Satellite mission datasets increase in size as their life span grows and the resolutions of the instruments increase. Accurately projecting antenna-based satellite measurements on a geographical grid while maintaining reasonable computational time and costs can be challenging. It is thus necessary to include big data algorithms and dedicated management techniques in the processing of such datasets.In this regard, the optimization of the number of interpolations and projections is one of the key aspects, as the native errors of the measurements propagate at each processing step. Besides each interpolation and projection implies a non-negligible increase in total computational time.This work is based on the Sea Surface Salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission, but it could be easily extended to any other satellite mission where individual values for each measurement are retrieved. We propose a redefinition of the complete processor chain so it can work with the measurements within the instrument coordinate system. This allows us to avoid projection-related errors during the generation of the final product.Additionally, we introduce a novel algorithm to project those measurements taking into account the actual spatial extent of the acquisitions instead of taking them as points, so measures are averaged weighted by the area they cover on the Earth-based grid. This method is optimized to transform 2D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. Our algorithm has demonstrated its potential when incorporated into the SMOS SSS processor at the Barcelona Expert Center (BEC), allowing us to keep a final resolution very close to the one attained at the antenna coordinate system.

In the framework of the ESA regional initiative Arctic+ Salinity, a new Sea Surface Salinity (SSS) product (v4) has been developed from the Soil Moisture and Ocean Salinity (SMOS) measurements. The data processing starts from the SMOS Level 0 (L0) data and applies specific algorithms for reducing the contamination close to the ice edge at Level 1 (L1) (in the Brightness Temperature reconstruction) and at Level 2 (L2) (in the correction of biases in the salinity retrieval). In this work we explain these algorithms and present the new 13-year temporal series of SMOS SSS maps as well as the main results of the quality assessment.

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.

Sea surface temperature (SST) is a key oceanic variable controlling energy fluxes, as well as several atmospheric parameters such as wind speed, air temperature, humidity and cloudiness. During the last decades, mesoscale has received much attention and the new frontier for the coming years is the understanding of sub-mesoscale dynamics and its impact on climate. In order to address this challenge, there is a need of developing high-resolution observing systems, remote sensing sensors in conjunction with in-situ observations. Some traditional climate-oriented SST observational datasets generally do not include satellite observations and are typically based on in-situ observations, prominent examples being NOAA Extended Reconstructed SST (ERSST) and Hadley Centre SST version 3 (HadSST3). Other datasets combine both, in-situ and satellites observations, like the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST). The main objective of this work is to characterize sea surface temperature (SST) climatology and variability in the tropical Atlantic region. For that purpose, we thoroughly compare two standard, climate-oriented datasets, HadISST (1° resolution) and ERSSTv5 (2° resolution), with the GHRSST product developed by the European Space Agency (ESA) Climate Change Initiative (CCI) (0.05° resolution). Our results show that, at grid-point level, the three datasets behave similarly on a large scale, but they show consistent differences in all seasons, with CCI distinctly displaying more expansive and larger variability in the equatorial Atlantic and also in the subtropical North Atlantic. The differences in climatology are less apparent. In particular, over the ATL3 region, CCI is systematically colder than ERSST and HadISST, and displays higher variability.

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.

Arctic freshwater fluxes make this region key to regulate ocean currents and global climate. Hence, the Arctic Ocean sea surface salinity (SSS) knowledge is crucial to describe some of the processes that govern climate change.Recently, Barcelona Expert Center (BEC) deployed their version 2 of SSS Arctic data retrieved from Soil Moisture and Ocean Salinity mission (SMOS) mission. Nevertheless, in the context of the ESA Arctic+ initiative, BEC has planned to introduce improvements in all the processing levels. Some of the planned improvements include: (i) optimizing the projection grid of level 1, (ii) studying the performance of different dielectric models in the Artic region and (iii) producing an additional level 4 product. The SSS Arctic products from Soil Moisture Active Passive (SMAP) mission could be used to produce a level 4 by merging them with this new version of SSS level 3 produced from SMOS.Big data techniques are applied to produce debiased SSS maps. These techniques will be refined by introducing a new grouping method for the statistical study of the data.The aim of this work is to obtain a more accurate version of the Arctic salinity maps starting at 2011. The new SMOS SSS maps are expected to better capture the Arctic river plumes and thus they will help to better understand the freshwater inflow/outflow in the Arctic Ocean.

The quality of the Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) maps has been noticeably improved in the last two years, in particular those produced at the Barcelona Expert Center (BEC). However, the BEC SSS maps are still affected by a latitudinal and seasonal bias. In this work, we comprehensively characterize the residual latitudinal and seasonal biases, which are used to correct de retrieved SSS, leading to a new generation of higher-quality SSS maps. The shape and regularity of this bias suggests that the effect, which produces this error, is not a poor characterization of the galaxy, some residual Total Electron Content (TEC) effect, or a poor characterization of the systematic Sea Surface Temperature (SST) effects on the SSS retrieval. It appears to be related to a geometrical effect associated to the relative position between the SMOS antenna, the Sun and the Earth.

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.

In this work we evaluate surface ocean current reconstruction at a global scale using a new methodology that combines the benefits of microwave and altimetry measurements. The combined method is based on SQG theory and combines the information about the energy spectrum provided by altimeters with SST microwave measurements. We compare ocean surface current reconstruction using the combined method with eSQG reconstruction, taking as a reference surface geostrophic currents derived from altimetry. Results showed that reconstruction of surface currents based on SQG theory can be improved if the information about the energy spectrum provided by altimeters is used. Furthermore, we investigate to which extent the quality of our results is related to the seasonal cycle of the Mixed Layer Depth (MLD). Results confirmed that the reconstruction performs better for deeper mixed layer.

In this work we evaluate surface ocean current reconstruction at a global scale from microwave Sea Surface Temperature (SST) measurements, based on the Surface Quasi- Geostrophic (SQG) theory. One of the main advantages of microwave measurements is that they are not affected by cloud cover, which is an important aspect for global studies and operational applications. A new heuristic method that combines the benefits of microwave and altimetry measurements is also proposed and evaluated. Results showed that reconstruction of surface currents based on SQG theory can be improved if the information about the energy spectrum provided by altimeters is used.

The performance of the power detectors used to denormalize the digital correlations in interferometric radiometers may be degraded due to the non-linear behavior of the diode response. This work presents the comparison of two methods used to characterize and correct the non-linear response of the detectors. The results are illustrated by presenting the characterization of the power detectors in the MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) instrument, the single payload of the ESA-SMOS mission.

The performance of the power detectors used to denormalize the digital correlations in interferometric radiometers is degraded due to the non-linear behavior of the diode response. This work presents a comprehensive analysis of the impact of detector non-linearity and related correction techniques in the performance of the MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) instrument, which is the single payload of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission.