Advanced methods for earth observation data synergy for geophysical parameter retrieval

The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the its sensitivity to thin ice. The spatial patterns clearly discriminate well-determined areas of open water, old sea ice and a transition zone, which is sensitive to thin sea ice thickness (SIT) and SIC. In a third application, SMOS and the AMSR2 data are used to examine the joint effect of CIMR-like observations. The information contained in the low-frequency channels allows to reveal ranges of thin sea ice, and thicker ice can be determined from the relationship between the high-frequency channels and changing conditions as the sea ice ages. The proposed approach is suitable for merging large data sets and provides metrics for class analysis, and to make informed choices about integrating data from future missions into sea ice products. A regression neural network approach was investigated with the goal to infer SIT using TB data from the Flexible Microwave Payload 2 (FMPL-2) of the FSSCat mission. Two models - covering thin ice up to 0.6m and the full-range of SIT - were trained on Arctic data using ground truth data derived from the SMOS and Cryosat-2. This work demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.La primera parte de la tesis se centra en el análisis de los factores relevantes para estimar el tiempo de respuesta entre la humedad del suelo (SM) basada en el satélite y la in-situ, utilizando una deformación temporal dinámica (DTW). El DTW se aplicó al SMOS L4 SM, y se comparó con la SM in-situ en la red REMEDHUS en el oeste de España. El método se adaptó para controlar la evolución del desfase temporal durante diferentes condiciones de humedad y secado. Se estudiaron los factores climáticos en combinación con los períodos de crecimiento de los cultivos para revelar los procesos relacionados con la SM. La heterogeneidad del uso del suelo se analizó utilizando imágenes de alta resolución de NDVI de Sentinel-2 para proporcionar información sobre el nivel de representatividad espacial de los datos de SMOS a cada estación in situ. La comparación de los patrones de precipitación a largo plazo y la evapotranspiración potencial permitió estimar las estaciones de SM que describen diferentes condiciones de SM en función del clima y las propiedades del suelo. La segunda parte de esta tesis se centra en métodos dirigidos por datos para la segmentación del hielo marino y la obtención de parámetros. Se emplea un método de inferencia bayesiano para segmentar conjuntos de datos satelitales de múltiples fuentes. El algoritmo de aprendizaje bayesiano no supervisado permite investigar el “vínculo oculto” entre múltiples datos. Las propiedades estadísticas se contabilizan mediante un modelo de mezcla gaussiana, y las interacciones espaciales se reflejan mediante campos aleatorios ocultos de Markov. El algoritmo segmenta los datos espaciales en una serie de clases, que se representan como un campo latente en el espacio físico y como clústeres en el espacio de las variables. En una primera aplicación, se utilizó un enfoque probabilístico de dos pasos basado en la maximización de expectativas y el algoritmo de segmentación bayesiano para segmentar imágenes SAR con el objetivo de discriminar el agua superficial de los tipos de hielo marino. La información sobre la rugosidad de la superficie está contenida en las imágenes de backscattering del radar, que puede utilizarse -en principio- para detectar estanques de deshielo y estimar la concentración de hielo marino (SIC) de alta resolución. En un segundo estudio, el algoritmo se aplicó a los datos TB de múltiples ángulos de incidencia del producto SMOS L1C para aprovechar su sensibilidad al hielo fino. Los patrones espaciales discriminan claramente áreas bien determinadas de aguas abiertas, hielo marino viejo y una zona de transición, que es sensible al espesor del hielo marino fino (SIT) y al SIC. En una tercera aplicación, se utilizan los datos de SMOS y de AMSR2 para examinar el efecto conjunto de las observaciones tipo CIMR. La información contenida en los canales de baja frecuencia permite revelar rangos de hielo marino delgado, y el hielo más grueso puede determinarse a partir de la relación entre los canales de alta frecuencia y las condiciones cambiantes a medida que el hielo marino envejece. El enfoque propuesto es adecuado para fusionar grandes conjuntos de datos y proporciona métricas para el análisis de clases, y para tomar decisiones informadas sobre la integración de datos de futuras misiones en los productos de hielo marino. Se investigó un enfoque de red neuronal de regresión con el objetivo de inferir el SIT utilizando datos de TB de la carga útil de microondas flexible 2 (FMPL-2) de la misión FSSCat. Se entrenaron dos modelos - que cubren el hielo fino hasta 0.6 m y el rango completo del SIT - con datos del Ártico utilizando datos de “ground truth” derivados del SMOS y del Cryosat-2. Este trabajo demuestra que las misiones CubeSat de coste moderado pueden proporcionar datos valiosos para aplicaciones de observación de la Tierra.Postprint (published version

Spectral decomposition of real circulant matrices

AbstractThis paper presents spectral decompositions, i.e., eigendecompositions and singular value decompositions of four types of real circulant matrices. Right and left circulants (whose elements topple from right to left or from left to right, respectively) as well as skew right and skew left circulants (whose elements change their sign when toppling) are analyzed.The inherent periodicity of circulant matrices means that they are closely related to Fourier analysis and group theory. This relationship is utilized in the spectral decompositions of this paper

Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures

Consistent sea ice monitoring requires accurate estimates of sea ice concentration. Current retrieval algorithms are based on low-resolution microwave radiometry data with limited penetration depth and are unable to resolve surface characteristics of sea ice in sufficient detail which is necessary to discriminate intact sea ice from closed water. Important information about surface roughness conditions are contained in the distribution of radar backscattering images which can be - in principle - used to detect melt ponds and different sea ice types. In this work, a two-step probabilistic approach based on Expectation-Maximization and Bayesian inference considers the spatial and statistical characteristics of medium-resolution daily-available Sentinel-1 SAR images. The presented method segments sea ice into a number of separable classes and enables to discriminate surface water from the remaining sea ice types.The lead author was supported by “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/D118/11660050, and received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. The project was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project “L-band” ESP2017-89463-C3-2-R, and the project “Sensing with Pioneering Opportunistic Techniques (SPOT)” RTI2018-099008-B-C21/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Bayesian unsupervised machine learning approach to segment arctic sea ice using SMOS data

Microwave radiometry at L-band is sensitive to sea ice thickness (SIT) up to ~ 60 cm. Current methods to infer SIT depend on ice-physical properties and data provided by the ESA’s Soil Moisture and Ocean Salinity (SMOS) mission. However, retrieval accuracy is limited due to seasonally and regionally variable surface conditions during the formation and melting of sea ice. In this work, Arctic sea ice is segmented using a Bayesian unsupervised learning algorithm aiming to recognize spatial patterns by harnessing multi-incidence angle brightness temperature observations. The approach considers both statistical characteristics and spatial correlations of the observations. The temporal stability and separability of classes are analyzed to distinguish ambiguous from well-determined regions. Model uncertainty is quantified from class membership probabilities using information entropy. The presented approach opens up a new scope to improve current SIT retrieval algorithms, and can be particularly beneficial to investigate merged satellite products.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. It was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project “L-band” ESP2017-89463-C3-2-R, and the project “Sensing with Pioneering Opportunistic Techniques (SPOT)” RTI2018-099008-B-C21/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

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

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Penilaian Kinerja Keuangan Koperasi di Kabupaten Pelalawan

This paper describe development and financial performance of cooperative in District Pelalawan among 2007 - 2008. Studies on primary and secondary cooperative in 12 sub-districts. Method in this stady use performance measuring of productivity, efficiency, growth, liquidity, and solvability of cooperative. Productivity of cooperative in Pelalawan was highly but efficiency still low. Profit and income were highly, even liquidity of cooperative very high, and solvability was good