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Aquarius is an L-band radiometer system designed to map sea surface salinity from space. This is a sensitive measurement, and protection from radio frequency interference (RFI) is important for success. An initial look at the performance of the Aquarius RFI detection and mitigation algorithm is reported together with examples of the global distribution of RFI at the L-band. To protect against RFI, Aquarius employs rapid sampling (10 ms) and a “glitch” detection algorithm that looks for outliers among the samples. Samples identified as RFI are removed, and the remainder is averaged to produce an RFI-free signal for the salinity retrieval algorithm. The RFI detection algorithm appears to work well over the ocean with modest rates for false alarms (5%) and missed detection. The global distribution of RFI coincides well with population centers and is consistent with observations reported by the Soil Moisture and Ocean Salinity mission. [ABSTRACT FROM PUBLISHER]

This paper demonstrates the use of moderate resolution imaging spectro-radiometer (MODIS) data for fish forecasting mapping of seasonal spatial distribution of sea surface salinity (SSS), temperature (SST) and chlorophyll-a in the ocean waters off the coast of Semporna, Malaysia. Multi-linear regression analysis was performed to estimate SSS and the Brown and Minnet algorithm was used for the SST. The extracted parameters were validated usingin situmeasurement taken with Hydro-Lab equipment. The extracted parameters from MODIS images reveal the signature values which establish the relationships between these parameters, and thus delineating the potential fish zonation (PFZ) map. These developed models will help for accurate monitoring of large coverage areas at low cost and within short period of time. Furthermore, such models will allow the prediction of the total fish catch in different seasons, thus contributing to fish industry management and marketing. This research recommends the use of PFZ map for mass scale fish harvesting in short time for larger areas. Finally, the research has developed a potential fish zone model amalgamating all the above parameters. The PFZ mapping was carried out off the coast of Semporna, Sabah as there were sufficient fish catch data for accuracy assessment. The R was computed as 0.93 and the higher fish catch areas have coincided very well with the higher PFZ values, meaning the tool is ready for use for operational near real-time fish forecasting. [ABSTRACT FROM AUTHOR]

After 2.5 years of the Soil Moisture and Ocean Salinity (SMOS) mission, the characterization of residual instrumental systematic errors in the measured brightness temperatures (TB) is still rather poor. This, in turn, negatively impacts the sea surface salinity retrievals and, as such, notably limits the mission's success. The error mitigation methodology currently used operationally, the so-called Ocean Target Transformation (OTT), mixes both instrumental and model-induced errors. In this paper, it is proposed to distinguish errors by their type of impact on the TB images: mean brightness level, incidence angle dependence, and azimuth angle dependence. A new approach to characterize the azimuth-dependent errors is proposed. First, a careful data selection strategy is applied. Then, an empirically fitted model, which only accounts for the TB incidence angle dependence, is subtracted from the mean TB images of the selected data sets to estimate the systematic antenna-frame errors. The robustness of this methodology is assessed through the estimated anomaly pattern stability when computed for different geophysical conditions, periods of time, and latitudinal bands. The residual variability ranges from 0.03 K to 0.14 K, whereas the OTT variability is about 0.5 K. The new method is forward model independent and generic. It can therefore be applied to estimate the antenna-frame systematic errors over land and ice. Moreover, it proves to be very effective in separating different sources of error and can therefore be used to further characterize other error components and improve the various SMOS forward model terms. [ABSTRACT FROM AUTHOR]

Abstract: The spectral reflectance of the sea surface recorded using ocean colour satellite sensors has been used to estimate chlorophyll-a concentrations for decades. However, in bio-optically complex coastal waters, these estimates are compromised by the presence of several other coloured components besides chlorophyll, especially in regions affected by low-salinity waters. The present work aims to (a) describe the influence of the freshwater plume from the La Plata River on the variability of in situ remote sensing reflectance and (b) evaluate the performance of operational ocean colour chlorophyll algorithms applied to Southwestern Atlantic waters, which receive a remarkable seasonal contribution from La Plata River discharges. Data from three oceanographic cruises are used, in addition to a historical regional bio-optical dataset. Deviations found between measured and estimated concentrations of chlorophyll-a are examined in relation to surface water salinity and turbidity gradients to investigate the source of errors in satellite estimates of pigment concentrations. We observed significant seasonal variability in surface reflectance properties that are strongly driven by La Plata River plume dynamics and arise from the presence of high levels of inorganic suspended solids and coloured dissolved materials. As expected, existing operational algorithms overestimate the concentration of chlorophyll-a, especially in waters of low salinity (S<33.5) and high turbidity (Rrs (670)>0.0012sr−1). Additionally, an updated version of the regional algorithm is presented, which clearly improves the chlorophyll estimation in those types of coastal environment. In general, the techniques presented here allow us to directly distinguish the bio-optical types of waters to be considered in algorithm studies by the ocean colour community. [Copyright &y& Elsevier]

Global sea surface salinity (SSS) measurements retrieved from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission are the first highest resolution salinity data available from space. There are many challenges to measuring salinity from space and obtaining a targeted accuracy of 0.1 psu. Comparisons of Level 2 (L2) SMOS SSS data with the 1/12° high resolution HYbrid Coordinate Ocean Model (HYCOM) simulations of SSS reveal large differences. These differences are minimized for an extent during the creation of Level 3 (L3) SMOS data through spatial and temporal averaging. Depending on the retrieval algorithm used, there are differences between ascending and descending passes with data collected during the descending pass exhibiting a bias toward lower SSS. It is challenging to process SMOS SSS data in the northern Indian Ocean due to radio frequency interference and large seasonal variability due to monsoonal circulation. Comparisons of SMOS L3 data with Argo float SSS and HYCOM SSS indicate the lowest discrepancies in SSS for these data sets occur in the southern tropical Indian Ocean and the largest differences between the compared salinity products are noticed in the Arabian Sea and Bay of Bengal with an erratic root mean square error in the latter region. Higher errors in SSS occurred in coastal areas compared to the open ocean. The accuracy of SMOS salinity measurements is increasing with the maturity of the data and new algorithms. [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]

A prerequisite for the successful retrieval of geophysical parameters from remote sensing measurements is the development of an accurate forward model. The European Space Agency Soil Moisture and Ocean Salinity (SMOS), carrying onboard an L-band interferometric radiometer (Microwave Interferometric Radiometer using Aperture Synthesis), was launched on November 2009. Due to the lack of L-band passive ocean measurements from space, several prelaunch forward models were developed and initially used in the SMOS ocean salinity operational processor. In this paper, an update of the prelaunch semi-empirical forward model is presented, using for the first time, real SMOS data. In particular, the ocean surface emissivity modulation at L-band due to rough sea surface is reviewed and reanalyzed. A new model definition is provided with the help of a simple neural network. The improvement is quantified in terms of retrieved salinity accuracy compared with the climatology and concerns essentially the range of wind speeds higher than 12 m·s-1. [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]

Multi-angular images of the brightness temperature (TB) of the Earth at 1.4 GHz are reconstructed from the Soil Moisture and Ocean Salinity (SMOS) satellite sensor data since end 2009. Sea surface salinity (SSS) products remote sensing from space is being attempted using these data over the world oceans. The quality of the first version of the European Space Agency operational Level 2 (L2) SSS swath products is assessed in this paper, using satellite/in situ SSS data match-ups that were collected over the second half of 2010. This database reveals that 95% of the SMOS L2 products show a global error standard deviation on the order of ~ 1.3 practical salinity scale. Simple spatiotemporal aggregation of the L2 products to generate monthly SSS maps at 1° ×1° spatial resolution reduces the error down to about 0.6 globally and 0.4 in the tropics for 90% of the data. Several major problems are, however, detected in the products. Systematically, SMOS SSS data are biased within a ~ 1500 km wide belt along the world coasts and sea ice edges, with a contamination intensity and spread varying from ascending to descending passes. Numerous world ocean areas are permanently or intermittently contaminated by radio-frequency interferences, particularly in the northern high latitudes and following Asia coastlines. Moreover, temporal drifts in the retrieved SSS fields are found with varying signatures in ascending and descending passes. In descending passes, a time-dependent strong latitudinal bias is found, with maximum amplitude reached at the end of the year. Errors in the forward modeling of the wind-induced emissivity and of the sea surface scattered galactic sources are as well identified, biasing the sss retrievals at high and low winds and when the galactic equator sources are reflected toward the sensor. [ABSTRACT FROM AUTHOR]

The Level 2 soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) mission have been re- leased. The data must be validated under different scenarios of biophysical and climatic conditions. For the current study, the data from January to December 2010 from 20 in situ soil moisture stations from the REMEDHUS soil moisture measurement station network (Spain) were used. A comparison analysis was carried out in terms of the soil moisture content, its spatial variability, and temporal stability. The results show an acceptable level of agreement (R = 0.73, RMSD = 0.069 m3 · m-3, and bias = 0.053 m3 · m-3) between the in situ and satellite data. A slight constant underestimation from the SMOS data set was detected. A centered (bias removed) root-mean-square difference was calculated to account for this persistent bias (RMSDc = 0.044 m3 · m-3). This result is close to the SMOS accuracy objective of 0.04 m3 · m-3. Two conclusions can be drawn: First, SMOS is close to meet the mission accuracy requirements in REMEDHUS, and second, SMOS is able to detect temporal anomalies and the temporal evolution of ground soil moisture, even though the soil moisture was slightly underestimated. Despite a noticeably reduced spatial variability among the SMOS grid cells, the remotely sensed soil moisture shows a spatial pattern of the soil moisture fields on the area scale, in agreement with the site-specific characteristics of REMEDHUS. No differences were found between the use of ascending and descending orbits. In addition, no differences were detected between the use of time-overpass values of in situ soil moisture and that of the daily average. [ABSTRACT FROM AUTHOR]

A renewal of interest for the radiometric L-band Sea Surface Salinity (SSS) remote sensing appeared in the 1990s and led to the Soil Moisture and Ocean Salinity (SMOS) satellite launched in November 2009 and to the Aquarius mission (launched in June 2011). However, due to low signal to noise ratio, retrieving SSS from L-band radiometry is very challenging. In order to validate and improve L-band radiative transfer model and salinity retrieval method used in SMOS data processing, the Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS) was developed. We analyze here a coastal flight (20 May 2009), in the Gulf of Biscay, characterized by strong SSS gradients (28 to 35 pss-78). Extensive in-situ measurements were gathered along the plane track. Brightness temperature (Tb) integrated over 800 ms correlates well with simulated Tb (correlation coefficients between 0.80 and 0.96; standard deviations of the difference of 0.2 K). Over the whole flight, the standard deviation of the difference between CAROLS and in-situ SSS is about 0.3 pss-78 more accurate than SSS fields derived from coastal numerical model or objective analysis. In the northern part of the flight, CAROLS and in-situ SSS agree. In the southern part, the best agreement is found when using only V-polarization measured at 30° incidence angle or when using a multiparameter retrieval assuming large error on Tb (suggesting the presence of biases on H-polarization). When compared to high-resolution model SSS, the CAROLS SSS underlines the high SSS temporal variability in river plume and on continental shelf border, and the importance of using realistic river run-offs for modeling coastal SSS. [ABSTRACT FROM AUTHOR]

The presence of radio frequency interferences (RFIs) in the data collected by the Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency is larger than what was anticipated, and it has a strong impact on the retrieval of these geophysical parameters. The first efforts to deal with this problem were proposed by DEIMOS Engenharia in the Level 1 Prototype Processor released in November 2010, and they are also a part of its latest versions. DEIMOS focused on three key aspects to detect the RFIs, to mitigate these sources, and to flag the data in SMOS products affected by RFIs. The detection algorithm is based on a statistic approach that computes the most probable position of the RFI source based on multiangular observations made by SMOS. The mitigation algorithm has been designed taking as reference the existing technique to remove the sun effect. The mechanism to flag the data has been developed in cooperation with the European Space Agency and consists in flagging geolocated pixels according to the strength of the RFI. In this paper, three algorithms will be presented, as well as their results. For completion, a high-level description of the data processing for SMOS is also a part of this text. [ABSTRACT FROM AUTHOR]

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2, 2009. Providing accurate soil moisture (SM) estimation is one of its main scientific objectives. Since the end of the commissioning phase, preliminary global SMOS SM data [Level 2 (L2) product] are distributed to users. In this paper, we carried out a first assessment of the reliability of this product through a comparison with in situ observed and modeled SM over three different sites: One is located in Luxemburg, and two are located in Italy. The period from August 1, 2010, to July 1, 2011, has been analyzed, giving us the opportunity to evaluate the satellite response to different SM states. The selected period is important for hydrological predictions as it is typically characterized by a sequence of transitions from dry to wet and from wet to dry conditions. In order to compare SMOS and ground SM measurements, a two-step approach has been applied. First, an exponential filter has been applied to approximate root-zone SM, and second, a cumulative distribution function matching has been employed to remove systematic differences between satellite and in situ observations and model simulations of SM. Our results indicate rather good reliability of the filtered and bias-corrected SM estimates derived from the first SMOS L2 products. Bearing in mind that an updated/advanced version of the SMOS SM product has been recently produced, our preliminary results already seem to confirm the potential of SMOS for monitoring of water in soils. [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]

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]

In this paper, we present an analysis of the radiometric performance of the three 1.4-GHz noise injection radiometers of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite. The units measure the antenna temperature, which contributes to the average brightness temperature level of SMOS retrievals. We assess the radiometric resolution of the receivers, the similarity between their measurements, and their thermal stability. For these purposes, we use SMOS measurement data gathered during the first year of the orbital operations of the satellite, which was launched in November 2009. The main results from the analysis are that the units meet the design requirements with a margin. Also, we present a new thermal model for the radiometers to further enhance their stability. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) detection techniques have different challenges and opportunities for interferometric radiometers such as the Microwave Imaging Radiometer using Aperture Synthesis on the Soil Moisture and Ocean Salinity (SMOS) mission. SMOS does not have highly oversampled temporal resolution or subband filters for oversampled spectral resolution, as do other radiometers with enhanced RFI detection capabilities. It does, however, have multisampled angular resolution in the sense that a single location is viewed from many different angles of incidence. This paper compares and contrasts RFI detection algorithms that use measurements made at a variety of different levels of SMOS signal processing, including the visibility domain, brightness temperature spatial domain, and brightness temperature angular domain. The angular domain detection algorithm, in particular, is developed and characterized in detail. Examples of the algorithms applied to cases with RFI (to assess detection skill) and without RFI (to assess false-alarm behavior) are considered. [ABSTRACT FROM AUTHOR]

With the Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2009, global measurements of L-band microwave emissions and processed “soil moisture” products at a fine time resolution are available. They may, after validation, lead to quantitative maps of global soil moisture dynamics. This paper presents a first validation of the SMOS “soil moisture” product delivered by the European Space Agency in the upper Danube catchment (southern Germany). Processing of the SMOS “soil moisture” product and the methodology to compare it with in situ and model data are described. The in situ data were taken from May to mid-July 2010 in a small and homogeneous area within the catchment, while the modeled time series spans from April to October 2010 for the whole catchment. The comparisons exhibit a dry bias of the SMOS data of about 0.2 m3·m-3 with respect to in situ measurements. Throughout the catchment, the SMOS data product shows a dry bias between 0.11 and 0.3 m3·m-3 when compared to modeled soil moisture. Correlation coefficients between both data were found to be mostly below 0.3. Radio-frequency interference (RFI) over Europe appears to be the main problem in obtaining valuable information from the SMOS soil moisture product over this region. RFI is not adequately captured by current methods for filtering and flagging. Nevertheless, some improvements of these results might be achievable through refinements of the soil moisture modeling as well as through improvements to the processors used to generate the SMOS soil moisture product. [ABSTRACT FROM AUTHOR]

The validation of coarse-scale remote sensing products like SMOS (ESA's Soil Moisture and Ocean Salinity mission) L2 soil moisture or L1c brightness temperature data requires the maintenance of long-term soil moisture monitoring sites like the Upper Danube Catchment SMOS validation site situated in Southern Germany. An automatic framework has been built up to compare SMOS data against in situ measurements, land surface model simulations, and ancillary satellite data. The uncertainties of the different data sets used for SMOS validation are being assessed in this paper by comparing different microwave radiative transfer and land surface model results to measured soil moisture and brightness temperature data from local scale to SMOS scale. The mean observed uncertainties of the modeled soil moisture decrease from 0.094 m3 m-3 on the local scale to 0.040 m3 m-3 root mean squared error (RMSE) on the large scale. The RMSE of anomalies is 0.023 m3 m-3 on the large scale. The mean R2 increases from 0.6 on the local scale to 0.75 on the medium scale. The land surface model tends to underestimate soil moisture under wet conditions and has a smaller dynamical range than the measurements. The brightness temperature comparison leads to a RMSE around 12-16 K between microwave radiative transfer model and airborne measurements under varying soil moisture and vegetation conditions. The assessed data sets are considered reliable and robust enough to be able to provide a valuable contribution to SMOS validation activities. [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]

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 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]

This paper provides an assessment of synoptic measurements of sea surface salinity (SSS) from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite. Due to the complex nature of the response of L-band signals to SSS, SMOS provides three values of SSS at each grid point from three different forward models. To meet oceanographic requirements for SSS retrieval accuracy, SMOS Level 2 SSS products are averaged over time and space. This paper reports on validation studies in the Atlantic based on monthly Level 3 products on a 1°×1° grid for September 2010. Outside coastal regions, large-scale SSS patterns from SMOS are in general agreement with climatology, Argo, and ocean model output. During September 2010, SSS from descending passes provides reasonable quantitative estimates, while SSS from ascending passes overestimates SSS by over 1 practical salinity unit (psu). The daily mean difference in SSS between ascending and descending passes varies during August-December 2010, reaching a maximum in September. Differences in SMOS SSS from the three models are an order of magnitude smaller than differences between ascending and descending passes. Gridded SMOS SSS data are compared against output from the U.K. Met Office Forecasting Ocean Assimilation Model (FOAM)-Nucleus for European Modelling of the Ocean (NEMO). Basic checks confirm that SSS from FOAM-NEMO is unbiased against Argo and that FOAM-NEMO SSS is a useful independent data source to validate and rapidly identify departures in SMOS SSS. Over the whole Atlantic, SMOS SSS variability against FOAM-NEMO is around 0.9 psu, decreasing to 0.5 psu over the subtropical North Atlantic. [ABSTRACT FROM AUTHOR]

The launch of the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite mission in November 2009 opened a new era of global passive monitoring at L-band (1.4-GHz band reserved for radio astronomy). The main objective of the mission is to measure soil moisture and sea surface salinity; the sole payload is the Microwave Imaging Radiometer using Aperture Synthesis. As part of comprehensive calibration and validation activities, several ground-based L-band radiometers, so-called ETH L-Band radiometers for soil moisture research (ELBARA-II), have been deployed. In this paper, we analyze a comprehensive set of measurements from one ELBARA-II deployment site in the northern boreal forest zone. The focus of this paper is in the detection of the evolution of soil frost (a relevant topic, e.g., for the study of carbon and methane cycles at high latitudes). We investigate the effects that soil freeze/thaw processes have on the L-band signature and present a simple modeling approach to analyze the relation between frost depth and the observed brightness temperature. Airborne observations are used to expand the analysis for different land cover types. Finally, the first SMOS observations from the same period are analyzed. Results show that soil freezing and thawing processes have an observable effect on the L-band signature of soil. Furthermore, the presented emission model is able to relate the observed dynamics in brightness temperature to the increase of soil frost. [ABSTRACT FROM AUTHOR]

The introductory overview and 27 papers in this special issue present information on instrument performance and first results of the SMOS mission. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the periodical. [ABSTRACT FROM AUTHOR]

Presents the front cover/table of contents for this issue of the periodical. [ABSTRACT FROM AUTHOR]