Radio-Frequency Interference Detection and Mitigation Algorithms for Synthetic Aperture Radiometers

Sea surface salinity retrievals from HUT-2D L-band radiometric measurements

Measurements of both the Soil Moisture and Ocean Salinity (SMOS) and Aquarius L-band microwave radiometers show a significant presence of radio frequency interference (RFI), although they operate in a protected frequency band where transmission is prohibited. RFI detection and mitigation remain a challenging problem for both missions, especially for low or moderate (i.e., on the order of 10 K or less) amplitude contributions. An algorithm for low-level source detection and mitigation is already included in Aquarius data sets, and both Aquarius and SMOS have distinct attributes that can potentially enable further improvements in detection and mitigation of these sources to some degree. The combination of SMOS and Aquarius data sets may enable further future improvements as well. Initial efforts toward this goal are reported in this paper. Similarities and differences in RFI effects on SMOS and Aquarius are examined, with a particular focus on instrument properties that cause differences in received RFI power in SMOS and Aquarius observations of a specific source. A study is also performed of SMOS observations for regions reported by Aquarius to contain “low-level” RFI. It is shown that the detection of these sources in the SMOS data set is challenging and that the dependence of the SMOS third and fourth Stokes parameters on incidence angle makes the polarimetric features of SMOS difficult to utilize for low-level source detection. However, an angular fitting procedure suggested previously in the literature can, in some cases, detect such sources in horizontal and vertical polarizations.

Abstract. The sea surface salinity (SSS) measured from space by the Soil Moisture and Ocean Salinity (SMOS) mission has recently been revisited by the European Space Agency first campaign reprocessing. We show that, with respect to the previous version, biases close to land and ice greatly decrease. The accuracy of SMOS SSS averaged over 10 days, 100 × 100 km2 in the open ocean and estimated by comparison to ARGO (Array for Real-Time Geostrophic Oceanography) SSS is on the order of 0.3–0.4 in tropical and subtropical regions and 0.5 in a cold region. The averaged negative SSS bias (−0.1) observed in the tropical Pacific Ocean between 5° N and 15° N, relatively to other regions, is suppressed when SMOS observations concomitant with rain events, as detected from SSM/Is (Special Sensor Microwave Imager) rain rates, are removed from the SMOS–ARGO comparisons. The SMOS freshening is linearly correlated to SSM/Is rain rate with a slope estimated to −0.14 mm−1 h, after correction for rain atmospheric contribution. This tendency is the signature of the temporal SSS variability between the time of SMOS and ARGO measurements linked to rain variability and of the vertical salinity stratification between the first centimeter of the sea surface layer sampled by SMOS and the 5 m depth sampled by ARGO. However, given that the whole set of collocations includes situations with ARGO measurements concomitant with rain events collocated with SMOS measurements under no rain, the mean −0.1 bias and the negative skewness of the statistical distribution of SMOS minus ARGO SSS difference are very likely the mean signature of the vertical salinity stratification. In the future, the analysis of ongoing in situ salinity measurements in the top 50 cm of the sea surface and of Aquarius satellite SSS are expected to provide complementary information about the sea surface salinity stratification.

The SMOS(Soil Moisture and Ocean Salinity) Mission was selected in May 1999 by the European Space Agency to provide global and frequent soil moisture and sea surface salinity maps. SMOS' single payload is MIRAS(Microwave Imaging Radiometer by Aperture Synthesis), an L-band 2D aperture synthesis interferometric radiometer with multiangular observation capabilities. To achieve the ultimate goal of 0.1 psu error in ocean salinity retrievals, most studies assume the independence of measurements both in time and space so that the standard deviation of the retrieval errors decreases with the inverse of square root of the number of measurements being averaged. However, biases in the brightness temperature images originated from errors in the noise injection radiometers. Sun contributions to the antenna temperature, and imaging under aliasing conditions need to be compensated before attempting to retrieve the sea surface salinity. This work presents an external calibration suitable to perform salinity retrievals with an acceptable error. This study has been performed using the SMOS End-to-end Performance Simulator(SEPS) including thermal noise, all instrumental error sources, current error-correction and image reconstruction algorithms, and correction of atmospheric and sky noises. Simulation results show a retrieved salinity error of /spl sim/1 psu in one overpass over open ocean, which can be reduced afterwards by spatio-temporal averaging, much better than the -5 psu error that appears when image biases are not corrected.

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.

Soil moisture is an important hydrologic variable due to its influence on plant growth and the surface energy budget. Most current soil moisture networks measure soil moisture at single points; these point measurements of soil moisture may not relate to the soil moisture of the larger, surrounding areas. Crop models and weather and climate models, which are sensitive to soil moisture, could be improved by having accurate soil moisture data that is relevant at scales larger than a point. Microwave C–band and X–band sensing satellites have attempted to retrieve soil moisture at relevant, large spatial scales needed for models, but these satellites are limited in their sensing depth, sensitivity to vegetation, and sensitivity to scattering by precipitation. Two recent satellite missions, the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission and NASA’s Soil Moisture Active Passive (SMAP) mission, retrieve soil moisture from L–band measurements of radiation, which improve upon the limitations of previous satellites. The retrieval of soil moisture from microwave satellite measurements requires knowledge of the vegetation optical thickness (τ), which is related to the vegetation water content (VWC). The multi-incidence angular design of SMOS provides its retrieval algorithm the advantage of retrieving soil moisture and τ simultaneously. SMAP will measure at a single incidence angle and will require an ancillary source of τ data. The current plan is for SMAP to use a climatology of normalized difference vegetation index (NDVI) data to generate a τ climatology. This dissertation analyzed this SMAP τ climatology by first validating SMOS τ and then by comparing SMOS τ to SMAP τ . The validation of SMOS τ was done by comparing SMOS τ during the growing season to crop yield estimates for each Iowa county. The result was that larger values of SMOS τ were correlated with larger crop yields, which suggested that SMOS τ is related to the growth of vegetation. SMOS τ was also found to be noisy and to behave strangely outside of the growing

<p>During the last 3<span> dec</span><span>ades, the Arctic rivers have increased their discharge around 10%, mainly due to the increase of the</span> <span>g</span><span>lobal atmospheric </span>temperature. The increase of the river discharge carries higher loads of dissolved organic matter (DOM) and suspended matter (SM) entering to the Arctic Ocean. This results in increased absorption of solar energy in the mixed layer, which can potentially contribute to the general sea ice retreat. Observation based studies (e.g. Bauch et al., 2013) showed correlation between river water discharge and local sea ice melting on the Laptev sea shelf due to the change on the ocean heat. Previous studies are based with a limited number of observations, both in space and in time.</p><p>Thanks to the ESA SMOS (Soil Moisture and Ocean Salinity) and NASA SMAP (Soil Moisture Active Passive) missions we have daily the sea surface salinity (SSS) maps from the Arctic, which permit to observe the salinity variations due to the river discharges. The Arctic sea surface salinity products obtained from SMOS measurements have been improved considerable by the Barcelona Expert Center (BEC) team thanks to the project Arctic+Salinity, funded by ESA. The new version of the product (v3) covers the years from 2011 up to 2018, have a spatial resolution of 25km and are daily maps with 9 day averages. The Arctic+ SSS maps provide a better description of the salinity gradients and a better effective spatial resolution than the previous versions of the Arctic product, so the salinity fronts are better resolved. The quality assessment of the Arctic+SSS product is challenging because, in this region, there are scarce number of in-situ measurements.</p><p>The high effective spatial resolution of the Arctic+ SSS maps will permit to study for the first time scientific physical processes that occurs in the Arctic. We will explore if a correlation between the Lena and Ob rivers discharge with the sea ice melting and freeze up is observed with satellite data, as already stated with in-situ measurements by Bauch et al. 2013. Salinity and sea ice thickness maps from SMOS and sea ice concentration from OSISAF will be used in this study.</p><p> </p><p>Bauch, D.,Hölemann, J. , Nikulina, A. , Wegner, C., Janout, M., Timokhov, L. and Kassens, H. (2013): Correlation of river water and local sea-ice melting on the Laptev Sea shelf (Siberian Arctic) , Journal of Geophysical Research C: Oceans, 118 (1), pp. 550-561 . doi: 10.1002/jgrc.20076</p>

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

An important contribution of satellite Sea Surface Salinity (SSS) is the spatio-temporal monitoring of rivers fresh water plumes at mesoscale. In this paper, we detail a new correction for systematic errors in the Soil Moisture and Ocean Salinity (SMOS) measurements that is implemented in the Centre Aval de Traitement des Donnees SMOS (CATDS). With this new mitigation, the SMOS and Soil Moisture Active Passive (SMAP) SSS monitor very consistent features in most areas close to continents. The rms-difference between bi-weekly SMOS and SMAP SSS over 20 months and in selected coastal regions is about 0.3pss (once outliers are filtered out), rather consistent with the rms-difference between satellite and in situ SSS (on the order of 0.2pss). The coefficient of determination (r2) between SMOS and SMAP SSS is above than 0.8 in very fresh areas (river plumes). Over the open ocean, the rms difference between SMOS and ship SSS is 0.2pss.

Capability for sea surface salinity observation was an important gap in ocean remote sensing in the last few decades of the 20th century. New technological developments during the 1990s at the European Space Agency led to the proposal of SMOS (Soil Moisture and Ocean Salinity), an Earth explorer opportunity mission based on the use of a microwave interferometric radiometer, MIRAS (Microwave Imaging Radiometer with Aperture Synthesis). SMOS, the first satellite ever addressing the observation of ocean salinity from space, was successfully launched in November 2009. The determination of salinity from the MIRAS radiometric measurements at 1.4 GHz is a complex procedure that requires high performance from the instrument and accurate modelling of several physical processes that impact on the microwave emission of the ocean’s surface. This paper introduces SMOS in the ocean remote sensing context, and summarizes the MIRAS principles of operation and the SMOS salinity retrieval approach. It describes the Spanish SMOS high-level data processing centre (CP34) and the SMOS Barcelona Expert Centre on Radiometric Calibration and Ocean Salinity (SMOS-BEC), and presents a preliminary validation of global sea surface salinity maps operationally produced by CP34.

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, launched in 2009, provides global measurements of Sea Surface Salinity (SSS) using L-band radiometry. In this paper, we revisit the algorithms to empirically correct the residual latitudinal and seasonal biases seen in the Debiased non-Bayesian (DNB) retrieval algorithm. We characterize these biases that affect the retrieved SSS and derive empirical corrections to mitigate them. We also revisit the filtering criteria for the new release of Brightness Temperature (v724). We compare the SSS retrieved by using different DNB filtering configurations with in-situ Argo measurements and discuss the importance of these corrections and the optimal configuration parameters for the generation of the Level 3 SSS maps. We observe significant improvements in the retrieved SSS when compared to the ones retrieved in the Barcelona Expert Center global SSS product v2

In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.

Considering combined or separated roughness and vegetation effects in soil moisture retrievals

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 <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> of the selected node. Data is stepwise compared from point via EMIRAD to SMOS scale. From ground soil moisture samples, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's are pointwise estimated through the L-band microwave emission of the biosphere model using land cover specific model settings. These <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> 's are patchwise averaged and compared with EMIRAD <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> '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 <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> '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 <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TB</i> '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.

Radio-frequency interference (RFI) has negatively impacted scientific measurements of passive remote sensing satellites. This has been observed in the L-band radiometers Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) for the Soil Moisture and Ocean Salinity (SMOS) mission, Aquarius and more recently, Soil Moisture Active Passive (SMAP). RFI has also been observed at higher frequencies such as K band. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A radio frequency interference detector based on complex signal kurtosis was developed to help identify corrupted measurements. This work explores the use of Independent Component Analysis (ICA) as a blind source separation (BSS) technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.