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About SMOS

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Data > MIRAS Payload Calibration

Brightness temperatures for two orbits overAustralia acquired and processed on the 18thof December 2009 with on-ground calibration (left) and acquired on the 16th of February 2010 and processed in March 2010 with in-orbit calibration (right). Only the image processed with in-orbit calibration is suitable for the retrieval of soil moisture and sea surface salinity. Credit: ESA and DEIMOS Engenharia SA.

Calibration is the process of determining the response of your measuring device to known, controlled signals so that the variables you want to measure can be better determined. From this, it follows that the precision and accuracy of your measuring device is determined by the precision and accuracy of its calibration. This ‘calibration data’ is then used to ensure the most accurate measurements are made of unknown sources. As very high accuracies are required from radiometers, radiometry can be thought of as being primarily about calibrating the instrument well. The accuracy and stability required from the Soil Moisture

and Ocean Salinity (SMOS) mission for reliable salinity and soil moisture measurements is achieved by the careful selection of calibration procedures.

Read more about these procedures by selecting from the list: On-ground calibration In-flight calibration Calibration errors Calibration processing (L1A) Calibration long-term performance

Data > MIRAS Payload Calibration > On-ground calibration

The most important on-ground calibration activities of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument were:▪ Characterisation of the complex radiation

patterns of the 69 antennas. This activity was carried out before launch in a cylindrical near-field antenna test range;

▪ Characterisation of the Noise Injection Radiometer (NIR) and the Lightweight Cost-Effective Front-end (LICEF) receivers’ noise temperatures over a wide temperature range and under laboratory conditions.

The MIRAS instrument undergoing testing in the Maxwell Facility at ESA-ESTEC. Credit: Anneke Le Floc’h, ESA.

Data > MIRAS Payload Calibration > In-flight calibration

An example definition of a short calibration sequence, which is divided into 30 steps. For each step the table shows: the number of commanded calibration events (epochs), the number of valid events, whether the noise signal injected into the LICEFs is correlated (C) or uncorrelated (U), the settings of the NIRs, Noise Source (NS), Attenuator, and Correlation Delay modes (0 for no delay). The rightmost columns indicate which type of calibration parameters may be extracted from each step, and whether there are any epochs of the step that will be marked as corrupted and so will be discarded during data processing. Credit: CASA.

In-flight calibrations are mainly based on two calibration schemes: noise established with internal diodes and reference loads, and measurements of celestial radiation. The most important in-flight calibration activities of the MIRAS instrument are:▪ Calibration of the receivers’ Local Oscillator (LO) phase

differences. This is done using internal noise diodes every 10 minutes in order to track the exact phase delay difference between receivers;

▪ Calibration of the receivers’ gains (long calibration) and power detector offset voltages (short calibration). This is done with internal noise diodes every eight weeks and once a week for long and short calibration respectively;

▪ Calibration of the receivers’ front-end losses and calibration of the NIRs. This is done during a special sky-pointing manoeuvre once every two weeks;

▪ Characterisation of the instrument’s Flat Target Response (FTR). This is done during a special sky-pointing manoeuvre twice a year.

To learn more select from the following options: Celestial radiation for SMOS calibration Internal calibration External (Vicarious) calibration

Data > > >MIRAS Payload Calibration

In-flight calibration

Celestial radiation for SMOS calibration



Internal calibration



External (Vicarious) calibration

In order to validate the SMOS calibration and performance measurements, other well known, or partially well known, targets are measured and analysed on a regular basis. One such target is Antarctica (especially the Dome-C area), an established large snow and ice covered target for SMOS. With in-situ measurements, SMOS measurements, and measurements from other space-borne L-band radiometers (e.g. NASA’s Aquarius and Soil Moisture Active Passive (SMAP) missions), the scientific community has learnt that, at L-band, certain areas of Antarctica are reasonably stable, particularly for vertical polarisation. Furthermore, the target is well suited for long-term stability analysis of L-band radiometers.

Another external target type is the ocean, which is also one of the primary measurement targets for SMOS. Over oceans, there are locations where the temperature and salinity are well known at large scales. Over such areas SMOS measurements can be compared with forward modelling results and examine the large scale error characteristics of these measurements.

In the course of the SMOS mission such activities have led to the development of Ocean Target Transformation (OTT) processing, which removes residual small-scale spatial errors from SMOS Level 2 (L2) sea surface salinity product

generation, and to the tuning of frontend loss models of the radiometers, which mostly affects long-term temporal stability.

Evolution of the Ocean Target Transformation (OTT) correction from January 2012 to January 2015 in ascending (left) and descending orbit direction (right).

Data > MIRAS Payload Calibration > Calibration errors

Simulation of SMOS brightness temperature measurements of Iceland (top) and the influence of visibility amplitude and phase errors on the SMOS image, (bottom left and right, respectively). Credit: Juha Kainulainen (Harp Technologies Ltd.).

Although calibration activities are carefully designed and carried out, they can still contain inaccuracies orcalibration errors. These errors propagate into errors in the instrument’s actual measurements, which consist of samples of the visibility function. This function describes the spatial spectrum of the brightness temperature scene that is observed by the instrument’s antennas. Different calibration errors cause different kinds of errors in this function. At the highest level, the complex visibility function (at any spectrum) can have 1) amplitude errors, 2) phase errors, and 3) errors in the zero-frequency component.

Amplitude errors are mostly caused by antenna pattern amplitude characterisation errors and receiver system temperature calibration errors. These errors typically cause the intensity dependent error component, which blurs the brightness temperature measurement. Phase errors are dominated by antenna relative phase pattern calibration errors, phase characterisation errors of the calibration network, and residual errors of the LO calibration. In SMOS images, phase errors typically cause strong errors close to contrast changes. This is understandable as the target direction is imposed in the phase information and errors in phase cause target dislocation. Finally, there are the errors caused by the zero-frequency component, which is determined with special reference radiometers: NIRs. The error in this zero-frequency component transforms into a constant bias or offset added to the whole brightness temperature image.

Data > MIRAS Payload Calibration > Calibration processing (L1A)

The Level 1A (L1A) processor is responsible for calibrating the measurements performed by the MIRAS instrument. It is able to process Level 0 (L0) data from the instrument and extract calibration parameters from in-orbit calibration. The processor then uses these calibration parameters during the calibration of the science measurement, after the appropriate interpolations and temperature sensitivity corrections have been applied. The L1A processor consists of different units that implement self-contained sets of

algorithms. After this initial sequence of processing, calibration data is refreshed at different intervals depending upon how each calibration parameter evolves over time. Expand the interactive diagram below and select the different units for a high level description of the L1A processing. For further details about the L1A processing, see the Algorithm Theoretical Baseline Document (ATBD) available on the ESA SMOS webpage.

Detailed view of L1A Processing Module. Credit:DEIMOS Engenharia SA.

Data > MIRAS Payload Calibration > Calibration long term performance

Average temperature evolution of the 66 LICEFs and three NIRs since January 2010. Credit: Airbus DS Spain.

The monitoring of the MIRAS instrument’s calibration data is a continuous activity performed during the mission exploitation phase to:▪ Identify any instrument malfunction;▪ Derive evolution trends to predict

instrument behaviour;▪ Improve mission operations and

the calibration baseline;▪ Increase the understanding of the

instrument behaviour through analysis of long-term data trends.

If a deviation is found, the cause of that deviation is investigated. Behavioural projections are made and recommendations to re-establish nominal values or at least limit the deviation’s impact are made. The instrument long-term monitoring is executed monthly, based on the analysis of the instrument and platform HKTM and calibration information from the operational ground segment or generated by the different Expert Support Laboratories (ESLs). The good performance of the thermal control can be identified, as the instrument has remained stable about the thermal control point, which is set to 22 ˚C. Small variations, as shown in the image, are due to the different amount of illumination from the sun that seasonally affects the SMOS orbit. The small peaks are representative of the external calibration manoeuvres looking to the cold sky, where the performance of the thermal control reduces.

Data > Algorithm

Data > Algorithm > L1A to L1B Data Processor

L1A to L1B data processor

L1ATLM

L1B

Data extraction

Receivers correction

Foreign sources correction(Sky, Direct Sun, Reflected Sun)

Bias correctionalgorithm

Image reconstructionalgorithm

The Level 1A (L1A) to Level 1B (L1B) data processor, or Image Reconstruction Processor, converts the interferometric measurements (L1A data) into brightness temperature Fourier components (L1B data). These Fourier components are then used to obtain the brightness temperature values in the spatial domain (Level 1C (L1C) data).

Select boxes on the diagram to learn more about the main blocks of the L1A to L1B data processor.

Variations in the transmission factor of MIRAS are shown with the spatial frequency of an input cosine wave. The circles are the result of numerical simulations and the solid line curve is the expected behaviour of the star-shaped frequency coverage of MIRAS.

Spatial frequencies up to a ratio of d/λ equal to 20 are correctly reconstructed by the instrument and then gradually distorted. In the case of the SMOS satellite this corresponds to an on-ground spatial resolution of ~40 km. Credit: E. Anterrieu CESBIO


Here we see the role of spatial frequencies in the reconstruction of the image. On the left is the brightness temperature distribution in front of the instrument. On the right are brightness temperature maps reconstructed with the various numbers of baselines from the star-shaped frequency coverage of MIRAS (central panel). The higher frequencies, far away from the starshaped centre, allows the reconstruction of the finer structure of the image. Credits: E. Anterrieu

The SMOS instrument is band-limited in the Fourier domain. Indeed, due to its Y shape, the spatial frequencies in the Fourier domain are confined to a limited star-shaped region (Martin-Neira et al., 1994) and coincide with the nodes of a hexagonally sampled grid, as shown in the red region of this figure. A consequence of this band limitation is that the image reconstruction algorithm is applied in the Fourier domain instead of the spatial domain, as explained in Anterrieu (2004). Credit: CESBIO.


The observed field of view (FOV) of the SMOS instrument is the entire half space in front of the instrument, which is in the form of a unit circle on the antenna plane. The spacing between the antennas was chosen to be d = 0.875 λ, and is larger than would be required by the Shannon-Nyquist criterion, leading to earth and sky aliasing inside the reconstructed FOV as shown in this figure: the cyan circle corresponds to the entire FOV (the unit circle) and the blue ellipsoid corresponds to the Earth as seen by the instrument. The region between the Earth’s horizon and the unit circle corresponds to the sky, which has a well-known brightness temperature. The dotted circles correspond to the six aliases due to the sampling of SMOS over hexagonal grids. The hexagon in red corresponds to the reconstructed FOV limited by the spacing between the antennas. The region in green corresponds to the Extended Alias Free FOV (EAFFOV) where sky aliasing is allowed as it can be corrected. The symbols ‘+’ and ‘o’ indicate the sub-satellite footprint and the boresight respectively. Credit: CESBIO.

Data > Algorithm > Brightness Temperature Image Validation

The mission requirements for SMOS data make the Level 1 (L1) brightness temperature image stability extremely importance. To assess this, very stable and homogeneous targets are used in addition to the cold sky observations. The Antarctic plateau and the open ocean represent relatively stable targets that can be used to assess any biases and long term trends in the reconstructed L1 brightness temperature images.

To find out more select from the following list: Validation over Antarctica Validation over ocean

Data > Algorithm > >Brightness Temperature Image Validation

Validation over Antarctica

The Antarctic plateau surrounding the Concordia station on Dome-C is used as a reference target. Its brightness temperature is constant at around 200 K. The plateau has been used extensively for stability monitoring at various frequencies and continuous activity is planned onsite to characterise the surface. For long-term stability assessment,

only the SMOS grid node closest to the station is used and images are either averaged in temporal bins of 1 day or angular bins of 1 degree. To guarantee consistent comparison with other satellite-borne sensors, the unique capacity of the SMOS interferometer is used to mimic the characteristics of those sensors and the angle at which they observe.

Data > Algorithm > >Brightness Temperature Image Validation

Validation over Ocean

The open ocean represents a relatively stable target with which to assess any biases in the reconstructed images. Oceans tend to be more stable than landscapes, with temporal and spatial brightness temperature variations that do not exceed a few kelvin providing there are no strong storms nearby, which can increase the emission total power by several tens of kelvin. In calm conditions, it is sea surface salinity variations, which change the sea water dielectric constant, that drive changes in the surface emission at L-band.

Read more:

Brightness temperature biases over pure ocean scenes

Brightness temperature biases over mixed land-sea scenes

Brightness temperature biases short and long term drift over ocean

Data > Algorithm > > >Brightness TemperatureImage Validation

Biases over pure Ocean scenes


This open-ocean bias is referred to as the Ocean Target Transformation (OTT) and is used to correct the SMOS measurements in the Level 2 (L2) processor to allow the retrieval of sea surface salinity. The OTT is computed by calculating the median difference between the reconstructed image brightness temperatures and the ocean forward model in a region of ocean between 40 degrees South and the equator. This latitude band is outside the regions most impacted by sun glint, radio frequency interference (RFI), and strong winds associated with extratropical cyclones where the forward model is less accurate.

Near the coasts, the brightness temperature image biases are significantly modified by the presence of land in the SMOS field of view (FOV). To quantify this land-sea contamination (LSC) effect on a global scale, the SMOS images and the corresponding ocean forward model have been used to compute the bias (data – model) as a function of both the position on Earth and the position within the FOV.


Biases over mixed Land Ocean scenes


Drifts in brightness temperature over the ocean are assessed by comparing the SMOS measurements in the alias free FOV with the brightness temperature generated by the ocean model.

The differences between the data and model are averaged at a given latitude for all passes on a given day. Only pure open ocean scenes for which no strong RFI sources are present in the image are included.

Time-latitude (or ‘Hovmoller’) plots of the difference between the data and the ocean model averaged over the full alias free field of view (FOV) region. These plots reveal a clear annual cycle that repeats every year, although there are some year-to-year differences in the overall amplitude of the bias variations. The descending-ascending difference shows that the drift has a strong orbital component in the period between August and November of every year. Although this bias couldbe linked to a diurnal variation in the upwelling radiation that is not well modelled, no such variation has yet been identified. However, scattered celestial sky radiation does exhibit a strong descending-ascending difference during this period, so any errors in the modelling of this contribution could play a role in this drift. The similarity of the peaks in the model and the biases suggests the need for further improvements in the model. Credit: OceanDataLab.


Short- and long-term drift over ocean


Data > Algorithm > Soil Moisture Retrieval Algorithms

Several soil moisture retrieval algorithms are currently available at different processing levels and from various data processing/distribution centres. Descriptions of these algorithms are provided below.• Soil moisture level 2 retrieval• Soil moisture near real time neural network retrieval

The neural network (NN) approach is based on a feed-forward NN using SMOS TB grouped with other sources of information. It uses an approach similar to that described by Rodriguez-Fernandez et al. (2015). The NN is a global method that allows us to check a posteriori the statistical consistency of the reference soil moisture dataset and to correct poor estimates in that dataset.

• SMOS INRA-CESBIO (SMOS-IC) A simplified approach based on the L-MEB model for L2 and L3 is being developed by the Institut National de la Recherche Agronomique (INRA) and the Centre d’Etudes Spatiales de la Biosphère (CESBIO). It is simplified in the sense that there are no fractions in a pixel (somewhat similar to the SMAP algorithm). This approach is efficient despite its simplicity and, once validated globally, could be used to quickly derive results when changing parameterisations for assessing novel modelling.

• Soil Moisture product validation

Data > Algorithm > SMOS Level 2>Soil Moisture Retrieval Algorithms

SMOS L1CData products filesAuxiliary files

Data Analysis Reportalgorithms internal datafull flags

User Data Productretrieved parametersparameters variancesscience & quality flags

Decision Treeselect main fraction for retrievalselect one model for retrievalselect other models for default contributions

TB models libraryforward modelsdefault modelsfuture models

SMOS L2 auxiliaryFixed referencesEvolving referencesUser parameters files

Generate output data

Discrete Global Grid (DGG) node data iterator

Pre-processingfilter SMOS L1C viewsco-locate Discrete Flexible Fine Grid auxiliary data with DGG nodecompute aggregated mean cover fractionscompute reference values

Do full retrieval againeliminate bad versions (outliers)decrease free number of parameters

Iterative parameters retrievalfit the selected forward model to observationsuse other models for default contributions

Post-processingcompute parameters posterior variancesretrieval analysis and diagnosticscompute modelled surface TB @ 42.5˚generate flags

The SMOS Level 2 Soil Moisture algorithm flowchart, describing all the major elements that occur during soil moisture retrieval. Credit: SMOS L2 soil moisture ATBD (Array Systems Computing et al., 2017).

The ESA SMOS Level 2 Soil Moisture (L2SM) algorithm is based on the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model (Wigneron et al., 2007). The approach is used to retrieve surface characteristics by minimising the difference between radiative transfer estimates of the brightness temperatures (TB) and actual satellite TB measurements. The approach relies heavily on the available multi-angular measurements to compensate for the relatively poor radiometric sen-sitivity of the sensor and to separate the vegetation contribution from the surface contribution. The approach is described in the SMOS soil moisture Algorithm Theoretical Basis Document (ATBD) (Kerr et al., 2014).As of November 2017, ESA has released new operational and reprocessed soil moisture products (processor version v650) to users. The main improvements of this new dataset can be summarised as follows:▪ improved characterisation of the retrieval uncertainties by introducing

a rescaling of the Chi-2 parameter;▪ usage of a land-cover map based on the International Global Biosphere

Programme (IGBP) dataset, unlike the ECOCLIMAP map that was used previously;

▪ usage of a rescaled European Centre for Medium-Range Weather Forecasts (ECMWF) forecast soil moisture.

The L2 soil moisture retrieval algorithm, with slight modifications (see Kerr et al., 2013), is the basis for the generation of the Level 3 (L3) Cen-tre Aval de Traitements des Données SMOS (CATDS) soil moisture pro-duct which include the use of three overpasses and a time correlation of the vegetation (Al Bitar et al., 2012).

Data > Algorithm > Soil Moistureproduct validation

>Soil Moisture Retrieval Algorithms

The overarching goal of product validation is to evaluate satellite soil moisture products, and also to assess their validity range. For this task, one should be aware of the different types of products, as it impacts the validation approach and validity range. Three general categories of algorithms can be defined:1. algorithms that operate on a node per node basis (i.e. a

law is defined for each grid-node, as in change detection algorithms);

2. global empirical models that are based on an empirical law obtained using a “training” dataset but applied globally;

3. grid-node global models that are generally based on a radiative transfer approach, which is generic and applied globally.

When compared to other model or satellite products it was found that SMOS is more consistent globally and often gives the best results. It does not exhibit anomalous wet values over dry areas and seems to be relatively insensitive to vegetation density (parameterised by the normalised difference vege-tation index (NDVI)). Furthermore, some progress has been made over forested areas even though, before launch, it was not expected to achieve good results with SMOS for areas with high biomass levels.

The SMOS L2 SM time series, along with other soil moisture datasets (top), a Cumulative Distribution Function (CDF) for these datasets, including SMOS L3 and SMOS NN (bottom left), and a Taylor’s diagram (bottom right) for the Niger African Monsoon Multidisciplinary Analysis Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique (AMMA- CATCH) site from January 2010 to January 2014. Credit: Kerr et al. (2016).

Data > Algorithm > Ocean Salinity Retrieval Algorithm

The Level 2 Ocean Salinity (L2OS) inversion algorithm follows an iterative retrieval scheme consisting of a series of physical models relying on auxiliary data (i.e. Sea Surface Temperature (SST), Wind Speed (WS), Total Electron Content (TEC) and the sun’s temperature at L-band) and a first guess Sea Surface Salinity (SSS), to compute the brightness temperature (TB) that should be measured at a specific polarisation and geometric configuration (see an overview in Zine et al., 2008). These TB values are computed at the ocean surface and projected onto the SMOS antenna reference frame to be compared to the measured TBs. To retrieve the sea surface salinity for a grid point, the iterative process minimises the difference between modelled and measured TB from all measurements of the grid point obtained in consecutive snapshots. The retrieved sea surface salinities are provided in the L2 processor user data products.

Find out more browsing the algorithm interactive diagram

Sea Surface Salinity product validation The SMOS Level 2 Ocean Salinity algorithm flowchart, showing all the major elements that occur during sea surface salinity retrieval. Credit: ARGANS and the SMOS ESL Team (2016).

Auxiliary Data(static/dynamic)

exit

or

SMOS L1Cproduct

flags TB noise

Pre-processing

Measurementsdiscrimination

Retrieval

Post-processing

RegressionNN

Auxiliary Dataprojection

Prior values andconstraints

Contaminations(land, glitter, other)

ForwardModels

ConvergenceModules

Quality assessment

Theoretical errors

Tests

Convergencecriteria

The assessment of the L2OS data quality is based on comparisons between SMOS sea surface salinity and:

1. In Situ Analysis System (ISAS) fields for validation of large scales and monthly averages over the global ocean;

2. Thermosalinograph salinities for more punctual and higher resolution validation (e.g. in the vicinity of continents).

ISAS consists of an optimal interpolation of global in situ salinity, most of them being the entire dataset of Argo floats. The Argo float array (a system of almost 4000 worldwide distributed floats) is the only global observing system providing an almost real-time set of ocean salinity in-situ observations under all-weather conditions (Le Traon et al., 2015). The typical scales resolved by ISAS are ±300 km at the equator (decreasing with latitude) and ±1 month.

The thermosalinograph dataset consists of in situ measure-ments collected by ships of opportunity.

SMOS L2OS v662 SSS minus ISAS SSS in August 2012 for ascending and descending orbits. Units are in psu. Credit: LOCEAN.

Ship transects used for the comparison with SMOS SSS. Credit: LOCEAN.

Data > Algorithm > Product validation>Ocean Salinity Retrieval Algorithm

Data > Algorithm > Sea Ice Thickness Retrieval Algorithm

Besides soil moisture and ocean salinity, SMOS can also be used to obtain sea ice thickness measurements. The original semi-empirical SMOS sea ice thickness retrieval algorithm is called Algorithm I. It can be retrieved from the TB without any auxiliary dataset. However, Algorithm I does not account for variations in ice temperature and salinity. This means that there could errors in regions where these parameters differ from the constant values (Tian-Kunze et al., 2014).

Hence, changes were introduced to create Algorithm II. Assuming thermal equilibrium, the ice surface temperature can be estimated from the surface air temperature. Algorithm II uses a heat flux balance equation with the surface air temperature as a boundary condition. Ice salinity is also estimated from the underlying sea surface salinity using an empirical function. With the ice temperature and salinity parameters now included, TB is calculated using a sea ice radiation model. However, as both ice temperature and salinity are functions of sea ice thickness, a linear approximation method was applied to simultaneously retrieve sea ice thickness and estimate suitable ice temperature and salinity values.

In Algorithm II, a uniform plane ice layer is assumed; however, this is known to be an invalid assumption. Therefore, sea ice thickness is corrected for the influence of the ice thickness distribution function. This statistical correction leads to an apparent deeper ice penetration depth. The correction of sea ice thickness retrie-ved from Algorithm II using this distribution function is called Algorithm II*. This version of the algorithm accounts for variations of ice temperature and salinity, which determine to what depth thickness can be retrieved (Tian-Kunze et al., 2014).

Read more about SMOS sea ice thickness product validation

Schematic flowchart of the retrieval steps. d and d’ are the sea ice thicknesses from the consecutive steps, TB and TBobs are calculated and observed brightness temperatures respectively. T0 is the brightness temperature of sea water, which is assumed to be 100.5 K. Credit:Tian-Kunze et al. (2014, p1005).

The assessment of the data quality is based on comparisons between SMOS sea ice thickness and the datasets described below:

For more details on the SMOS sea ice thickness retrieval algorithms and the data validation, see the Sea Ice Thickness products page and the corresponding documentation.

Data > Algorithm > Sea Ice Thickness Product validation

>Sea Ice Thickness Retrieval Algorithm

Data > Data Products

Select from the following options: Level 1 data products

This section describes the SMOS L1 data products. Level 2 data products

This section describes the SMOS L2 data products. Level 3 and Level 4 Data Products

This section describes the SMOS L3 and L4 data products.

Level 1 (L1) SMOS data products are designed for scientific and operational users who need to work with calibrated MIRAS instrument measurements. The products are generated by the L1 Operational Processor (L1OP) and by the Near Real Time Processor (NRTP) integrated in the Data Processing Ground Segment (DPGS). The different types of L1 products are described below.A detailed description of the product contents is available in the SMOS L1 product specification document available on the ESA SMOS Data Products webpage. More details about all data types can be found in the SMOS Data Products brochure which can be found here.

Select from the following list to read more. Level 1A data products Level 1B data products Level 1C data products Level 1 Near Real Time data products

This interactive plot shows how the multi-angular brightness temperature for one grid point over Skagerrak strait running between the southeast coast of Norway, the southwest coast of Sweden, and the Jutland peninsula of Denmark is acquired along the SMOS satellite pass and made available in the L1C product. The red and blue lines shows the signal evolution for the two different antenna polarisation frame, X and Y respectively, for various incidence angles. Credit: ESA

Data > Data Products > Level 1 data products

Data > Data Products > Level 1 data products > Level 1A

Amplitude (upper left panel) and phase (upper right panel) of the calibrated visibilities in the interferometric domain (u, v coordinates). The transformation from antenna position coordinates (x, y coordinates, bottom left panel) to interferometric domain coordinates (u, v coordinates, bottom right panel) is shown. The signal from the small interferometric coordinates around zero corresponds to a short antenna baseline and represents, in a reddish yellow colour, the main energy signal from the low spatial frequency (main image structure) contained in the sensed scene. The signal from the large interferometric coordinates corresponds to a longer antenna baseline and represents, in blue, the energy from the high spatial frequency contained in the sensed scene. Credit: ESA.

Level 1A (L1A) products are calibrated interferometric measurements, called ‘calibrated visibilities’, between the individual antenna receivers of the MIRAS instrument, before image reconstruction is applied. The L1A products are available in full polarisation (i.e. measurements between antennae sharing the same polarisation (XX or YY) on the antenna polarisation reference frame and measurements between antennae with different polarisations (XY) are combined in an integration time of 1.2 seconds).

The single L1A product is provided in pole- to-pole (half-orbit) time-based segments. The sensing time included in one L1A product is about 50 minutes and the product size is about 200 MB. L1A products are available within 6-8 hours of acquisition and are provided by ESA in Earth Explorer format. L1A products are only provided to calibration/ validation (cal/val) users registered with the SMOS data dissemination service.

Data > Data Products > Level 1 data products > Level 1B

Data > Data Products > Level 1 data products > Level 1C

A L1C geo-located brightness temperature image at the top of the atmosphere in the antenna frame X polarisation. The unusual shape of the image is because only the alias free and extended alias free field of view can be considered to have accurate radiometry suitable for science applications; hence they are geo-located and consolidated inside the L1C product. Credit: ESA, Google Maps

Level 1C (L1C) brightness temperature products are the output of the geolocation algorithm applied to the L1B images. Geolocation is performed on the Icosahedral Snyder Equal Area (ISEA) projection (ISEA 4H9 grid with a grid spacing of about 15 km) and the brightness temperatures are from the top of the atmosphere in the antenna reference frame for different polarisations (i.e. X, Y and XY). Several images with different acquisition geometries will cover the same Earth grid point, hence the geo-located L1C products provide multi-incidence angle brightness temperature measurements in a range of incidence angles: from 0 to 60 degrees. Faraday and geometric rotation information is available in the L1C products to convert X and Y measurements into H and V surface polarisation status, as well as several Radio Frequency Interference (RFI) contamination flags to signal the presence of degraded measurements. The Earth grid points in the L1C product are consolidated over a ground swath of about 1000 km wide, with single L1C products being provided in pole-to-pole (half-orbit) time-based segments. The sensing time included in one L1C product is about 50 minutes and the product size is about 400 MB. Separate datasets are available for sea and land grid points. For each L1C product there is also a browse product containing the brightness temperatures averaged for an incidence angle of 42.5°. The L1C and browse products are available within 6-8 hours of acquisition and are provided by ESA in Earth Explorer format. L1C products are provided via the SMOS data dissemination service to registered users.

Data > Data Products > Level 1 data products > Level 1 NRT

Data > Data Products > Level 2 data products

Level 2 (L2) data products are designed for scientific users for research and application development. The products are generated by the L2 Operational Processors (L2OP) integrated in the Data Processing Ground Segment (DPGS).

A detailed description of the product contents is available in the SMOS L2 product specification document available on the ESA SMOS Data Products webpage. More details about all data types are available in the SMOS Data Products brochure which can be found here.

Select from the following list to read more. Level 2 Soil Moisture product Level 2 Sea Surface Salinity product Level 2 Soil Moisture neural network product

in near real-time

Data > Data Products > > Level 2 data products

Level 2 Soil moisture

Level 2 Soil Moisture (L2SM) products contain the retrieved swath-based soil moisture, vegetation optical depth and other ancillary data derived during processing (e.g. surface temperature, roughness parameter, dielectric constant, brightness temperature at the top of the atmosphere and at the surface) with their corresponding uncertainties and associated quality indexes and flags. The Earth grid points in the L2SM products are consolidated over a ground swath with a width of about 1000 km. Geolocation is performed on the Icosahedral Snyder Equal Area (ISEA) projection (ISEA 4H9) grid with grid spacing of about 15 km. The single L2SM products are provided in pole-to-pole (half-orbit) time-based segments. The sensing time included in one L2SM product is about 50 minutes and the product size is about 20 MB. These products are available within 8-12 hours of acquisition. The L2SM products are provided by ESA in Earth Explorer format and Network Common Data Form (NetCDF). The L2SM products are provided through the SMOS data dissemination service to registered users. An example of a SMOS swath-based L2SM product over Africa. Very dry

areas are represented by orange colours; wet areas are represented by blue colours. Areas represented in green and yellow have intermediate soil moisture values. Credit: ESA


Level 2 Sea surface salinity

Level 2 Sea Surface Salinity (L2SSS) products contain the retrieved swath-based sea surface salinity, the sea surface salinity anomaly with reference to the World Ocean Atlas (WOA) 2009 climatology and a corrected sea surface salinity (to mitigate the impact of the land/sea contamination along the coastline) with their corresponding uncertainties and associated quality indexes and flags. Ancillary data derived during the processing (e.g. brightness temperature at the top of the atmosphere and at the sea surface) are also available in the products. The Earth grid points in the L2SSS products are consolidated over a ground swath with a width of about 1000 km. Geolocation is performed on the ISEA projection (ISEA 4H9) grid with grid spacing of about 15 km. The single L2SSS products are provided in pole-to- pole (half-orbit) time-based segments. The sensing time included in one L2SSS product is about 50 minutes and the product size is about 20 MB. These products are available within 8-12 hours of acquisition. The L2SSS products are provided by ESA in Earth Explorer and NetCDF format. The L2SSS products are provided via the SMOS data dissemination service to registered users.

An example of a SMOS swath-based L2SSS product over the Atlan-tic Ocean. An area of ocean with a high salt content (red region) is clearly visible in the North Atlantic at about 30 degrees North. Fre-sh water from the Amazon River plume (green colour) is also visible slightly above the equator thousands of kilometres away from the Brazilian coastline. Credit: ESA

Maps (left) and histograms (right) of the mean SM-NRT-NN (top) and mean L2 SM (bottom) for each Discrete Global Grid (DGG) point. Credit: CESBIO.

L2 Soil Moisture Near Real Time Neural Network (SM-NRT-NN) products provide fast access to soil moisture measurements for operational meteorological agencies. The products contain the retrieved swath-based soil moisture and its uncertainty. The soil moisture estimation is retrieved from the multi-angular brightness temperature available in the SMOS L1 NRT products. The L2 SM-NRT-NN products are comparable to the L2SM products but they have been produced through a statistical algorithm (in this case a neural network) trained on operational SMOS L2SM data. The Earth grid points in the L2 SM-NRT-NN products are consolidated over a slightly reduced ground swath width of about 915 km. Geolocation is performed on the ISEA projection (ISEA 4H9) grid with a grid spacing of about 15 km. The sensing time included in one L2 SM-NRT-NN product is variable and depends on the acquisition scenario; therefore the data size is also variable. The data volume is approximately 5 MB/orbit. The L2 SM-NRT-NN products are provided via the SMOS data dissemination service to registered users and from EUMETCast and the World Meteorological Organisation (WMO) Global Telecommunication System (GTS) within four hours of sensing time in NetCDF.


Level 2 Soil moisture NRT-NN

Data > Data Products > Level 3 and Level 4 Data Products

Level 3 (L3) and Level 4 (L4) products cover a wider range of applications for Land, Ocean and the Cryosphere. The products differ in, for example, algorithm, spatial/temporal sampling resolution and choice of grid. More details about L3 and L4 products are available in the SMOS Data Products brochure which can be found here.

An overview of the different types of L3 and L4 products is given below.

Select from the following list to read more: Data Products over Land - Centre Aval Traitement des Donnés

SMOS (CATDS) products - Barcelona Expert Centre (BEC) products - Soil Freeze/Thaw State product Data Products over Ocean - Centre Aval Traitement des Donnés

SMOS (CATDS) products - Barcelona Expert Centre (BEC) products - L3 and L4 sea ice products

Data > > > > Level 3 and Level 4 Data Products

DataProducts

Data Productsover Land CATDS


DataProducts

Data Productsover Land BEC


DataProducts

Data Productsover Land

Soil Freeze/ Thaw State

Maps of SMOS soil freeze/thaw state estimates over the northern hemisphere on 10 October 2014, 1 November 2014 and 20 November 2014. Credit: FMI

The L3 soil freeze/thaw state product contains daily global maps of soil freeze/thaw state estimates derived from L3 SMOS brightness temperatures. Soil state is categorised into three discrete levels: ‘frozen’, ‘partially frozen’, and ‘thaw’, and accompanied with a quality data matrix estimating the data reliability for each freezing season separately.


DataProducts

Data Productsover Ocean CATDS


DataProducts

Data Productsover Ocean BEC

Level 3 and Level 4 BEC sea surface salinity productsThe L3 and L4 SMOS-BEC sea surface salinity products are available as a reprocessed dataset or as a Near Real Time (NRT) dataset. Both datasets are created over various averaging periods: 3 days, 9 days, monthly, seasonal (quarterly) and annual. In all cases, except the 3-day averaging, three different processing techniques (binning, optimal interpolation and fusion) are used, each resulting in a separate data product. Each of these data products is available for ascending, descending, and combined ascending-descending orbits. The NRT dataset is based on the ESA L2 sea surface salinity products. The NRT products are generated about 3 days after the L2 data become available. The reprocessed dataset is based on ESA L2 sea surface salinity products generated by ESA in periodic reprocessing campaigns. The L3 and L4 data grid is a regular latitude-longitude grid sampled every 0.25°. The advanced L4 products contain daily global and local (MediterraneanSea, high latitudes and Arctic) maps of sea surface salinity. The products are derived from ESA L1B products and the use of a methodology which allows the retrieval of sea surface salinity very close to the coasts and at high latitudes. The products are delivered in NetCDF and are accessible from the SMOS-BEC data portal. For further information on these products, visit the SMOS-BEC products webpage.

SMOS L3 high latitudes sea surface salinity in psu objective analysis product over the Arctic Ocean. Credit: BEC


DataProducts

Data Productsover Ocean Sea Ice

Level 4 sea ice thickness map from the week of the 6th of February 2017 based on CryoSat-2/SMOS data fusion (optimal interpolation). Credit: AWI

Level 3 sea ice thickness map productThe L3 sea ice thickness map products have been developed by ESA within the Support To Science Element (STSE) project led by the University of Hamburg. The sea ice thickness map product is based on the ESA L1C brightness temperature data. SMOS brightness temperatures are used to retrieve sea ice thicknessup to a depth of ~0.5-1 m, using a semi- empirical method. The ice thickness measurements are available mainly for thinner and younger ice at the edge of the Arctic Ocean, during the period of October-April. The observations are complementary to measurements from ESA’s CryoSat mission. Daily maps are disseminated with a latency of 24 hrs. The maps are geolocated over a polar-stereographic grid of the US National Snow and Ice Data Center (NSIDC) polar-stereographic projection at a standard latitude of 70°N with a grid spacing of 12.5 x 12.5 km2. The product is accessible from the SMOS data dissemination service. For further information visit the ICDC webpage.

Level 4 sea ice productThe L4 sea ice product developed by the Alfred Wagner Institute contains weekly maps of sea ice thickness for Arctic region. The product is derived from the L3 SMOS sea ice thickness product and the use of CryoSat-2 sea-ice thickness information. The combination of both datasets is based on a statistical approach (optimal interpolation) and

respective uncertainties for different thickness classes. The product will be disseminated by ESA. For more information visit the Alfred Wagner Institute sea ice portal.

Data > Data visualisation & access

Data visualisation toolsThere are a number of tools for viewing and manipulating SMOS data that have been made available by ESA. The main tools available are described below. For more details and access to the links to download all tools please visit the website.Select from the following list to read more: SMOS Data Viewer Global Mapping Tool SMOS Toolbox on SNAP SMOS NetCDF Conversion Tool SMOS Comparison Tool ESOV Tool

Access to SMOS dataSMOS L1 and L2 NRT and operational science data products are freely available via the SMOS Online Dissemination Service. The service provides access via Hypertext Transfer Protocol (HTTP) and File Transfer Protocol (FTP) to all users who have registered to access SMOS data (see Applying for SMOS Science Data below), upon login in with their own ESA EO-SSO account.

Select from the following list to read more: Web Access FTP Access

Data > Calibration and Validation Campaign

During the last 35 years more than 140 ESA Earth Observation (EO) campaigns have been conducted including ground- based, airborne, balloon-borne, shipborne and small satellite experiments. These campaigns are essential to support future mission development and the validation of missions in orbit.

To learn more about the calibration campaigns select from the following list: ESA Campaigns Campaigns for SMOS: an overview - ELBARA - DOMEX - International Soil Moisture Network - Sea Ice Campaign Access to campaign data

CryoSat-2 Validation Experiment (CryoVEx). Credit: MetaSensing

Data > Calibration and Validation Campaign > ESA Campaigns

ESA Earth Observation (EO) campaigns support all phases of mission development. Campaign requirements are associated with different objectives for technology, geophysical modelling, simulation and validation and cover a broad range of measurements, from active and passive optical and microwave, to gravity and magnetic measurements. Technological campaigns provide data and analyses to answer important questions related to new missions, such as choice of orbit, wavelength, spatial, spectral and temporal resolution, and sensitivity. Geophysical campaigns provide data and analyses to answer important algorithm questions related to the retrieval of geophysical information from datasets collected by EO sensors in space. Simulation campaigns deliver representative datasets for testing and training purposes which help prepare the user community for future satellite systems.

Campaigns for calibration and validation are aimed at calibration and geophysical validation of satellite data products. Calibration campaigns involve deploying reference devices in field locations and/or making in situ measurements to characterise natural reference surfaces to ensure instrument stability in orbit. Validation is the process of checking data products against independent measurements of geophysical variables, such as atmospheric variables (e.g. temperature, pressure), marine variables (e.g. sea ice thickness, ocean

salinity) and land variables (e.g. temperature, soil moisture). Validation methods use in situ measurements (ground-based, airborne, shipborne and balloon-borne instruments), data assimilation using numerical prediction models and satellite inter-comparisons. A typical calibration/validation (cal/val) campaign consists of airborne measurements using instrumentation similar to that on board the satellite. For example, the SMOSice campaign used the EMIRAD-2 radiometer, an airborne version of the MIRAS instrument on the SMOS satellite.

Up, up and away. Credit: Stefan Hendricks, AWI

Data > >Calibration and Validation Campaign

Campaigns for SMOS: an overview

Data > > >Calibration and Validation Campaign

Campaigns for SMOS: an overview ELBARA

To better understand the responses observed by the MIRAS instrument, and to aid algorithm improvement and validation, comparable ground measurements are needed. To do this ESA set up multiple ESA L-Band Radiometer (ELBARA) instruments. The ELBARA instruments are passive L-band radiometers measuring at 1.4 GHz; the same as MIRAS. Initially three second generation ELBARA (ELBARA-II) instruments were set up in different locations: Valencia

(Spain), Sodankylä (Finland) and Vercors (France). There are now four ELBARA sites; two of the original sites in Finland and Spain, and two new sites hosting third generation ELBARA instruments (ELBARA-III) in Poland and the Tibetan Plateau in China.

Click on a place name to find out more aboutthe ELBARA instrument at that location.

All satellite sensors, including the MIRAS radiometer on board SMOS, require calibration/validation (cal/val) activities in order to provide accurate data and products to the scientific community. For high brightness temperature values (i.e. higher than 150 K) measured by SMOS, the East Antarctic Plateau, in particular the area of Dome-C near the Italian-French base of Concordia, is being investigated as one of the most suitable long-term test sites.

Read more about the Concordia Station

The DOMEX installation on the observation tower at Concordia Station, Antarctica. RADOMEX is the white box placed about 1/3 up the tower (i.e. 13 m above the surface). Credit: M. Brogioni, PNRA, IFAC-CNR


Campaigns for SMOS: an overview DOMEX

Data > > >Calibration and Validation Campaign DOMEX Concordia Station

Data > >Calibration and Validation Campaign International Soil Moisture Network

Taylor diagram statistics comparing SMOS soil moisture products v6.20, v6.50 and D51 (internal experiment) to 119 concatenated ISMN cal/val sites in situ soil moisture time series for ascending orbits. SMOS v6.50 soil moisture product exhibits slightly improved correlations and reduced bias compared to v6.20. Credit: CESBIO

The International Soil Moisture Network (ISMN) is an international cooperation to set up and maintain a global in situ soil moisture database (Dorigo et al., 2011 and 2013). The ISMN was initiated in 2009 to support the calibration and validation of remote sensing products and land surface models, and to help study our climate’s behaviour through space and time. The ISMN does this by collecting and harmonising soil moisture datasets from a large variety of individually operating networks and making them available through a centralised data portal.The launch of SMOS in November 2009 led to the realisa-tion that an integrated and centralised system was needed to host quality controlled and consistent soil moisture measurements from various worldwide ground validation campaigns and networks. It was for this reason that ESA supported the development and first phase of operations of the ISMN, to be used to reliably calibrate and validate SMOS SM products. To date, more than 50 networks have become part of the ISMN, providing soil moisture and meteorological data from more than 2,100 stations, with the database running from 1952 to the present. Updated studies on the comparison of SMOS L2 SM data with ISMN datasets are available on the ISMN website.

Data > >Calibration and Validation Campaign Sea Ice campaign


Sea Ice campaign

Comparison with SMOS

Sea ice from the Polar-5 during the SMOSice campaign. Credit: Stefan Hendricks, AWI

Campaign (left) and SMOS (right) sea ice thicknesses (in metres). The campaign data consists of SEM (white lines), HEM (black lines) and ALS (red lines) measurements shown with a 12.5 km grid resolution. The SMOS plot shows the average thicknesses over the period 19-26th of March 2014. Credit: Kaleschke et al., 2016Table 1: Comparison results

From the comparison of EMIRAD-2 and SMOS brightness temperatures, a 10-15K bias was identified. SMOS SIT was also compared to SIT measurements from the ALS instrument. Both captured the gradient of the thick ice region well, however, the SMOS product underestimated the SIT by about 50% with respect to ALS (Hendricks et al., 2015). A further study compared the campaign data to SMOS measurements of SIT in more detail (Kaleschke et al., 2016). The field campaign Helicopter and Ship EM (HEM and SEM respectively) and Polar-5 ALS measurements were compared with SMOS L3 SIT products. Overall, SMOS underestimated SIT on average by about 50-60%. When the SMOS data were compared to the SEM data only, they agre-ed within 1 cm of the observed mean thickness, although the ship was limited to thin ice areas. SMOS and airborne data agree well over thin ice, however, the thicker deformed ice was substantially underestimated by SMOS, with a mean thickness difference of 30%. Therefore, further improvements of the SMOS retrieval algorithm are needed, in particular over deformed ice areas.

Data (Campaign data) SMOS data Mean DifferenceSMOS vs all data (SEM, HEM & ALS) 44 ± 36 cm 26 ± 19 cm -18 cm

SMOS vs airborne data (HEM & ALS) 65 ± 33 cm 31 ± 21 cm -33 cm

SMOS vs ship data (SEM) 17 ± 13 cm 18 ± 7 cm -1 cm

Data > >Calibration and Validation Campaign Access to Campaign Data

Screenshot of the campaign list on the Earth Online EO Campaigns Data webpage.

For more information about ESA’s Earth Observation (EO) campaigns, including the full campaign list, go to the Earth Online EO Campaigns Data webpage. This site, for each campaign, lists the year, the location, the datasets collected and the availability and size of the dataset. There is also a link to the final report describing the full campaign and presenting the results. A short, one page description of several recent campaigns can also be found on the ESA Living Planet Programme website Campaign webpage.

It is possible for users to access ESA EO campaign data. This can be done via the Principle Investigator (PI) Community Campaigns area of the ESA Earth Online website. To access campaign data, users are first required to register for a MyEarthnet account (link on the top right of all ESA Earth Online webpages) and then submit a data request by writing a short proposal (1 page maximum).

The campaign data proposal should include the title of the proposal, the details of the lead researcher or PI and a short description of how the user intends to use the data. Detailed guidelines for writing and submitting a request for ESA EO campaign datasets can be found at the PI Community Campaigns webpage link above. Once a request has been accepted by ESA, users will then have access to the Campaigns data portal to download their requested datasets or will receive a hard disk containing the datasets, in cases of large dataset requests.

Data > Radio Frequency Interferences

The MIRAS receiver chain is very selective, with a 3 dB bandwidth of only 19 MHz, with rejection above 30 dB at the edges of the 1400-1427 MHz purely passive band. Despite this sharp selectivity to protect measurements from unwanted emissions, the SMOS dataset is perturbed by Radio Frequency Interference (RFI) that jeopardises part of its scientific retrieval in certain areas of the world.

In the majority of cases, RFIs are caused either by unwanted emissions of radar systems, malfunctioning equipment working in the protected band or by illegal in-band emissions of radio-links, surveillance cameras and local terrestrial broadcasting systems. Man-made RFI emissions are typically stronger than the natural radiation emitted by the Earth.

Select from the following list to read more about: Effect of RFI on SMOS data Strategies to deal with RFI Successes and setbacks

SMOS observations during a descending orbit passage over Ukraine and the Black Sea on the 1st of June 2011. On the left is shown the brightness temperature on the antenna frame, for the vertical polarisation in the upper pane and for the horizontal polarisation in the bottom pane. On the right is the corresponding brightness temperature projected onto the Earth’s surface for all Ukrainian pixels. The effects of two strong RFI sources present in Ukraine can be observed.

Data > Radio Frequency Interferences > Effect of RFI on SMOS data

Effects of RFI in SMOS images are not restricted to the location of the emitter but extend to the entire image through the SMOS antenna’s side lobes.

Areas affected by RFI might experience data loss or underestimation of soil moisture and sea surface salinity retrieval values. Strong RFI emitters are responsible for di-rect loss of geophysical values as after data filtering no, or not enough, measurements remain to attempt any re-trieval. Weak RFI sources and secondary side-lobe conta-

mination emerging from the strong sources are the most detrimental as the retrievals are successful, but affected by unfiltered corrupted measurements.

A SMOS soil moisture map (left) acquired for the region of São Paolo, Brazil, during the four day period from the 1st to the 5th of June 2015. Soil moisture retrieval for black pixels was not attempted due to too many measurements being removed by RFI filtering. For grey pixels, the soil moisture retrieval was attempted and failed. Pockets of low soil moisture are a clear consequence of undetected RFI contamination. RFI probabilities (right) as derived from SMOS Level 2 data. The area with a RFI probability of above 10% corresponds very well with the anomalies in soil moisture map. Credit: ESA SMOS RFI Team

Data > Radio Frequency Interferences > Strategies to deal with RFI

ESA have put several strategies in place to alleviate the effects of RFI. These efforts have focussed on: ▪ Regular reporting of interference cases to the national

spectrum management authorities; ▪ Interference detection and data flagging processes; ▪ Increasing awareness of the RFI problem by supporting

initiatives to improve and reinforce the relevant regulatory frameworks.

Read more about: Regular reporting of harmful interference

RFI detection and flagging

International Telecommunications Union Radiocommunications (ITU-R) frequency allocation in the 1400-1427 MHz passive band and active services in adjacent bands. This passive band is allocated to Earth Exploration Satellites (passive), the Space Research (passive) Service and the Radio Astronomy service. Credit: ESA SMOS RFI Team

The evolution of the percentage of SMOS pixels over land affected by RFI for the first six years of the SMOS mission. About 13% of land pixels worldwide have been cleaned of RFI. Credit: ESA SMOS RFI Team.

1 400 MHz

1 360 1 370 1 380 1 390 1 400 1 410 1 420 1 430 1 440 1 450 1 460 1 470 1 480

Freq(MHz)

1 427 MHz

Fixed Service (only ITU Region 1)

Mobile Service (only ITU Region 1)

Radiolocation

Broadcasting-Satellite

Broadcasting

Fixed Service

Mobile Service

Earth ExplorationSatellite (passive)Space Research(passive) Service

Radio Astronomy

Space Ops(E-s)SO

After nine years of the SMOS mission, the overall RFI scenario has improved globally. However, difficult situations have occurred during the SMOS mission. In September 2011, most Japanese urban areas started showing RFI contamination due to Intermediate Frequency leakage in satellite TV home receivers following the activation of two new channels in the Ku-band broadcasting satellite service. This has

remained ever since, even though ESA is in contact with the Japanese administration and has asked them repeatedly to resolve this issue. In Poland in 2012, a single, very strong RFI blinded the instrument and caused the loss of SMOS data over central Europe for several months. The situation returned to nominal following investigations and contact with the Polish administration.

Noticeable improvements have been seen in the RFI situation in North America following the action of the Canadian Spectrum Management Authorities. The top panels show the RFI probability map over North America as derived from SMOS L2 products in May 2011 (left) and May 2012 (right). The bottom panels show the retrieved sea surface salinity for the same period for most of the northern hemisphere. The May 2011 image (left) clearly suffers from RFI and exhibits unrealistic low sea surface salinity on the northern coast of Canada and in the sea north of Europe. Credit: ESA SMOS RFI Team.

Data > Radio Frequency Interferences > Successes and Setbacks

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