This study addresses discerning causal relationships in complex systems, a key aspect of interpretable machine learning. It focuses on the unusual and intense early summer weather in South Asia during April and May 2022 that led to an increased number of forest fires. This work employs a Bayesian network (BN), constructed using the NOTEARS algorithm, to analyse the contribution of various land and atmospheric variables on the extent of burned areas. In a scenario analysis using peak values of 300-hPa meridional circulation index and 500-hPa Geopotential Height Anomalies, indicative of a strong atmospheric block, the likelihood of large burned areas (>3.06 log ha or >1150 ha) increases from 36.6% to 41.6%. This is due to a rise of conditional probabilities in the Vapor Pressure Deficit (VPD) (> 5.21 kPa) by 24.8%, and the Land Surface Temperature (LST) (>45.7°C) by 15.6%. In addition, sensitivity and spatial analyses indicate that extreme dry conditions, characterized by high LST and VPD due to the trapping effects of the omega block jet stream pattern, were the primary factors influencing the extent of burned areas during the 2022 South Asia heatwave.

The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum.

This study presents a methodology for disaggregating the ESA Climate Change Initiative (CCI) Soil Moisture (SM) maps from 0.25° to a 60 m grid, using a feedforward neural network. This technique is applied over an area of 66,700 km2, encompassing parts of Oklahoma and Kansas (US), throughout 2021. The disaggregation approach leverages synergies between different variables, including various Sentinel-2 bands and indices, land surface temperature from MODIS, accumulated precipitation from ERA5-Land, terrain elevation and slope from the STRM, and soil composition. The methodology employs a two-step process: (i) the model is first trained using all the variables at low resolution (0.25°), and (ii) it is then employed to estimate the SM at high resolution using the input variables at 60 m. Results indicate that the model trained at low resolution achieves a reasonably high accuracy over the testing data (RMSE3•m-3and R2>0.9). The preliminary analysis of the 60 m resolution SM maps and their comparison with two in-situ stations show a strong correlation (R>0.8), and uRMSE close to 0.04 m3•m-3, with a bias ranging from 0.022 m3•m-3to 0.06 m3•m-3.

In the work a random forest model has been implemented as an interpretable machine learning tool in the effort to estimate the burned areas caused by fire outbreaks in India, Pakistan, and Myanmar in April and May 2022. The proposed model combines environmental and atmospheric (including upper tropospheric) factors suggested to drive patterns of burned areas, and determines the weight of each factor on the propagation of fires. Results demonstrate that the model mimics the actual burned area by considering a combination of vegetation, atmosphere, and human-related variables and improves accuracy by approximately 7% after adding jet stream features. This approach could lead to implement a semi-operational forecast system that may be tested in multiple demonstration sites.

A modification of the Barcelona Expert Center (BEC) algorithm to downscale the Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) to 300 m spatial resolution is presented. It maintains the same functional relationship as the currently implemented version but employs the following inputs: SMOS brightness temperature (TB) and SM (25 km), European Center for Medium Weather Forecast (ECMWF) skin temperature (9 km), and Sentinel 3 Normalized Difference Vegetation Index (NDVI, 300 m).The performance of the downscaled SMOS SM at 300 m is analyzed by means of a temporal validation with in-situ observations from the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) and the Continuous Soil Moisture and Temperature Ground-based Observation Network (RSMN) during the year 2021. No significant differences in correlation, unbiased root mean square difference (ubRMSD) and bias are obtained over both networks compared to the 25 km and 1 km SM products, suggesting the BEC downscaling algorithm could work at hundreds of meters and result in a similar SM accuracy.

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m3•m-3), demonstrating the method’s significant potential for deriving high-resolution soil moisture information.

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the Single Channel Algorithm (SCA) is first applied to SMOS brightness temperatures at vertical polarization (TBV) to estimateSMat coarse-resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of single scattering albedo (ω) and soil roughness (hs) parameters. These effective parameters are used together with fine-scale multi-angular Sentinel-1 backscatter in a single-pass active-passive downscaling approach to estimateTBVat fine-scale (1 km) for each SMOS incidence angle. TheseTBVare finally inverted to obtain the corresponding high-resolutionSMmaps. Results over the Iberian Peninsula for year 2018 show an increasing trend of ω and a decreasing trend ofhswith SMOS incidence angle, with almost no variability of ω across land cover types. The active-passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fineTBVmaps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolutionSMmaps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well within situmeasurements. Our results indicate that model-based microwave approaches to estimateSMcan be adequately adapted to account for the incidence angle diversity of planned missions such as CIMR, ROSE-L and Sentinel-1 next generation.

FSSCat, the “Federated Satellite Systems/3Cat-5” mission was the winner of the 2017 ESA S^3 (Sentinel Small Satellite) Challenge and overall winner of the Copernicus Masters competition. FSSCat consists of two 6 unit cubesats carrying on board UPC's Flexible Microwave Payload - 2 (FMPL-2), an L-band microwave radiometer and GNSS-Reflectometer implemented in a software defined radio, and Cosine's HyperScout-2 visible and near infrared + thermal infrared hyperspectral imager, enhanced with PhiSat-1, a on board Artificial intelligence experiment for cloud detection. Both spacecrafts include optical and UHF inter-satellite links technology demonstrators, provided by Golbriak Space and UPC, respectively. This paper describes the mission, and the main scientific results of the FMPL-2 obtained during the first three months of the mission, notably the sea ice concentration and thickness, and the downscaled soil moisture products over the Northern hemisphere.

Knowledge about sea ice concentration and extent in polar regions is of great interest both for economic interests, and as a proxy of the climate change. Retrieved maps are based on data from microwave radiometers, which are currently provided by large satellite missions. Nowadays, CubeSats have proven to be a cost-effective alternative. Due to their low cost, they can be launched in large constellations to obtain high spatial coverage and daily revisit. This study presents a neural network approach to generate sea ice concentration and sea ice extension maps using the L-band microwave radiometer, and the GNSS-Reflectometer data from the FMPL-2 instrument onboard3Cat-5/A, one of the two CubeSats of the FSSCat mission. The results obtained during the first 2 months of the mission are presented.

This work presents the first scientific results over land from the Flexible Microwave Payload −2 (FMPL-2), onboard the FSSCat mission. FMPL-2 is composed of an L-band microwave radiometer and a Global Navigation Satellite System - Reflectometer (GNSS-R). Two separate ANNs models are trained using the first three months of collected data of both observations, with the objective to retrieve global soil moisture maps. The first network addresses the coarsely-resolved FMPL-2 antenna footprint in a downscaling approach. Predicted values resulted in good agreement with those obtain from the SMAP mission, with an error smaller than 9.6%, and a bias smaller than 0.001 m3/m3. The second network is implemented to estimate soil moisture exclusively on GNSS-R data. In this second case, the combination of multiple GNSS-R measurements in a single track allows to retrieve soil moisture data with an error standard deviation with respect to SMAP lower than 0.056 m3/m3, with a bias smaller than 0.0007 m3/m3.

The novel Correlated Triple Collocation (CTC) analysis allows to assess three different data sources of similar spatial resolutions, but with two of them being correlated. In this study, the CTC was applied to estimate the unbiased random errors of the global soil moisture (SM) data provided by two L-band satellite missions —the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP)— and one numerical model—the ERA5-Land. The three existing SMOS SM products distributed by different research institutions were also analyzed. Preliminary results revealed that errors of SMOS and SMAP SM are correlated, with correlations of ∼0.5-0.6. Thus, only ERA5-Land can be considered as independent. The lowest error was obtained for SMAP (0.025 m3m−3), followed by ERA5-Land (0.036 m3m−3). Among the SMOS SM, SMOS-IC had the lowest error (0.046 m3m−3), SMOS-BEC showed an intermediate value (0.048 m3m−3), and SMOS-CATDS had the highest error (0.055 m3m−3).

Incidence angle diversity of space-borne L-band radiometers needs to be taken into account for a consistent estimation of surface soil moisture (SM). In this study, the Land Parameter Retrieval Model (LPRM) is applied to SMOS brightness temperatures to calibrate the effective scattering albedo (w) and the soil roughness (h1) parameter against ERA5-land SM. The analysis is carried out for SMOS data at three different incidence angles ($32.5\pm 5^{\circ},\ 42.5\pm 5^{\circ}$and$52.5\pm 5^{\circ}$) focusing in 2016 on the three main land cover types of the Iberian Peninsula according to the Climate Change Initiative (agricultural, forest and grassland). The parameterization shows an increasing trend of w and h1with rise of incidence angle. The SM retrieval have been evaluated with in situ SM measurements of the REMEDHUS network on rainfed crop fields. Both compare well at the three incidence angles, obtaining high correlations (0.81-0.85), an ubRMSE around 0.04 m3m−3and low bias (0-0.015 m3m−3).

Unaffected by the atmospheric conditions and solar illumination, L-band emission and scattering are sensitive to vegetation water content and can be used to estimate crop yield. However, for rice which has an inundated period during its growing cycle, the current methods do not work due to the water under the crops. In this paper, we propose to use Global Navigation Satellite System Reflectometry (GNSS-R) signals to find how the water in rice field influence vegetation optical depth (VOD) which had been recently used to estimate the crop yield. Soil moisture (SM) and VOD in Thailand rice fields are compared to signal to noise ratio (SNR) from CYGNSS. Good correlation among them has been found. Results indicate that GNSS-R signals can be used to flag the presence of water and develop an adapted VOD algorithm that can be used to improve the estimation of rice yields.

Global Soil Moisture and Ocean Salinity (SMOS) Level 3 (L3) soil moisture (SM) products are being routinely distributed by the Barcelona Expert Centre (BEC). The quality and accuracy of these SM products have been demonstrated not only by direct validation, but also by its adoption in a wide range of applications. Recently, changes in SMOS Level 2 (L2) SM have led to the reprocessing of the BEC SMOS L3 SM. As in previous versions, a filtering and a weighted binning based on the uncertainty of the SM retrievals by means of the Data Quality Index (DQX) was applied for the L3 production. However, the DQX was modified in the latest L2 release (v650), which could possibly have an influence in the performance of the derived products.This study assesses the impact of the current DQX-based BEC L3 SM quality filtering and binning approach and the possibility of using an alternative strategy based on the chi-squared (χ2) parameter, which is defined as the cost function of the retrieval. The study is performed over continental USA using in situ SM from the U.S. Climate Reference Network (USCRN) as a benchmark. In both approaches, similar results were obtained in terms of correlation and unbiased root mean square difference (ubRMSD). Nevertheless, the χ2-based L3 SM is in general slightly wetter and has a lower dry bias than the DQX-based L3 SM. Further assessments will be performed to stablish the optimal filtering/binning of BEC SMOS L3 SM products.

This research presents a comparison between three different root zone soil moisture (RZSM) estimations obtained through satellite with in situ measurements from the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS). The first product analyzed was the Soil Moisture Active Passive (SMAP) L4 RZSM product. The second satellite product was the surface soil moisture (SSM) of the Soil Moisture and Ocean Salinity (SMOS) mission, in which the Soil Water Index (SWI) model was applied to retrieve RZSM. Finally, the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to retrieve SSM using visible/infrared observations and the apparent thermal inertia (ATI) algorithm. The derived SSM from the ATI was then transformed into RZSM through the SWI. The statistical results showed similar performance for the three products. However, the good results based on the MODIS ATI must be highlighted since this methodology is only based in thermal imagery.

L-band radiometry has been considered the preferred technique for global soil moisture (SM) remote sensing, as the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions proved. Owing the limited spatial resolution of current L-band radiometers, several downscaling algorithms have been developed to enhance the SMOS and SMAP SM resolutions. In this regard, the apparent thermal inertia (ATI) may be used for indirectly estimating SM, not only for disaggregation. In this study, l-km ATI-derived SM was obtained from March 31, 2015 to December 31, 2016, using Moderate Resolution Imaging Spectroradiometer (MODIS) as an alternative to L-band radiometry. The ATI-derived SM was validated over the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS). Comparisons with in situ SM showed slightly lower correlation (~0.62), but similar error (~0.043 m3·m-3) and lower bias (~0.027 m3·m-3) than the existing SMOS and SMAP products, whilst improving the resolution.

After more than 8 years in orbit the Soil Moisture and Ocean Salinity (SMOS) satellite is still in good health and several algorithms for improving its spatial resolution have been proposed and validated in a variety of catchments. However, none of them has yet been applied at the global scale. In this article we present: i) a review of the latest SMOS-BEC downscaling algorithm, which allows for its global application using an adaptive moving window and ii) a thorough validation of the resulting maps over two in-situ networks: REMEDHUS in Spain and OzNet in Australia. The proposed algorithm combines SMOS brightness temperatures (at ~40 km spatial resolution), and MODIS-derived Land Surface Temperature and Normalized Differenced Vegetation Index (at 1 km), into 1km soil moisture maps. This paper also presents a variant of the algorithm, which allows for cloud-free retrievals. A statistical comparison has been carried out when the MODIS Land Surface Temperature is replaced in the algorithm by the one provided by the ERA5 reanalysis. Fine-scale estimates show good agreement in terms of correlation and root-mean-squared error with in-situ soil moisture.

Whereas satellite soil moisture provides global and periodic coverage of the Earth's surface with coarse to medium spatial resolution, the in situ measurements are discrete, scattered and punctual. In this work, ground soil moisture measurements from the the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) are upscaled following different approaches to match the observations provided by the Soil Moisture and Ocean Salinity (SMOS) mission, with tens km of spatial resolution. Each method led to a representative value of the soil moisture network used to validate the SMOS data. The results of the validation through the different methods showed that all of them performed in a similar way, similar in turn to the simple average.

The European Space Agency's Climate Change Initiative (CCI) soil moisture (or Essential Climate Variable soil moisture) long-term database was used to assess the agricultural drought evolution. With this aim, the Soil Water Deficit Index (SWDI) was calculated with the CCI soil moisture product over the agricultural area of the Zamora province (Spain) from 1978 to 2014. The SWDI was compared with other climate-based agricultural drought indices. The results of SWDI with CCI were also compared with those obtained with two soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) satellite. The results obtained are very promising and pave the way for using this new long-term soil moisture database as a useful tool for agricultural drought monitoring.

The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improving the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm is proposed for retrieving high resolution (1 km) SM. This model is an extension of the “universal triangle” technique, and also introduces the concept of adaptive moving window. Its inputs are the low resolution SMOS BEC L3 SM and the brightness temperatures at vertical and horizontal polarizations (SMOS L1C), and the high resolution NDVI and LST from optically-based sensors. The proposed method allows obtaining high resolution SM maps worldwide, with no limitation in extension.

An application of the Soil Moisture Agricultural Drought Index (SMADI) for global agricultural drought monitoring is presented. The index integrates surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and allows for global drought monitoring at medium spatial scales (0.05°). Biweekly maps of SMADI were obtained from year 2010 to 2015 over all agricultural areas on Earth. The SMADI time-series were compared with state-of-the-art drought indices over the Iberian Peninsula. Results show a good agreement between SMADI and the Crop Moisture Index (CMI) retrieved at five weather stations (with correlation coefficient, R from -0.64 to -0.79) and the Soil Water Deficit Index (SWDI) at the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) (R=-0.83). Some preliminary tests were also made over the continental United States using the Vegetation Drought Response Index (VegDRI), with very encouraging results regarding the spatial occurrence of droughts during summer seasons. Additionally, SMADI allowed to identify distinctive patterns of regional drought over the Indian Peninsula in spring of 2012. Overall results support the use of SMADI for monitoring agricultural drought events world-wide.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multi-static radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from a ground-based and airborne experiments, but studies using space-borne data are still preliminary. This work presents a sensitivity study of Using TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e. vegetation covers). Despite the scattering in the data, which can be attributed to the temporal and spatial (footprint size) collocation mismatch with the SMOS and MODIS NDVI data, and errors in the land use data preliminary results show a good correlation with soil moisture.

The Light Airborne Reflectometer for GNSS-R Observations (LARGO) is an airborne instrument designed for measuring the coherent reflectivity from different soils. In this work, an improved version of LARGO has been used in a field campaign together with other conventional remote sensing instruments. All the corrections made to the raw coherent reflectivity including the antenna pattern compensation, and a topographic correction are presented. The correlation between corrected coherent reflectivity and in situ soil moisture was not high enough due to the dry conditions of the field campaign.

The Dome-C region, in the East Antarctic Plateau, has been used for calibration/validation of satellite microwave radiometers since the 1970's. However, its use as an independent external target has been recently questioned due to some spatial inhomogeneities found in L-band airborne and satellite observations. This work evidences the influence of the Antarctic ice thickness spatial variations on the measured SMOS and Aquarius brightness temperatures (TB). The possible effects of subglacial water and bedrock on the acquired radiometric signals have also been analyzed. A 3-months no-daylight period during the Austral winter has been selected. Four transects over East Antarctica have been defined to study the spatial variations. A good agreement between SMOS and Aquarius TBchanges and ice thickness variations over the whole Antarctica has been observed, obtaining linear correlations of 0.6-0.7 and slopes of 8.6-9.5 K/km. The subglacial lakes may affect the vertical physical temperature profile and/or the dielectric properties of the ice layers above. As expected, the subglacial bedrock is not contributing to the measured TB, since the maximum estimated L-band penetration depth is ~1-1.5 km.

New remote sensing techniques based on the analysis of the Earth's surface-reflected signal from the Global Navigation Satellite Systems (GNSS-Reflectometry, or GNSS-R in short) are emerging. Soil moisture and vegetation status are some of the potential parameters that could be also retrieved from these sources. However, the complex interactions between the soil-vegetation interface can lead to spurious effects on the reflected signal. In order to study these effects, an airborne campaign was developed in an experimental area in Spain in August, 2014. A new GNSS-R-based instrument was flown together with thermal and optical cameras mounted on a paramotor. Ground measurements of soil moisture were taken during the flight. Maps at very high spatial resolution of reflectivity, Land Surface Temperature (LST) and the Digital Surface Model (DSM) were jointly analyzed, together with the ground observations. The results showed an important influence of the topography (i.e., the local incidence angle) on the GNSS-R reflectivity, and promising patterns relating reflectivity with soil moisture and LST were found. However, owing the dry soil and weather conditions during the experiment, further tests are needed over different environment and climatic conditions.

Surface Soil Moisture (SSM) affects the soil surface energy balance and thus affects the Land Surface Temperature (LST), and viceversa. Currently, LST and SSM are remotely sensed using TIR sensors and L-band radiometers, respectively. The NASA's Terra/Aqua missions provide full coverage of LST measurements under clear sky conditions using MODIS. The ESA's SMOS mission is the first satellite providing frequent SSM and ocean salinity observations at global scale. In this paper, a sensitivity study about the relationship of the LST and SSM is performed using in-situ measurements from the REMEDHUS network and spaceborne observations from MODIS and SMOS. Results show that the correlation between SSM and LST (both in-situ and remotely sensed) is highest using the daily maximum LST. This could help improving SSM algorithms and deriving new SSM products at higher resolution from the synergy of microwave and TIR observations.

The spectral window at L-band (1.400 - 1.427 GHz) is reserved for passive microwave remote sensing. This band is well-suited to retrieve soil moisture and ocean salinity due to emissivity of soil and seawater decreases with moisture and salinity, respectively, affecting microwave radiation of the Earth's surface. Nowadays, there are two space missions devoted to Earth observation with L-band radiometers on-board: the SMOS mission from the ESA and the Aquarius/SAC-D mission from the NASA and CONAE. Both missions are providing the first TB measurements of the Earth's surface at 1.413 GHz. Thus, it is a great opportunity to compare SMOS and Aquarius TBs and verify the continuity and consistency of the data. This inter-comparison is a key requirement needed to use data of both radiometers for meteorological, hydrological and climatological studies on a long term.

This paper presents a comparative study of the radiometric sensitivity and spatial resolution of three near-field (NF) passive screener systems: real aperture, 1-D synthetic aperture (SA), and 2-D SA radiometers are compared. The analytical expressions for the radiometric resolution, the number of required antennas, and the number of pixels in the image are derived taking into account the distortion produced by the NF geometry at nonboresight directions where the distortion is dominant. Based on the theoretical results, a performance comparison among the studied systems is carried out to show the advantages and drawbacks when using the radiometers in a close-range screening application. Moreover, the screener performance in a close-range environment is discussed from the results obtained in the aforementioned comparison.

This paper focuses on the theoretical basis and general procedures for the characterization of antenna loss using the cold sky calibration sequences. The main objective is to find an improved procedure to compensate for the long- and short-term drifts observed in SMOS data. Future versions of the SMOS level 1 processor will include the procedures detailed here.

SMOS is the acronym for the Soil Moisture and Ocean Salinity mission by the European Space Agency (ESA) [1]. Its single payload, the Mcrowave Imaging Radiometer using Aperture Synthesis (MΓRAS), was successfully launched in November 2009. A six months Commissioning Phase was devoted to bring the satellite into a fully operational condition and to characterize the payload using specific orbits to check all instrument modes [2]. An on- going activity is devoted to analyze the contribution of each single calibration procedure to the overall radiometric accuracy in order to assess the dominant sources of spatial errors. In this framework, this paper is devoted to assess the performance of the phase calibration procedures by means of the so called Redundant Space Calibration (RSC)1.

After the successful launching of the Soil Moisture and Ocean Salinity satellite in November 2009, continuous streams of data started to be regularly downloaded and made available to be processed. The first six months of operation were fully dedicated to the In-Orbit Commissioning Phase, with an intense activity aimed at bringing the satellite and instrument into a fully operational condition. Concerning the payload Microwave Imaging Radiometer with Aperture Synthesis, it was fully characterized using specific orbits dedicated to check all instrument modes. The procedures, already defined during the on-ground characterization, were repeated so as to obtain realistic temperature characterization and updated internal calibration parameters. External calibration maneuvers were tested for the first time and provided absolute instrument calibration, as well as corrections to internal calibration data. Overall, performance parameters, such as stability, radiometric sensitivity and radiometric accuracy were evaluated. The main results of this activity are presented in this paper, showing that the instrument delivers stable and well-calibrated data thanks to the combination of external and internal calibration and to an accurate thermal characterization. Finally, the quality of the visibility calibration is demonstrated by producing brightness temperature images in the alias-free field of view using standard inversion techniques. Images of ocean, ice, and land are given as examples.

After the six-month long In-Orbit Commissioning Phase (IOCP) the SMOS satellite started to work in its fully operational mode. During the IOCP, the payload MIRAS was completely characterized, both in short- and long-term, and the optimum calibration rate for in-flight operation was established. The results show that the amplitude of the visibility is very stable, thus allowing a very low calibration rate, and that the phase has a systematic and periodic variation, easily tracked with short but frequent internal calibration sequences. Absolute calibration for antenna temperature is carried out by external maneuvers to account for drift in the reference Noise Injection Radiometer. Brightness temperature images of good quality are obtained by inverting the calibrated visibility. The images show features compatible with ocean salinity over ocean and soil moisture over land.