Sensitivity Analysis of MIRAS/SMOS Instrument Calibration Parameters

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

Earth remote sensing with SMOS, Aquarius and SMAP missions

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 dataset for environmental, meteorological, hydrological or climatological studies. -Ice thickness effects on passive remote sensing of Antarctic continental ice. The relationship between Antarctic ice thickness spatial variations and changes detected by SMOS and Aquarius measurements is explored. The emissivity of Antarctica is analyzed to disentangle the role of the geophysical contributions (snow layers at different depths and subglacial lakes) to the observed signal. The stability of the L-band signal in the East Antarctic Plateau, calibration/validation site for microwave satellite missions, is assessed. -Microwave/optical synergy for multi-scale soil moisture sensing. The relationship of SM and land surface temperature (LST) dynamics is evaluated to better understand the fundamental SM-LST link through evapotranspiration and thermal inertia physical processes. A new approach to measure the critical soil moisture from time-series of spaceborne SM and LST is proposed. The synergistic use of SMOS SM and remotely sensed LST for refining SM disaggregation algorithms is also analyzed. -Comparison of passive and active microwave vegetation parameters. Recent research has shown that microwave vegetation opacity, sensitive to biomass and water content, and albedo, related to canopy structure, can be retrieved from passive L-band observations. The relationships between these two parameters and radar-derived vegetation descriptors have been explored using airborne observations from the SMAP Validation Experiment 2012 (SMAPVEX12). The obtained relations could allow for improved SM retrievals in active-passive systems, and also to estimate the vegetation properties at high resolution using SAR observations. The Ph.D. Thesis has been developed within the activities of the Barcelona Expert Centre (BEC). The presented results contribute to the use of L-band remote sensing in different scientific disciplines such as climate, cryosphere, hydrology and ecology.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 climatològics. -Efecte de gruix de gel en teledetecció de gel continental a l'Antàrtida. S'explora la relació entre les variacions espaials del gruix de gel antàrtic i els canvis detectats a les mesures d'SMOS i Aquarius. L'emissivitat de l'Antàrtida es analitzada per discernir el rol de les contribucions geofísiques (capes de gel a diferents profunditats i llacs subglacials) al senyal observat. S'avalua l'estabilitat del senyal a banda L sobre la zona est de l'altiplà antàrtic, lloc per calibratge/validació de satèl·lits de microones. -Sinèrgia de microones/òptic per teledetecció de SM multiescala. S'avalua la correlació entre la SM i la temperatura de la superfície del sòl (LST) per entendre millor la relació SM-LST a través de processos físics d'evapotranspiració i inèrcia tèrmica. Es proposa un nou mètode per mesurar la humitat crítica utilitzant sèries temporals de SM i LST de satèl·lit. S'analitza l'ús de la SM de SMOS amb la LST de teledetecció per refinar algorismes de desagregació de SM. -Comparació de paràmetres passius i actius de microones relatius a la vegetació. Recent investigació ha mostrat que l'opacitat, sensible a la biomassa i el contingut d'aigua, i l'albedo, relacionat amb l'estructura, poden ser recuperats d'observacions passives a banda L. S'exploren les relacions entre aquests dos paràmetres i estimadors de vegetació derivats de radar utilitzant les observacions d'avió de l'experiment de validació d'SMAP 2012 (SMAPVEX12). Les relacions obtingudes podrien permetre millors recuperacions de SM en sistemes actius/passius i estimar les propietats de la vegetació a alta resolució utilitzant mesures de SAR. La tesi s'ha desenvolupat dins les activitats del Barcelona Expert Centre (BEC). Els resultats presentats contribueixen a l'ús de la banda L a diferents disciplines científiques com la climatologia, la criosfera, la hidrologia i l'ecologia

Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations

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 situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.Peer ReviewedPostprint (published version

Correlated triple collocation to estimate SMOS, SMAP and ERA5-Land soil moisture errors

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 ReviewedPostprint (author's final draft

Analyzing spatio-temporal factors to estimate the response time between SMOS and in-situ soil moisture at different depths

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 use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.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 ReviewedPostprint (published version

Soil moisture estimation synergy using GNSS-R and L-Band microwave radiometry data from FSSCat/FMPL-2

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 model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.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 ReviewedPostprint (published version

FSSCat Mission description and first scientific results of the FMPL-2 onboard 3CAT-5/A

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 ReviewedArticle 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, ItalyPostprint (author's final draft

Multisensor experiments over vineyard: new challenges for the GNSS-R technique

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 ReviewedPostprint (published version

Evaluation of microbial extracts for contamination control in plant tissue culture systems

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