Lessons learnt from SMOS after 7 years in orbit

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.

In microwave interferometric radiometers with a large field of view, as for example the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite, one of the major causes of reconstruction error is the contribution to the visibility of the brightness temperature outside the fundamental period, defined on the basis of reciprocal grids. A mitigation method consisting of estimating this contribution through the application of a brightness temperature model outside the fundamental period is proposed. The main advantage is that it does not require any a posteriori addition of artificial scenes to the reconstructed image. Additionally, a method to avoid the sophisticated matrix regularization and inversion techniques usually applied in microwave interferometry is presented. Image reconstruction algorithms are implemented on a minimum grid size in order to maximize their numerical efficiency. An improved method to apply an apodization window to the reconstructed image for reducing Gibbs oscillations is also proposed. All procedures are generally described considering the single polarization case and successively implemented applying the MIRAS layout in both its single polarization and full polarimetric modes. Results show similar performance of the proposed algorithm with respect to the nominal one applied by SMOS. All algorithms are implemented in the MIRAS Testing Software and have been successfully used for scientific studies by other teams.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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

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 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> 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.

This paper presents the Soil Moisture and Ocean Salinity (SMOS) mission data assimilation activities conducted at the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyse soil moisture for Numerical Weather Prediction (NWP) applications. Two different approaches are presented based on SMOS brightness temperature and SMOS neural network soil moisture data assimilation, respectively. For the first approach, SMOS brightness temperature data assimilation relies on forward modelling. Long term results, spanning the SMOS period, of SMOS forward modelling, monitoring and data assimilation are presented. They emphasize the relevance of SMOS data for monitoring and to support NWP model developments. For the second approach, a SMOS soil moisture product has been produced based on a Neural Network (NN) trained on ECMWF soil moisture. So, the SMOS-ECMWF NN soil moisture product captures the SMOS signal variability in time and space, while by design its climatology is consistent with that of the ECMWF soil moisture, which makes it suitable for data assimilation purpose. This approach, initially tested for 2012 in a global scale stand alone approach, shows that SMOS NN data assimilation slightly improves the two-metre air temperature forecast in the short range at regional scale. For NWP applications this approach has been further developed with a near real time production of the SMOS-ECMWF NN soil moisture product, with the implementation of the SMOS NN data assimilation in the ECMWF Integrated Forecasting System (IFS), and with high resolution (9km) global scale testing compatible with the current ECMWF NWP system.

The recently developed Spherical Wave Expansion-to-Plane Wave Expansion (SWE-to-PWE) antenna diagnostics technique is employed in an investigation of the antenna system in the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) for ESA's Soil Moisture and Ocean Salinity (SMOS) mission. The SWE-to-PWE antenna diagnostics technique successfully identifies the sources of the anomalies detected in 2 of the 138 MIRAS antenna far- field patterns that were measured during the on-ground calibration at the DTU-ESA Spherical Near-Field Antenna Test Facility in 2006. In addition to its obvious value for the SMOS mission, this investigation also provides an experimental validation of the SWE-to-PWE antenna diagnostics technique. (6 pages)

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.

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

Comparison of SMOS and AMSR-E vegetation optical depth to four MODIS-based vegetation indices

Three flight models (FMs) of the reference radiometer of the Soil Moisture and Ocean Salinity (SMOS) mission have been developed and tested. SMOS is a joint mission of the European Space Agency, Centre National d'Etudes Spatiales of France, and Centre for the Development of Industrial Technology of Spain. The reference radiometer is a noise injection radiometer (NIR); the NIR subsystem FM has been developed by Elektrobit Microwave, Ltd., in collaboration with the Laboratory of Space Technology of Helsinki University of Technology, which has acted as a subcontractor. The NIR subsystem will be integrated into the microwave imaging radiometer using aperture synthesis (MIRAS) payload in 2006. MIRAS will be the sole instrument onboard the SMOS satellite. MIRAS has 66 total power receiver units (light and cost-effective front end) and three NIR units. The purpose of the NIR subsystem is 1) to provide precise measurement of the average brightness temperature scene for absolute calibration of the MIRAS image map, 2) to measure the noise temperature level of the internal active calibration sources of MIRAS [referred to as the calibration subsystem (CAS)], and 3) to form interferometer baselines, so-called mixed baselines, with the regular receiver units. The performance of the NIR is a decisive factor of overall MIRAS performance. In this paper, we present the design solutions for the NIR FMs, which enable the achievement of the mission goals set for the NIR subsystem. The results of the NIR test campaign, proving that the performance and environmental requirements are fulfilled, are also presented, and the outcome of the ground calibration campaign is analyzed. Furthermore, the orbital calibration scheme is depicted; the calibration scheme enables the NIR to measure its targets with precision.

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.

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 ESA Earth Explorer SMOS (Soil Moisture and Ocean Salinity) mission will carry the first polar-orbiting 2-D interferometric radiometer (MIRAS- Microwave Imaging Radiometer using Aperture Synthesis) acquiring data of emitted microwave radiation at the frequency of 1.4 GHz (L-band). The SMOS mission products will provide different data types, each with its own characteristics. SMOS products are classified according to the different levels of processing and each product will contain specific data. The aim of this paper is to highlight the innovative satellite instrument and to present to the scientific user community the content of each product type.

&amp;lt;p&amp;gt;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.&amp;lt;br&amp;gt;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&amp;amp;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.&amp;lt;br&amp;gt;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.&amp;lt;br&amp;gt;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.&amp;lt;/p&amp;gt;