piezometric anomalies referred to as domes and depressions whose depths are ~15 and ~60 m, respectively. A previous study (Leblanc et al. in Geophys Res Lett, , doi:) noticed that brightness temperatures from METEOSAT infrared images of the Lake Chad basin are correlated with the QPA piezometry. Indeed, at the same latitude, domes are ~4-5 K warmer than the depressions. Leblanc et al. (Geophys Res Lett, , doi:) suggested that such a thermal behaviour results from an evapotranspiration excess above the piezometric depressions, an interpretation implicitly assuming that the QPA is separated from the other aquifers by the clay-rich Pliocene formation. Based on satellite visible images, here we find evidence of giant polygons, an observation that suggests instead a local vertical connectivity between the different aquifers. We developed a numerical water convective model giving an alternative explanation for the development of QPA depressions and domes. Beneath the depressions, a cold descending water convective current sucks down the overlying QPA, while, beneath the dome, a warm ascending current produces overpressure. Such a basin-wide circulation is consistent with the water geochemistry. We further propose that the thermal diurnal and evaporation/condensation cycles specific to the water ascending current explain why domes are warmer. We finally discuss the possible influence of the inferred convective circulation on the transient variations of the QPA reported from observations of piezometric levels and GRACE-based water mass change over the region. [ABSTRACT FROM AUTHOR]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by radio frequency interferences (RFIs) that jeopardize part of its scientific retrieval in certain areas of the world, particularly over continental areas in Europe, Southern Asia, and the Middle East. Areas affected by RFI might experience data loss or underestimation of soil moisture and ocean salinity retrieval values. To alleviate this situation, the SMOS team has put strategies in place that, one year after launch, have already improved the RFI situation in Europe where half of the sources have been successfully localized and switched off. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) satellite has opened the era of soil moisture products from passive L-band observations. In this paper, validation of SMOS products over continental U.S. is done by using the Soil Climate Analysis Network (SCAN)/SNOwpack TELemetry (SNOTEL) soil moisture monitoring stations. The SMOS operational products and the SMOS reprocessing products are both used and compared over year 2010. First, a direct node-to-site comparison is performed by taking advantage of the oversampling of the SMOS product grid. The comparison is performed over several adjacent nodes to site, and several representative couples of site-node are identified. The impact of forest fraction is shown through the analysis of different cases across the U.S. Also, the impact of water fraction is shown through two examples in Florida and in Utah close to Great Salt Lake. A radiometric aggregation approach based on the antenna footprint and spatial description is used. A global comparison of the SCAN/SNOTEL versus SMOS is made. Statistics show an underestimation of the soil moisture from SMOS compared to the SCAN/SNOTEL local measurements. The results suggest that SMOS meets the mission requirement of 0.04 m3/m3 over specific nominal cases, but differences are observed over many sites and need to be addressed. [ABSTRACT FROM AUTHOR]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission was launched on the 2nd of November 2009. The first six months after launch, the so-called commissioning phase, were dedicated to test the functionalities of the spacecraft, the instrument, and the ground segment including the data processors. This phase was successfully completed in May 2010, and SMOS has since been in the routine operations phase and providing data products to the science community for over a year. The performance of the instrument has been within specifications. A parallel processing chain has been providing brightness temperatures in near-real time to operational centers, e.g., the European Centre for Medium-Range Weather Forecasts. Data quality has been within specifications; however, radio-frequency interference (RFI) has been detected over large parts of Europe, China, Southern Asia, and the Middle East. Detecting and flagging contaminated observations remains a challenge as well as contacting national authorities to localize and eliminate RFI sources emitting in the protected band. The generation of Level 2 soil moisture and ocean salinity data is an ongoing activity with continuously improved processors. This article will summarize the mission status after one year of operations and present selected first results. [ABSTRACT FROM AUTHOR]

The potential of the LAS (large aperture scintillometry) method for measuring sensible heat flux (H) directly integrated over a two-field composite surface is evaluated. We describe a field experiment performed within the Alpilles/ReSeDa project in the south-east of France over a composite surface made up of wheat and bare soil (451 and 216 m long respectively) using two 0.15-m diameter scintillometers mounted at heights of 2.05 and 4.54 m. When compared against reference values obtained by the eddy correlation technique, LAS-measured sensible heat flux reveals a systematic overestimation of about 10%. A simple model describing the integration of the scintillometer signal along the beam for a two-field composite surface is described. A simulation of the experiment confirms that the bias observed is related to non-linearities in the integration process in relation with the bell-shape sensitivity curve of the instrument to the structure parameter for the refractive index it measures. The model is used to test the sensitivity of the LAS-derived H values to the composition of the pathlength (ratio of both surfaces) and to the contrast in sensible heat flux and roughness length between the two fields. Sensitivity tests to the aggregation scheme for roughness length (two of them are tested) and to the measurement height are also presented. The composition of the surface in combination with the contrast in sensible heat flux (in direct relation with the contrast in latent heat flux) explains most of the bias, with possible deviations ranging from -50 up to 80 W m-2. A tentative semi-empirical method for correcting the bias is suggested, which only requires a crude estimate of the contrast in component sensible heat fluxes along the pathlength. [ABSTRACT FROM AUTHOR]

The goal of our work was to demonstrate that the usual assumptions of isotropy and symmetry of the bidirectional reflectance factor (BRF) are not valid for a specific anisotropic ground surface. Two methods illustrating this point are presented. The first method is based on goniometer measurements, and the second method uses a Monte Carlo algorithm. The results of both methods were analysed at different wavelengths and angular configurations. For the surface studied here, they showed that the BRF was significantly asymmetric with respect to the principal plane. In the extreme case, the difference between the two sides of the principal plane reached almost 30% of the mean reflectance value. [ABSTRACT FROM AUTHOR]

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. Key Points: European carbon sink estimated to be 0.303 ± 0.083 GtC/yrConsistent assimilation of three observational data products into a terrestrial biosphere modelSMOS soil moisture and vegetation optical depth reduce flux uncertainty by an additional 15–25% compared to assimilating CO2 only [ABSTRACT FROM AUTHOR]

The assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB) data in numerical weather prediction systems influences the state of the soil, which in turn affects the exchange of energy and water fluxes between the soil and the near‐surface atmosphere, with potential implications for the prediction of atmospheric variables. In this paper, the impact of assimilating SMOS TB alone or in combination with screen‐level observations and Advanced Scatterometer (ASCAT) soil moisture retrievals is assessed. Independent quality controlled insitu soil moisture observations belonging to several networks included in the International Soil Moisture Network, were used to validate the quality of both the new soil moisture analyses and the skill to predict soil moisture up to 5 days ahead. The impact on atmospheric variables is indirect and was evaluated through computation of the forecast skill at different lead times. The analysis period was selected to be around the boreal summer, a period of the year when evaporatranspiration fluxes are stronger, and when it is therefore expected that the assimilation of remote‐sensing data provides the largest impact on the state of the soil. The results show that the soil moisture state benefits from the direct assimilation of SMOS TB, especially in better representing the temporal variations of soil moisture. The skill of atmospheric variables is mainly driven by the screen‐level observations. Despite the clear benefits to the soil state, remote‐sensing data need to be used with screen‐level variables to add value to the state of the atmosphere, pointing to inconsistencies in the physical coupling between the land and near‐surface components of the ECMWF Earth system. [ABSTRACT FROM AUTHOR]

This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (H R , and N Rp , p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and H R = 0.1, whereas values of ω = 0.06 − 0.08 and H R = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, H R and N Rp , p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, H R = 0.4 and N Rp = −1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and H R varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = −0.019 m 3 m −3 , ubRMSE = 0.062 m 3 m −3 for the IGBP-based calibration; against R = 0.54, bias = −0.034 m 3 m −3 and ubRMSE = 0.070 m 3 m −3 for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product. [ABSTRACT FROM AUTHOR]

L-band passive measurements (1.4 GHz) over continental areas are known to be related to surface soil moisture. Two satellite missions were recently launched to measure land surface emissions at this frequency band (SMOS-Soil Moisture and Ocean Salinity in 2009 and SMAP-Soil Moisture Active/Passive in 2015). In order to improve soil moisture retrievals from satellite data, ground-based radiometer systems operating at the same frequency were deployed over specified areas to investigate the L-band emission of various land covers under various climatological conditions. In this study, three years of L-band passive measurements from a radiometer installed on top of a steep mountain in the French Alps were analyzed and compared to L-band passive simulations. The innovative radiometer location led to large footprints due to the distance between the radiometer and the area under study. This experiment also produced microwave measurements affected by various potential difficulties typically encountered in SMOS/SMAP satellite missions: topography, heterogeneous footprints, dry/wet snow events, dew and vegetation litter. Based on in situ and modeling data, this paper investigates the potential of a radiative transfer model (L-band Microwave Emission of the Biosphere, L-MEB) to simulate L-band measurements and analyzes the differences with ELBARA observations. First, it was found that the topography generated a mixing of the horizontal and vertical polarizations. In addition, a large positive bias was found on ELBARA measurements (31 K and 12 K in horizontal and vertical polarizations respectively). Investigations showed that the sky reflection measured by the radiometer was partially substituted by land reflection coming from the surrounding topography. Second, the low-vegetation emission was investigated and highlighted the inability of the MODIS NDVI product to correctly represent the vegetation dynamics. Finally, dry snow conditions were found to have non-negligible impact at L-band and a particular signature was found during snow melting periods, with potential applications at the SMOS/SMAP spatial scales (~ 40 km). [ABSTRACT FROM AUTHOR]

The SMOS (Soil Moisture and Ocean Salinity) mission provides surface soil moisture (SM) maps at a mean resolution of ~ 50 km. However, agricultural applications (irrigation, crop monitoring) and some hydrological applications (floods and modeling of small basins) require higher resolution SM information. In order to overcome this spatial mismatch, a disaggregation algorithm called Disaggregation based on Physical And Theoretical scale Change (DISPATCH) combines higher-resolution data from optical/thermal sensors with the SM retrieved from microwave sensors like SMOS, producing higher-resolution SM as the output. A DISPATCH-based processor has been implemented for the whole globe (emerged lands) in the Centre Aval de Traitement des Données SMOS (CATDS), the French data processing center for SMOS Level 3 products. This new CATDS Level-4 Disaggregation processor (C4DIS) generates SM maps at 1 km resolution. This paper provides an overview of the C4DIS architecture, algorithms and output products. Differences with the original DISPATCH prototype are explained and major processing parameters are presented. The C4DIS SM product is compared against L3 and in situ SM data during a one year period over the Murrumbidgee catchment and the Yanco area (Australia), and during a four and a half year period over the Little Washita and the Walnut Gulch watersheds (USA). The four validation areas represent highly contrasting climate regions with different landscape properties. According to this analysis, the C4DIS SM product improves the spatio-temporal correlation with in situ measurements in the semi-arid regions with substantial SM spatial variability mainly driven by precipitation and irrigation. In sub-humid regions like the Little Washita watershed, the performance of the algorithm is poor except for summer, as result of the weak moisture-evaporation coupling. Disaggregated products do not succeed to have and additional benefit in the Walnut Gulch watershed, which is also semi-arid but with well-drained soils that are likely to cancel the spatial contrast needed by DISPATCH. Although further validation studies are still needed to better assess the performance of DISPATCH in a range of surface and atmospheric conditions, the new C4DIS product is expected to provide satisfying results over regions having medium to high SM spatial variability. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. 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 vegetation, in particular plant available water and water content in a canopy, drought index and flood risks, surface ocean winds in storms, freeze/thaw state and sea ice and its effect on ocean–atmosphere heat fluxes and dynamics affecting large-scale processes of the Earth's climate system. Significant progress has been made over the course of the now 6-year life time of the SMOS mission in improving the ESA provided level 1 brightness temperature and level 2 soil moisture and sea surface salinity data products. The main emphasis of this paper is to review the status of the mission and provide an overview and performance assessment of SMOS data products, in particular with a view towards operational applications, and using SMOS products in data assimilation. SMOS is in excellent technical condition with no limiting factors for operations beyond 2017. The instrument performance fulfils the requirements. The radio-frequency interference (RFI) contamination originates from man-made emitters on ground, operating in the protected L-band and adding signal to the natural radiation emitted by the Earth. RFI has been detected worldwide and has been significantly reduced in Europe and the Americas but remains a constraint in Asia and the Middle East. The mission's scientific objectives have been reached over land and are approaching the mission objectives over ocean. This review paper aims to provide an introduction and synthesis to the papers published in this RSE special issue on SMOS. [ABSTRACT FROM AUTHOR]

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 might experience data loss or underestimation of soil moisture and ocean salinity retrieval values. To alleviate this situation, the SMOS team has put several strategies into place that help improve the RFI situation, filter the SMOS data from RFI perturbed measurements and bring awareness to the RFI problem. [ABSTRACT FROM AUTHOR]

The main objectives of this study were to provide a proxy “validation” of the Soil Moisture and Ocean Salinity (SMOS) mission's vegetation optical depth product ( τ SMOS ) on a global scale, to give a first indication of the potential of τ SMOS to capture large-scale vegetation dynamics, and to contribute towards investigations into the possible use of optical vegetation indices (VI's) for the estimation of τ . The analyses were performed by comparing the spatial and temporal behaviour of τ SMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E ( τ AMSR-E ). 16-day and annual average values of the passive microwave optical depth ( τ ) for the year 2010 were obtained from SMOS (1.4 GHz) and AMSR-E (6.9 GHz) observations. The VI's chosen for this study were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Normalized Difference Water Index (NDWI). The highest global-scale, annual correlation was found between τ SMOS and τ AMSR-E from ascending orbits (Spearman's R = 0.80). On global, annual scales, τ SMOS showed higher correlations with τ AMSR-E than with the VI's, while τ AMSR-E was more highly correlated with VI's than with τ SMOS . Timeseries of both τ and the VI's were made per landcover class, for the northern hemisphere, tropics and southern hemisphere. Although the large-scale spatial and spatio-temporal behaviour of τ SMOS and τ AMSR-E is generally similar, the results highlight some notable differences in observing vegetation with optical vs . passive microwave sensors, and certain crucial differences between the two passive microwave sensors themselves. Overall, the results found in this study give a good first confidence in the SMOS L3 τ product and its potential use in vegetation studies. These results provide an essential general reference for future (global-scale) vegetation monitoring with passive microwaves, for the future inclusion of τ SMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development. [ABSTRACT FROM AUTHOR]

The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010-11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation. [ABSTRACT FROM AUTHOR]

Abstract: Global Level-3 surface soil moisture (SM) maps derived from the passive microwave SMOS (Soil Moisture and Ocean Salinity) observations at L-band have recently been released. In this study, a comparative analysis of this Level 3 product (referred to as SMOSL3) along with another Surface SM (SSM) product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) at C-band is presented (this latter product is referred to as AMSRM). SM-DAS-2, a SSM product produced by the European Centre for Medium Range Weather Forecasts (ECMWF) Land Data Assimilation System (LDAS) was used to monitor both SMOSL3 and AMSRM qualities. The present study was carried out from 03/2010 to 09/2011, a period during which both SMOS and AMSR-E products were available at global scale. Three statistical metrics were used for the evaluation; the correlation coefficient (R), the Root Mean Squared Difference (RMSD), and the bias. Results were analysed using maps of biomes and Leaf Area Index (LAI). It is shown that both SMOSL3 and AMSRM captured well the spatio-temporal variability of SM-DAS-2 for most of the biomes. In terms of correlation values, the SMOSL3 product was found to better capture the SSM temporal dynamics in highly vegetated biomes (“tropical humid”, “temperate humid”, etc.) while best results for AMSRM were obtained over arid and semi-arid biomes (“desert temperate”, “desert tropical”, etc.). Finally, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (marginally better) SSM products correlated well with the SM-DAS-2 product over regions with sparse vegetation for values of LAI ≤1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI>1, SMOSL3 showed better correlations with SM-DAS-2 than AMSRM: SMOSL3 had a consistent performance up to LAI=6, whereas the AMSRM performance deteriorated with increasing values of LAI. This study reveals that SMOS and AMSR-E complement one another in monitoring SSM over a wide range in conditions of vegetation density and that there are valuable satellite observed SSM data records over more than 10years, which can be used to study land–atmosphere processes. [Copyright &y& Elsevier]

Present soil moisture and ocean salinity maps retrieved by remote sensing are characterized by a coarse spatial resolution. Hydrological, meteorological and climatological applications would benefit greatly from a better spatial resolution. Owing to the dimensions of the satellite structure and to the degradation of the instrument's radiometric sensitivity, such improvement cannot be achieved with classical interferometry. Then, in order to achieve this goal an original concept for passive interferometric measurements is described. This concept should allow to achieve a much finer spatial resolution, which can be further improved with the application of disaggregation methods. The results will then allow the integration of global soil moisture maps into hydrological models, a better management of water resources at small scales and an improvement in spatial precision for various applications. [ABSTRACT FROM AUTHOR]

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20°, 30°, 40°, 50°, and 60°) were considered in the analysis. The best wS estimations were obtained when using both polarizations at an angle of 40°. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions. [ABSTRACT FROM AUTHOR]

Radiative properties at 1.4 GHz of vine vegetation are investigated by measuring brightness temperatures with the ETH L-band Radiometer II (ELBARA II) operated on a tower at the Mediterranean Ecosystem L-band Characterisation Experiment III (MELBEX III) field site in Spain. To this aim, experiments with and without a reflecting foil placed under the vines were performed for the vegetation winter and summer states, respectively, to provide prevailingly information on vegetation transmissivities. The resulting parameters, which can be considered as “ground truth” for the MELBEX III vineyard, were retrieved from brightness temperature at horizontal and vertical polarization measured at observation angles between 30° and 60°. These MELBEX III “ground-truth” values are representative for the Mediterranean Soil Moisture and Ocean Salinity (SMOS) Valencia Anchor Station (VAS) and therefore valuable for the corresponding calibration and validation activities over the VAS site. Likewise, quantifying the uncertainties of the measured brightness temperatures was also important, particularly as several equivalent ELBARA II instruments are currently operative in ongoing SMOS-related field campaigns. [ABSTRACT FROM AUTHOR]

Disaggregation based on Physical And Theoretical scale Change (DisPATCh) is an algorithm dedicated to the disaggregation of soil moisture observations using high-resolution soil temperature data. DisPATCh converts soil temperature fields into soil moisture fields given a semi-empirical soil evaporative efficiency model and a first-order Taylor series expansion around the field-mean soil moisture. In this study, the disaggregation approach is applied to Soil Moisture and Ocean Salinity (SMOS) satellite data over the 500 km by 100 km Australian Airborne Calibration/validation Experiments for SMOS (AACES) area. The 40-km resolution SMOS surface soil moisture pixels are disaggregated at 1-km resolution using the soil skin temperature derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, and subsequently compared with the AACES intensive ground measurements aggregated at 1-km resolution. The objective is to test DisPATCh under various surface and atmospheric conditions. It is found that the accuracy of disaggregation products varies greatly according to season: while the correlation coefficient between disaggregated and in situ soil moisture is about 0.7 during the summer AACES, it is approximately zero during the winter AACES, consistent with a weaker coupling between evaporation and surface soil moisture in temperate than in semi-arid climate. Moreover, during the summer AACES, the correlation coefficient between disaggregated and in situ soil moisture is increased from 0.70 to 0.85, by separating the 1-km pixels where MODIS temperature is mainly controlled by soil evaporation, from those where MODIS temperature is controlled by both soil evaporation and vegetation transpiration. It is also found that the 5-km resolution atmospheric correction of the official MODIS temperature data has a significant impact on DisPATCh output. An alternative atmospheric correction at 40-km resolution increases the correlation coefficient between disaggregated and in situ soil moisture from 0.72 to 0.82 during the summer AACES. Results indicate that DisPATCh has a strong potential in low-vegetated semi-arid areas where it can be used as a tool to evaluate SMOS data (by reducing the mismatch in spatial extent between SMOS observations and localized in situ measurements), and as a further step, to derive a 1-km resolution soil moisture product adapted for large-scale hydrological studies. [ABSTRACT FROM AUTHOR]

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve. [ABSTRACT FROM AUTHOR]

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter. [ABSTRACT FROM AUTHOR]

The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m3/m3. Further analysis found the following: 1) The roughness value HR was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tth and ttv required optimization, yielding new values of tth = 0.2 and ttv = 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) mission delivers global surface soil moisture fields at high temporal resolution which is of major relevance for water management and climate predictions. Between April 26 and May 9, 2010, an airborne campaign with the L-band radiometer EMIRAD-2 was carried out within one SMOS pixel (44 × 44 km) in the Skjern River Catchment, Denmark. Concurrently, ground sampling was conducted within three 2 × 2 km patches (EMIRAD footprint size) of differing land cover. By means of this data set, the objective of this study is to present the validation of SMOS L1C brightness temperatures TB of the selected node. Data is stepwise compared from point via EMIRAD to SMOS scale. From ground soil moisture samples, TB's are pointwise estimated through the L-band microwave emission of the biosphere model using land cover specific model settings. These TB's are patchwise averaged and compared with EMIRAD TB's. A simple uncertainty assessment by means of a set of model runs with the most influencing parameters varied within a most likely interval results in a considerable spread of TB's (5-20 K). However, for each land cover class, a combination of parameters could be selected to bring modeled and EMIRAD data in good agreement. Thereby, replacing the Dobson dielectric mixing model with the Mironov model decreases the overall RMSE from 11.5 K to 3.8 K. Similarly, EMIRAD data averaged at SMOS scale and corresponding SMOS TB 's show good accordance on the single day where comparison is not prevented by strong radio-frequency interference (RFI) (May 2, avg. RMSE = 9.7 K). While the advantages of solid data sets of high spatial coverage and density throughout spatial scales for SMOS validation could be clearly demonstrated, small temporal variability in soil moisture conditions and RFI contamination throughout the campaign limited the extent of the validation work. Further attempts over longer time frames are planned by means of soil moisture network data as well as studies on the impacts of organic layers under natural vegetation and higher open water fractions at surrounding grid nodes. [ABSTRACT FROM AUTHOR]

Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil moisture improves the spatial distribution of surface soil moisture at the observation time. These results are obtainable regardless of the spatial scale at which solar radiation, air temperature, wind speed, and air humidity are available within the microwave pixel and for an assimilation frequency varying from 1/1 day to 1/5 days. [ABSTRACT FROM AUTHOR]

The main objective of the Soil Moisture and Ocean Salinity (SMOS) mission over the land surface is to monitor surface soil moisture (SM) from measurements of the microwave emission at L-band. These measurements will be done at many incidence angles and two polarizations. The multiconfiguration system of SMOS provides the possibility to retrieve one or several surface parameters in addition to soil moisture. Such an approach, referred to as N-parameter retrievals (where N is the number of retrieved parameters), could make the retrieval process more efficient. In this study, this possibility was investigated using an experimental dataset acquired over a wheat field. First, a detailed analysis of the attenuation properties of the wheat canopy, a vertical-stem dominated crop, was made. The results showed that the polarization and incidence angle dependence of attenuation was very significant and could be accurately modeled using only two parameters: the optical depth τ at nadir (τ(θ=0)) and a polarization parameter (Cpol). The need for this latter parameter results from the difference in the dielectric properties of wheat for the vertical and horizontal polarizations. Based on these results, a detailed comparison of different soil moisture retrieval approaches was made. One-, two-, and three-parameter retrieval approaches were tested. In this latter method, soil moisture and the two vegetation parameters (τ(θ=0) and Cpol) were retrieved simultaneously. This method was found to be the most efficient: it provided the best accuracy in the SM retrievals and it did not require ancillary information on the vegetation characteristics, which cannot be easily estimated for operational SM mapping applications. [Copyright &y& Elsevier]

This paper reports on a new soil moisture disaggregation scheme based on topography and soil depth information. It is designed for low resolution remote sensing data assimilation into hydrological modelling. The scheme makes use of a simple Soil Vegetation Atmosphere Transfer model coupled to the TOPMODEL formalism. Water and energy balance are computed at the catchment scale, taking lateral flows due to topography into account. Lumped values of near-surface and deep soil water content are then disaggregated at local scale using simple relationship between mean quantities, local topography and soil depth information. Results for a small water catchment in South-eastern Australia show satisfactory reproduction of the local soil moisture patterns using a combination of topography and soil depth information. Due to subgrid variability and differences between the simulation and observation scale (the Digital Elevation Model pixel versus the point measurement), the point-to-point comparison between observations and simulations shows a poor correlation. Rescaling shows that a good correlation is obtained when averaging the simulated and observed soil moisture over a length of 100 m. [Copyright &y& Elsevier]

This paper summarises the presentations and recommendations of an International Workshop on ‘Scaling up in hydrology using remote sensing’ which was held at the Institute of Hydrology in June 1996. The Workshop highlighted considerable gaps in our knowledge of scaling issues and strongly recommended further analysis of existing data sets to assess the accuracy of remotely-sensed algorithms and assessment of propagation of errors in remote sensing through models to their outputs. To facilitate these investigations the Workshop highlighted these essential requirements—to make data sets more readily available and user friendly, develop formal programmes to compare algorithms and earmark funds specifically for these analysis programmes. [ABSTRACT FROM AUTHOR]

This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future. • Satellite soil moisture validation methods are reviewed • Community-agreed validation good practice guidelines are presented • A standardized satellite soil moisture validation protocol is provided [ABSTRACT FROM AUTHOR]