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The Barcelona Expert Center (BEC) has become an international reference in the generation of high resolution soil moisture (SM) maps using disaggregation algorithms. More than a decade ago, a semi-empirical approach was developed to produce Soil Moisture and Ocean Salinity (SMOS) SM at 1 km. This method has been refined over the years to obtain cloud free maps, and modified to further improve the spatial resolution up to 300 m. More recently, a machine-learning approach has been developed to derive European Space Agency (ESA)’s Climate Change Initiative (CCI) SM at 60 m.Thanks to the multi-spectral information added during the disaggregation process, the downscaled SM maps have an overall accuracy similar to the coarse ones, but provide additional information about the SM spatial variability. In this regard, three different applications over the north of Africa are presented here to exemplify the added-value of SM data at high resolution., This research was supported by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI/10.13039/501100011033) through the project INTERACT (PID2020-114623RB-C32) and by the Institute of Space Studies of Catalonia (IEEC)., Peer Reviewed, Postprint (author's final draft)

Soil Moisture (SM) is one of the Essential Climate Variables (ECVs) needed to better understand the water cycle, to assess climate change, and it is crucial to monitor floods and droughts, in agriculture, etc. SM mapping at large scale is only feasible using remote sensing sensors for Earth observation. In this paper, the different soil moisture mapping techniques are first revised, then SM mapping using L-band microwave radiometers and GNSS-Reflectometers are presented using UPC-developed techniques and airborne and spaceborne instruments. Downscaled SM mapping techniques are described in detail in the companion paper by Pablos et al. titled "Two disaggregation algorithms to estimate soil moisture at moderate (1 km and 300 m) and at high (60 m) resolution: Applications over the North of Africa" presented also at this conference., This research was supported by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI/10.13039/501100011033) through projects GENESIS (PID2021-126436OB-C21), AI4WATER (TED2021-131877B-I00), and INTERACT (PID2020- 114623RB-C32)., Peer Reviewed, Postprint (author's final draft)

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 m 3 •m -3 ), demonstrating the method’s significant potential for deriving high-resolution soil moisture information., This research was supported by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011 033), through the project INTERACT “PID2020-114623RB-C32. This work was also supported by the Institute d’Estudis Espacials de Catalunya - IEEC. David Chaparro received funding from the Fundación Ramón Areces. In addition, Amir M. Irawan received the support of a fellowship from “La Caixa” Foundation (ID 100010434), with the fellowship code LCF/BQ/DI21/11860028., Peer Reviewed, Postprint (author's final draft)

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., This work was supported by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) through the projects INTERACT (PID2020-114623RB-C32 and PID2020-114623RB-C31), ERDF funds, the Institute of Space Studies of Catalonia (IECC), the ICM-CSIC Severo Ochoa Excellence Award (CEX2019-000928-S), and the CSIC Interdisciplinary Thematic Platform (PTI) TELEDETECT., Peer Reviewed, Postprint (author's final draft)

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., The project that gave rise to these results received the support of a fellowship from the ”la Caixa” Foundation (ID 100010434), with fellowship code LCF/BQ/DI21/11860028. This work was also supported by Project PID2020-114623RB-C32 funded by MCIN/ AEI /10.13039/501100011033. In addition, D. Chaparro has received funding from the XXXIII Ramón Areces Postdoctoral grant., Peer Reviewed, Postprint (author's final draft)

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 Soil Moisture and Ocean Salinity (SMOS) brightness temperatures at vertical polarization ( TBV ) to estimate SM at 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 ( ¿ ) and soil roughness ( hs ) parameters. These effective parameters are used together with fine-scale multiangular Sentinel-1 backscatter in a single-pass active–passive downscaling approach to estimate TBV at fine scale (1 km) for each SMOS incidence angle. These TBVs are finally inverted to obtain the corresponding high-resolution SM maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of ¿ and a decreasing trend of hs with SMOS incidence angle, with almost no variability of ¿ 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 fine TBV maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution SM maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m 3 /m 3 , respectively, and compare well with in situ measurements. Our results indicate that model-based microwave approaches to estimate SM can be adequately adapted to account for the incidence angle diversity of planned missions, such as Copernicus Microwave Imaging Radiometer (CIMR), Radar Observing System for Europe in L-band (ROSE-L), and Sentinel-1 next generation., This work was supported in part by Ministeriode Ciencia e Innovación (MCIN)/Agencia Estatalde Investigación (AEI)/10.13039/501100011033 under Project PID2020-114623RB-C32; in part by the Spanish Ministry of Science, Innovation and Universities, through the coordinated project L-Band [MCIU/AEI/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)]: Sobre la continuidad de las misiones satelitales de banda L. Nuevos paradigmas en productos y aplicaciones, under Grant ESP2017-89463-C3-2-R (UPC part) and Grant ESP2017- 89463-C3-1-R (ICM part); and in part by the Unidad de Excelencia María de Maeztu under Grant MDM-2016-0600. The work of María Piles was supported by project LEAVES under Grant RTI2018-096765-A-100 funded by MCIN/AEI/10.13039/501100011033 and by the European Regional Development Fund (ERDF, EU), Peer Reviewed, Postprint (author's final draft)

The Federated Satellite Systems/ 3 Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S 3 ) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3 Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3 Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3 Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3 Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results., This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (FSSCat project) and ESA project FSSCat Validation Experiment in MOSAIC; Spanish Ministry of Science, Innovation, and Universities Sensing With Pioneering Opportunistic Techniques grant RTI2018-099008-BC21/AEI/10.13039/501100011033; GNSS Environmental and Societal Missions grant PID2021- 126436OB-C21; and Unidad de Excelencia Maria de Maeztu grant MDM-2016-0600., Peer Reviewed, Article signat per 30 autors/es: Adriano Camps, Universitat Politècnica de Catalunya, Barcelona, Spain, Institut d’Estudis Espacials de Catalunya, Barcelona, Spain, United Arab Emirates University, Abu Dhabi, United Arab Emirates; Joan Francesc Munoz-Martin, Universitat Politècnica de Catalunya, Barcelona, Spain, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA; Joan Adrià Ruiz-de-Azua, Universitat Politècnica de Catalunya, Barcelona, Spain, i2Cat, The Internet Research Center, Barcelona, Spain; Lara Fernandez, Universitat Politècnica de Catalunya, Barcelona, Spain; Adrian Perez-Portero, Universitat Politècnica de Catalunya, Barcelona, Spain; David Llavería, Universitat Politècnica de Catalunya, Barcelona, Spain; Christoph Herbert, Universitat Politècnica de Catalunya, Barcelona, Spain; Miriam Pablos, Institut de Ciències del Mar, Barcelona, Spain, Barcelona Expert Center on Remote Sensing, Barcelona, Spain; Alessandro Golkar, Universitat Politècnica de Catalunya, Barcelona, Spain, Department of Aerospace and Geodesy, Technical University of Munich, Munich, Germany; Antonio Gutiérrez, Deimos Engenharia, Lisbon, Portugal; Carlos António, Deimos Engenharia, Lisbon, Portugal; Jorge Bandeiras, Deimos Engenharia, Lisbon, Portugal; Jorge Andrade, Deimos Engenharia, Lisbon, Portugal; David Cordeiro, Deimos Engenharia, Lisbon, Portugal; Simone Briatore, Golbriak Space, Tallin, Estonia; Nicola Garzaniti, Golbriak Space, Tallin, Estonia, School of Aerospace, Transport, and Manufacturing, Cranfield University, Cranfield, Bedfordshire, U.K.; Fabio Nichele, Tyvak International, Torino, Italy; Raffaele Mozzillo, Tyvak International, Torino, Italy; Alessio Piumatti, Tyvak International, Torino, Italy; Margherita Cardi, Tyvak International, Torino, Italy; Marco Esposito, Cosine, Sassenheim, The Netherlands; Chris van Dijk, Cosine, Sassenheim, The Netherlands; Nathan Vercruyssen, Cosine, Sassenheim, The Netherlands; Joao Barbosa, Cosine, Sassenheim, T, Postprint (author's final draft)

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 (h 1 ) parameter against ERA5-land SM. The analysis is carried out for SMOS data at three different incidence angles ( 32.5±5°, 42.5±5° and 52.5±5° ) 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 h 1 with 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 m 3 m -3 and low bias (0-0.015 m 3 m -3 )., This research was funded by the Spanish Ministry of Science, Innovation and Universities, through the coordinated project LBand (MCIU/AEI/FEDER, UE): ESP2017-89463-C3-R, the Unidad de Excelencia María de Maeztu MDM-2016-0600, the Centro de Excelencia Severo Ochoa CEX2019-000928-S, and the CSIC Plataforma Temática de Teledetección. M. Piles is supported by a Ramón y Cajal contract and the project RTI2018-096765-A100 (MCIU/AEI/FEDER, UE). The authors are grateful to MIT for supporting this research with the MIT-Germany Seed Fund “Global Water Cycle and Environmental Monitoring using Active and Passive Satellite-based Microwave Instruments”., Peer Reviewed, Postprint (published version)

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 m 3 /m 3 . 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 m 3 /m 3 , with a bias smaller than 0.0007 m 3 /m 3 ., This work was supported by 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). This work has been (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21 / AEI / 10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM2016-0600, and by the Spanish Ministry of Science and Innovation project ESP2017-89463-C3, the Centro de Excelencia Severo Ochoa CEX2019-000928-S and the CSIC Plataforma Tematica Interdisciplinar de Teledetección (PTI-Teledetect)., Peer Reviewed, Postprint (published version)

FSSCat, the “Federated Satellite Systems/ 3 Cat-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., This work was supported by 2017 ESA S 3 challenge and Copernicus Masters overall winner award (“FSSCat” project) and ESA project “FSSCat Validation Experiment in MOSAIC”, by the Spanish Ministry of Science, Innovation and Universities, "Sensing with Pioneering Opportunistic Techniques" SPOT, grant RTI2018-099008- BC21/AEI/10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600., Peer Reviewed, Article signat per 25 autors/es: A. Camps 1,2; J.F. Munoz‐Martin 1; J.A. Ruiz‐de‐Azua 1,2; L. Fernandez 1; A. Perez-Portero 1; D. Llavería 1; C. Herbert 1; M. Pablos 3; A. Golkar 4,1; A. Gutiérrrez 5; C. António 5; J. Bandeiras 5; J. Andrade 5; D. Cordeiro 5; S. Briatore 4,6; N. Garzaniti 4,6; F. Nichele 7; R. Mozzillo 7; A. Piumatti 7; M. Cardi 7; M. Esposito 8; B. Carnicero Dominguez 9; M. Pastena 9; G. Filippazzo 10; A. Reagan 10 // 1. Universitat Politècnica de Catalunya, Barcelona, Spain; 2. Institut d’Estudis Espacials de Catalunya, Barcelona, Spain; 3. Institut de Ciències del Mar (ICM-CSIC) & Barcelona Expert Center (BEC) on Remote Sensing, Barcelona, Spain; 4. Skolkovo Institute of Science and Technology, Moscow, Russia; 5. Deimos Eng., Lisbon, Portugal; 6. Golbriak Space, Tallin, Estonia; 7. Tyvak International, Torino, Italy; 8. Cosine, Oosteinde, The Netherlands; 9. ESA ESTEC, Noordwijk, The Netherlands; 10. ESA ESRIN, Frascati, Italy, Postprint (author's final draft)

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). © 2021 IEEE., This work has been supported by the Spanish Ministry of Science and Innovation through the projects ESP2017-89463-C3-1R and ESP2017-89463-C3-2R, the ICM-CSIC Severo Ochoa Excellence Award CEX2019-000928-S, the CommSensLab-UPC María de Maeztu Excellence Award MDM-2016-0600, and the CSIC Interdisciplinary Thematic Platform TELEDETECT., Peer Reviewed, Postprint (author's final draft)

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved., This work was supported by 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). this work has been (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21/AEI/10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work has also been (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, and by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). David Llavería receives support from a FPU fellowship from the Spanish Ministry of Education FPU18/06107.; Joan Francesc Munoz-Martin receives support from the grant for recruitment of early stage research staff FI-DGR 2018 of the AGAUR—Generalitat de Catalunya (FEDER), Spain; C.H. receives support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050, and funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673., Peer Reviewed, Postprint (published version)

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide TB data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 TB and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation., This work was supported by 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project), and has been (partially) sponsored by the project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21/AEI/10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work has also been (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, and by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). Christoph Herbert receives support from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050, and funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673; Joan Francesc Munoz-Martin receives support from the grant for recruitment of early-stage research staff FI-DGR 2018 of the AGAUR—Generalitat de Catalunya (FEDER), Spain; David Llavería receives support from a FPU fellowship from the Spanish Ministry of Education FPU18/06107., Peer Reviewed, Postprint (published version)

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth, This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). This work was (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21 / AEI / 10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work was also (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). Joan Francesc Munoz-Martin received support from the grant for the recruitment of early-stage research staff FI-DGR 2018 of the AGAUR - Generalitat de Catalunya (FEDER), Spain; Christoph Herbert received the support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050 and funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673; David Llavería received support from an FPU fellowship from the Spanish Ministry of Education FPU18/06107., Peer Reviewed, Postprint (published version)

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scal, This research was funded by the Spanish Ministry of Science, Innovation and Universities, through the coordinated project L-Band (MCIU/AEI/FEDER, UE): Sobre la continuidad de las misiones satelitales de banda L. Nuevos paradigmas en productos y aplicaciones, grant numbers ESP2017-89463-C3-2-R (UPC part) and ESP2017-89463-C3-1-R (ICM part), and the Unidad de Excelencia María de Maeztu MDM-2016-0600. M. Piles is supported by a Ramón y Cajal contract and the project RTI2018-096765-A-100 (MCIU/AEI/FEDER, UE)., Peer Reviewed, Postprint (published version)

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land, The project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660050. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. This study was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600 and by the Spanish Ministry of Science and Innovation through the projects ESP2017-89463-C3-1-R, ESP2017-89463-C3-2-R and ESP2017-89463-C3-3-R., Peer Reviewed, Postprint (published version)

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 (¿ 2 ) 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 ¿ 2 -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., Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

This article summarizes some of the activities in which Jordi Font, research professor and head of the Department of Physical and Technological Oceanography, Institut de Ciències del Mar (CSIC, Spanish National Research Council) in Barcelona, has been involved as co-Principal Investigator for Ocean Salinity of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Mission from the perspective of the Remote Sensing Lab at the Universitat Politècnica de Catalunya. We have probably left out some of his many contributions to salinity remote sensing, but we hope that this review will give an idea of the importance of his work. We focus on the following issues: 1) the new accurate measurements of the sea water dielectric constant, 2) the WISE and EuroSTARRS field experiments that helped to define the geophysical model function relating brightness temperature to sea state, 3) the FROG 2003 field experiment that helped to understand the emission of sea foam, 4) GNSS-R techniques for improving sea surface salinity retrieval, 5) instrument characterization campaigns, and 6) the operational implementation of the Processing Centre of Levels 3 and 4 at the SMOS Barcelona Expert Centre., Peer Reviewed, Postprint (published version)

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 multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant., Postprint (author's final draft)

Since its launch in 2009, the ESA’s SMOS mission is providing global soil moisture (SM) maps at ~40 km, using the first L-band microwave radiometer on space. Its spatial resolution meets the needs of global applications, but prevents the use of the data in regional or local applications, which require higher spatial resolutions (~1-10 km). SM disaggregation algorithms based generally on the land surface temperature (LST) and vegetation indices have been developed to bridge this gap. This study analyzes the SM-LST relationship at a variety of LST acquisition times and its influence on SM disaggregation algorithms. Two years of in situ and satellite data over the central part of the river Duero basin and the Iberian Peninsula are used. In situ results show a strong anticorrelation of SM to daily maximum LST (R˜-0.5 to -0.8). This is confirmed with SMOS SM and MODIS LST Terra/Aqua at day time-overpasses (R˜-0.4 to -0.7). Better statistics are obtained when using MODIS LST day (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.06 m3 /m3 ) than LST night (R˜0.45 to 0.80; ubRMSD˜0.04 to 0.07 m3 /m3 ) in the SM disaggregation. An averaged ensemble of day and night MODIS LST Terra/Aqua disaggregated SM estimates also leads to robust statistics (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.07 m3 /m3 ) with a coverage improvement of ~10-20 %., Peer Reviewed, Postprint (published version)

This article summarizes some of the activities in which Jordi Font, research professor and head of the Department of Physical and Technological Oceanography, Institut de Ciències del Mar (CSIC, Spanish National Research Council) in Barcelona, has been involved as co-Principal Investigator for Ocean Salinity of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Mission from the perspective of the Remote Sensing Lab at the Universitat Politècnica de Catalunya. We have probably left out some of his many contributions to salinity remote sensing, but we hope that this review will give an idea of the importance of his work. We focus on the following issues: 1) the new accurate measurements of the sea water dielectric constant, 2) the WISE and EuroSTARRS field experiments that helped to define the geophysical model function relating brightness temperature to sea state, 3) the FROG 2003 field experiment that helped to understand the emission of sea foam, 4) GNSS-R techniques for improving sea surface salinity retrieval, 5) instrument characterization campaigns, and 6) the operational implementation of the Processing Centre of Levels 3 and 4 at the SMOS Barcelona Expert Centre., Peer Reviewed, Postprint (published version)

Premi extraordinari doctorat UPC curs 2015-2016, àmbit Enginyeria de les TIC, The first three of a series of new generation satellites operating at L-band microwave frequencies have been launch in the last decade. L-band is particularly sensitive to the presence of water content in the scene under observation, being considered the optimal bandwidth for measuring the Earth's global surface soil moisture (SM) over land and sea surface salinity (SSS) over oceans. Monitoring these two essential climate variables is needed to further improve our understanding of the Earth's water and energy cycles. Additionally, remote sensing at L-band has been proved useful for monitoring the stability in ice sheets and measuring sea ice thickness. The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first mission specifically launched to monitor SM and SSS. It carries on-board a novel synthetic aperture radiometer with multi-angular and full-polarization capabilities. NASA's Aquarius (2011-2015) was the second mission, devoted to SSS monitoring with a combined real aperture radiometer/scatterometer system that allows correcting for sea surface roughness. NASA's Soil Moisture Active Passive (SMAP, 2015-2018) is the second mission dedicated to measure SM. It carries on-board a real aperture full-polarimetric radiometer and a synthetic aperture radar (SAR) for enhanced spatial resolution and freeze/thaw detection. This Ph.D. Thesis is focused on analyzing the geophysical information that can be obtained from L-band SMOS, Aquarius and SMAP observations. The research activities are structured as follows: -Inter-comparison of radiometer brightness temperatures at selected targets. A novel methodology to measure the consistency between SMOS and Aquarius radiometric data over the entire dynamic range of observations (land, ice and ocean) is proposed. It allows detecting spatial/temporal differences or biases without latitudinal limitations neither cross-overs. This is a necessary step to combine observations from different instruments in a long term data, Els primers tres d'una sèrie de satèl·lits de nova generació funcionant a la banda L han sigut llançats a l'última dècada. La banda L es molt sensible a la presència d'aigua a l'escena observada, sent considerada òptima per mesurar la humitat del sòl (SM) i la salinitat del mar (SSS) de manera global a la superfície de la Terra. Monitoritzar aquestes dues variables climàtiques essencials es necessari per millorar el nostre coneixement dels cicles de l'aigua i l'energia. La teledetecció a banda L també ha sigut útil per monitoritzar l'estabilitat de les capes de gel i mesurar el gruix de gel marí. La missió Soil Moisture and Ocean Salinity (SMOS, 2009-2017) de l'ESA és la primera específicament llançada per monitoritzar SM i SSS. Porta un nou radiòmetre d'apertura sintètica amb capacitat multiangular i polarització completa. La missió Aquarius (2011-2015) de la NASA va ser la segona, dedicada a monitoritzar SSS amb un sistema de radiòmetre/escateròmetre d’apertura real que permet corregir la rugositat de la superfície del mar. La missió Soil Moisture Active Passive (SMAP, 2015-2018) de la NASA és la segona dedicada a mesurar SM. Porta un radiòmetre d'apertura real i polarització completa i un radar d'apertura sintètica (SAR) per una millor resolució espaial i detecció de congelació/descongelació. Aquesta tesi està enfocada en analitzar la informació geofísica que pot obtenir-se de les observacions a banda L d'SMOS, Aquarius i SMAP. La seva investigació està estructurada com: -Intercomparació de temperatures de brillantor en zones seleccionades. Es proposa un nou mètode per mesurar la consistència entre les dades radiomètriques d'SMOS i Aquarius sobre el rang dinàmic complet d'observacions (terra, gel, oceà). Això permet detectar diferències espaials/temporals o biaixos sense limitacions latitudinals ni creuaments. Aquest pas es necessari per combinar observacions de diferents instruments en un llarg conjunt de dades per estudis mediambientals, hidrològics o climatolò, Award-winning, Postprint (published version)

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ˜ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ˜ - 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ˜ -0.5 to -0.7; satellite R ˜ -0.4 to -0.7) indicating SM–LST coupling, than in winter (in situ R ˜ +0.3; satellite R ˜ -0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12 m3/m3 , stable over the study period and consistent between in, Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

An airborne campaign was performed during August, 2014 in an agricultural area in the Duero basin (Spain) in order to appraise the synergy between very different sources of Earth Observation imagery, and very different instruments for soil moisture retrieval. During the flight, an intensive field campaign comprising soil, plant and spectral measurements was carried out. An innovative sensor based on the Global Navigation Satellite Systems Reflectometry (GNSS-R) was on board the manned vehicle, the Light Airborne Reflectometer for GNSS-R Observations (LARGO) engineered by the Universitat Politècnica de Catalunya. While the synergy between thermal, optical and passive microwave spectra observations is well known for vegetation parameters and soil moisture retrievals, the experiment aimed to evaluate the synergy of GNSS-R reflectivity with a time-collocated Landsat 8 imagery for soil moisture retrieval under semiarid climatic conditions. LARGO estimates, field measurements, and optical, NIR, SWIR and thermal bands from Landsat 8 were compared. Results showed that the joint use of GNSS-R reflectivity with vegetation and water indices together with thermal maps from Landsat 8 thoroughly improved the soil moisture estimation., Peer Reviewed, Postprint (published version)

©2015 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., Active and passive microwave observations over land are affected by surface characteristics in different ways. L-band radar backscatter and radiometer measurements each have distinct advantages and problematic issues when applied to surface soil moisture estimation. Spaceborne radiometry has the advantage of better sensitivity to the geophysical parameter but suffers from coarse spatial resolution given limitations on antenna dimensions. Active sensing has the advantage of higher spatial resolution, but the measurements are, relative to radiometry, more affected by the confounding influences of scattering by vegetation and rough surfaces. Active and passive measurements can potentially span different scales and allow the combining of the relative advantages of the two sensing approaches. This strategy is being implemented in the NASA Soil Moisture Active Passive (SMAP) mission, which relies on the relationship between active and passive measurements to provide 9-km surface soil moisture estimates. The aim of this paper is to study the sensitivity of spaceborne L-band active and passive temporal covariations to land surface characteristics, in preparation for SMAP. A significant linear relationship (with slope ß) is obtained between NASA's Aquarius scatterometer and radiometer observations across major global biomes. The error in ß estimation is found to increase with land cover heterogeneity and to be unaffected by vegetation density (up to moderate densities). Results show that ß estimated with two to eight months of Aquarius measurements (depending on vegetation seasonality) reflect local vegetation cover conditions under surfaces with complex mixture of vegetation, surface roughness, and dielectric constant., Peer Reviewed, Postprint (author's final draft)

The Dome-C region, in the East Antarctic Plateau, is regarded as an ideal natural laboratory for calibration/validation of space-borne microwave radiometers. At L-band, the thermal stability of this region has been confirmed by several experimental campaigns. However, its use as an independent external calibration target has recently been questioned due to some spatial inhomogeneities and seasonal effects revealed in the brightness temperatures (T-B) acquired in this area. This paper shows the observed relationship, from exploratory research, between the Antarctic ice thickness spatial variations and the measured Aquarius T-B changes. A 3-months no-daylight period during the Austral winter has been analyzed. Four transects have been defined over East Antarctica covering areas with different ice thickness variations and ranges. The theoretical L-band penetration depth has been estimated to understand the possible contributions to the measured signal. A good agreement has been observed between Aquarius T-B and ice thickness variations over the whole Antarctica, with correlations of approximate to 0.6-0.7. The two variables show a linear trend with slopes of approximate to 8.3-9.5 K/km. No correlation has been observed with the subglacial bedrock. The maximum L-band penetration depth has been estimated to be approximate to 1-1.5 km. Results are therefore consistent: the spatial variations found on Aquarius T-B are not related to the emissivity of the bedrock, which lies deeper. This study provides evidence that new L-band satellite observations could contribute to further our understanding of Antarctic geophysical processes., Peer Reviewed, Postprint (published version)

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, Peer Reviewed, Postprint (published version)

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 TB changes 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., Peer Reviewed, Postprint (published version)

Passive microwave remote sensing at L-band is considered to be the most suitable technique to measure soil moisture and ocean salinity. These two variables are needed as inputs of predictive models, to improve climate and weather forecast, and to increase our knowledge of the water cycle. Nowadays, there are two space missions providing frequent and global observations of moisture and salinity of the Earth's surface with L-band radiometers on-board. The first one is the ESA's SMOS satellite, launched on November 2, 2009, which carries a two-dimensional, multi-angular, and full-polarimetric synthetic aperture radiometer. The second one is the NASA/CONAE's Aquarius/SAC-D mission, launched on June 10, 2011, which includes three beam push-broom real aperture radiometers. The objective of this work is to compare SMOS and Aquarius brightness temperatures and verify the continuity and consistency of the data over the entire dynamic range of observations. This is paramount if data from both radiometers are used for any long term enviromental, meteorological, hydrological, or climatological studies. The inter-comparison approach proposed is based on the study of 1 year of measurements over key target regions selected as representative of land, ice, and sea surfaces. The level of linearity, the correlation, and the differences between the observations of the two radiometers are analyzed. Results show a higher linear correlation between SMOS and Aquarius brightness temperatures over land than over sea. A seasonal effect and spatial inhomogeneities are observed over ice, at the Dome-C region. In all targets, better agreement is found in horizontal than in vertical polarization. Also, the correlation is higher at higher incidence angles. These differences indicate that there is a non-linear effect between the two instruments, not only a bias., Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

SMOS is the acronym for the Soil Moisture and Ocean Salinity mission by the European Space Agency (ESA) [1]. Its single payload, the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), 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 ongoing 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., Peer Reviewed, Postprint (published version)

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., Peer Reviewed, Postprint (published version)

The Final Project has been developed in the framework of the ESA’s Soil Moisture and Ocean Salinity (SMOS) mission [1], during the development of Pre-Commissioning and Commissioning phases. The first steps of this work were in September 2009 with the Remote Sensing Laboratory group of TSC (Theory of Signal and Communications Department) at UPC [2]. One of the main objectives of any mission is to obtain and provide stable and accurate final products. So, a well-calibrated instrument provides the basis for stable and accurate measurements. The calibration of any Earth Observation sensor is a key stage which encompasses those tasks which are necessary to convert the raw measurement data into science data. The characterization of the instrument is a requirement for the development of the calibration activities. Characterization consists of the measurement of the typical behavior of instrument performances, including subsystems, which may affect the accuracy or quality of its response or derived data. The aim of this Final Project is to perform a comprehensive temperature sensitivity analysis of the instrument that is the SMOS payload. To do this, it is necessary to characterize the Power Measurement System (PMS) included in each receiver over the physical temperature. Additionally, the correlation phase related to the Local Oscillator (LO) located in each segment of the instrument is also analyzed. PMS calibration parameters (gain and offset) and the correlation phase (LO phase) are planned to be periodically updated during the mission to account for possible instrumental drifts. These parameters are tracked, initially on-ground and after in orbit, during the measurement mode using their respective corrections to remove physical temperature drifts.