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This remote sensing project, carried out at CommSensLab, has focused on developing a deep learning algorithm using variational autoencoders with data from ESA's SENTINEL-2 mission. The models trained with and without the MSSIM metric in the loss function have been compared, showing that the presence of MSSIM significantly improves the quality of the reconstructions and interpolations, highlighting its sensitivity to fine details and key structures. The variation of parameters such as beta, epochs and latent dimensions have influenced the results. Time constraints and technical challenges, such as network problems, have increased the time to run the code each time a modification was added to the code. The crucial role of the project managers in the successful development of the project is highlighted. The algorithm shows potential applications in the environmental field, with recommendations for future research, including the exploration of alternative metrics and hyperparameter optimization., Este proyecto dentro del ámbito de la teledetección, llevado a cabo en CommSensLab, se ha centrado en desarrollar un algoritmo de aprendizaje profundo utilizando autoencoders variacionales con datos de la misión SENTINEL-2 de la ESA. Se han comparado modelos entrenados con y sin la métrica MSSIM en la función de pérdida, evidenciando que la presencia de MSSIM mejora significativamente la calidad de las reconstrucciones e interpolaciones, destacando su sensibilidad a detalles finos y estructuras clave. La variación de parámetros como beta, epochs y dimensiones latentes han influido en los resultados. Limitaciones temporales y desafíos técnicos, como problemas de red, han aumentado el tiempo para ejecutar el código cada vez que se añadía alguna modificación a este. Se resalta el papel crucial de los directores del proyecto para que este se desarrollase satisfactoriamente. El algoritmo muestra aplicaciones potenciales en el ámbito medioambiental, con recomendaciones para futuras investigaciones, incluyendo la exploración de métricas alternativas y la optimización de hiperparámetros., Aquest projecte dins de l'àmbit de la teledetecció, dut a terme a CommSensLab, s'ha centrat a desenvolupar un algorisme d'aprenentatge profund utilitzant autoencoders variacionals amb dades de la missió SENTINEL-2 de l'ESA. S'han comparat models entrenats amb i sense la mètrica MSSIM en la funció de pèrdua, evidenciant que la presència de MSSIM millora significativament la qualitat de les reconstruccions i interpolacions, destacant-ne la sensibilitat a detalls fins i estructures clau. La variació de paràmetres com a beta, epochs i dimensions latents han influït en els resultats. Limitacions temporals i desafiaments tècnics, com ara problemes de xarxa, han augmentat el temps per executar el codi cada vegada que s'hi afegia alguna modificació. Es ressalta el paper crucial dels directors del projecte perquè aquest es desenvolupés satisfactòriament. L'algorisme mostra aplicacions potencials en l'àmbit mediambiental, amb recomanacions per a investigacions futures, incloent-hi l'exploració de mètriques alternatives i l'optimització d'hiperparàmetres.

Coastal upwelling has been extensively studied as it plays a critical role in the connectivity between offshore waters and coastal ecosystems, which has impacts on water quality, fisheries, and aquaculture production. Significant efforts have been devoted to the quantification of the intensity, duration, and variability of this phenomenon by means of coastal upwelling indexes (CUIs), derived from wind, sea level pressure, or sea surface temperature data. Although valuable, first-order descriptors, such classical indexes have been reported to present some limitations. As one of the major shortcomings is the omission of the direct influence of ocean circulation, this work introduces a novel CUI, generated from remotely sensed hourly surface current observations provided by a high-frequency radar (HFR). The consistency of the proposed index (CUI-HFR) is assessed in two different oceanographic areas during two distinct time periods: in the north-western Iberian (NWI) peninsula for 2021 and in the Bay of Biscay (BOB) for 2014. To this aim, CUI-HFR is compared against a traditional CUI based on hourly wind observations (CUI-WIND) provided by two buoys. Results revealed that HFR-derived circulation maps can be effectively used for the characterization of recurring upwelling and downwelling episodes and the noticeable agreement between CUI-HFR and CUI-WIND., Peer Reviewed

Hyperspectral imaging is a key tool in numerous remote sensing applications. Images with tens or even hundreds of spectral bands contain a wealth of information to retrieve multiple geophysical parameters, to perform target detection, or land classification with unbeatable high accuracies. In addition, hyperspectral sensors are becoming smaller, and today they even fit in CubeSats. However, the amount of data they generate is so large that satellite communication systems have severe limitations to download it, especially in SmallSats. It is therefore becoming urgent to develop efficient automated algorithms that can be executed in the limited capabilities of the onboard computers of these satellites, so as to reduce the amount of data to be stored and downloaded, while keeping as much information as possible for a given scene. In this work a band selection algorithm has been designed to deal with this problem. The proposed algorithm consists of the sequential selection of the spectral bands ranked using the amount of information provided by each band, and also the correlation of these bands with the previously selected ones. The algorithm performance is assessed by means of a suite of classification tests with hyperspectral datasets from different sensors. Results show comparable or even better performance than other existing Band Selection algorithms, while outperforming in terms of computational complexity, which makes it more suitable for SmallSats with limited computing resources., This work was supported by the project GENESIS: GNSS Environmental and Societal Missions, grant PID2021-126436OB-C21, and by a FPU fellowship from the Spanish Ministry of Education. Part of this work has also been possible thanks to the Italian Space Agency (ASI) that granted access to its PRISMA database (http://prisma.asi.it/)., Peer Reviewed, Postprint (published version)

La flora marina, en especial las praderas marinas y praderas de algas, posee un rol esencial en la sostenibilidad de los ecosistemas costeros. Por esto, existe una elevada necesidad en desarrollar herramientas que permitan monitorizar de forma precisa la flora marina. La teledetección por satélite es una tecnología eficiente y económica para poder elaborar mapas de hábitats bentónicos o de tipos de fondos, permitiendo caracterizar los fondos a lo largo del tiempo. Para la generación de estos mapas, en la actualidad típicamente se implementan técnicas basadas en píxel, como modelos Machine Learning convencionales, y otras técnicas basadas en objetos, como el OBIA. No obstante, el avance de las técnicas Deep Learning permite entrenar modelos que aprovechen mejor las características de los fondos marinos para la generación de mapas de elevada precisión. En este trabajo se expone el uso del modelo U-Net, basado en Redes Neuronales Convoluciones (CNN), para la generación de mapas de hábitats bentónicos a partir de imágenes satelitales multiespectrales. La zona de estudio es El Río, estrecho entre las islas de La Graciosa y Lanzarote, islas Canarias, donde existe una elevada biodiversidad con la presencia de multitud de especies como praderas de Cymodocea nodosa, Caulerpa prolifera y algas rojas filamentosas, entre otras especies., Marine flora, especially seagrass and seaweed meadows, plays an essential role in the sustainability of marine ecosystems. Therefore, there is a great need to develop tools to accurately monitor the marine flora. Satellite remote sensing is an efficient and economical technology for mapping benthic habitats or bottom types, allowing the characterization of the seabed over time. For the generation of these maps, techniques pixel-based techniques, such as conventional Machine Learning models, and object-based techniques, such as OBIA, are currently typically implemented. However, the advancement of Deep Learning techniques allows training models that take better advantage of seabed features for the generation of high accurate maps. In this paper we present the use of the U-Net model, based on Convolutional Neural Networks (CNN), for the generation of benthic habitat maps based on multispectral satellite imagery. The study area is El Río, a strait between the islands of La Graciosa and Lanzarote, Canary Islands, where there is a high biodiversity with the presence of a multitude of species such as Cymodocea nodosa meadows, Caulerpa prolifera and red filamentous algae, among other species., Esta investigación ha sido parcialmente subvencionada por el Ministerio para la Transición Ecológica y el Reto Demográfico mediante el proyecto TARA (Ref. SPIP2022-02897)., Peer Reviewed, Postprint (published version)

Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands. However, the computational complexity of traditional methods makes the algorithms difficult to be deployed on board satellites. This is especially true for Small Satellites with limited computational and power resources. Moreover, existing band selection techniques often require the hypercube to be processed at least at Level-1B product, i.e., the bands need to be finely aligned before selecting them, demanding more computational resources for the on-board computer. This study presents a novel neural network-based approach for on-board band selection using data with coarse band-to-band aligned. This methodology not only simplifies the pre-processing requirements, but also opens new possibilities for efficient hyperspectral imaging from space on-board Small Satellites, such as classification, change and target detection., This project was part of the project "GENESIS: GNSS Environmental and Societal Missions – Subproject UPC", Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”, and by a FPU fellowship from the Spanish Ministry of Education. Part of this work has also been possible thanks to the Italian Space Agency (ASI) that granted access to its PRISMA database (http://prisma.asi.it/)., Peer Reviewed, Postprint (published version)

La eutrofización es una gran amenaza para las aguas costeras, lo que ha llevado a la adopción en todo el mundo de políticas destinadas a protegerlas y restaurarlas. Muchas de estas leyes se basan en el marco de fuerzas motrices-presión-estado-impacto-respuesta y siguen el enfoque ecosistémico. Para aplicarlos, se necesita evaluar la presión de la eutrofización y su impacto, así como proporcionar información útil para los gestores. Por un lado, la presión de eutrofización en tierra se basa en los usos del suelo, en su cobertura y en las influencias fluviales (que pueden estimarse a partir de la salinidad en aguas costeras) y en aguas costeras en la concentración de nutrientes. Y por el otro, el impacto de eutrofización en aguas costeras se basa en la concentración de clorofila-a. A partir de esta información puede establecerse la relación presión impacto y rellenar la tabla Presión-Impacto-Gestión (PIG). Esta última, ayuda en el diseño de planes de gestión para zonas costeras, al identificar dónde se deben implementar acciones de monitoreo y de mitigación. Hasta ahora esta aproximación se ha realizado en base a datos in situ, pero la teledetección permite conocer esta información a partir de imágenes satelitales de microondas, infra rojos (incluyendo las térmicas) y visibles. Para comprobar si el enfoque PIG tiene éxito basándose en datos satelitales, se aplicará a dos casos de estudio: (i) en la costa noreste española (Costa Catalana), donde esta metodología ya ha tenido éxito basándose en datos in situ, y (ii) en la costa Atlántica de Honduras (Departamentos de Atlántida y Colón), donde no existe información sobre el proceso de eutrofización. Si tiene éxito, el abordaje PIG basado en datos satelitales será una piedra angular esencial para la gestión de las aguas costeras en todo el mundo., Postprint (published version)

Adverse weather conditions present a primary challenge for ground-based LiDAR imaging systems in outdoor applications. The use of polarization has been proposed as an effective filtering mechanism. However, the number of potential situations is large, complex and difficult to parameterize with accuracy. In such conditions, advanced simulation methods enable the testing of different experimental configurations and the determination of the best possible setup. With this purpose, a Monte Carlo algorithm is presented for modeling polarized pulsed LiDAR signals in turbid media. This algorithm is designed for immediate applicability, incorporating realistic media characterization and accounting for the attributes of existing prototypes. It allows testing various experimental configurations, managing optical obstacles, adjusting polarization arrangements, and different geometries of particles within the medium. The developed algorithm accurately characterizes backscattering signals, revealing their dependence on medium properties. A relationship between visibility and backscattering energy is identified, offering insights for sensor optimization. Polarization analysis highlights the efficacy of circular polarization in mitigating scattering effects and establishes a connection with the polarimetric characteristics of imaged targets. The algorithm's application to irregular particles reveals also an unexpected behavior of polarized light, challenging established strategies. These diverse use cases exemplify the algorithm's capability to model real-world circumstances, emphasizing its significance in predicting cutting-edge situations when designing optical systems for complex and demanding outdoor scenarios., Agència de Gestió d’Ajuts Universitaris i de Recerca (2021FI_B2 00077); Ministerio de Ciencia e Innovación (PID2020-119484RB-I00)., Peer Reviewed, Postprint (published version)

The TFG focuses on the research and development of spatial localization and sensing techniques assisted by Intelligent Reflective Surfaces (IRS) within the context of 6G communication networks. Given the increase in operating frequencies in 6G networks, which include millimeter-wave bands, the project addresses the challenges and opportunities of high-frequency communication. The use of passive IRS is considered a solution to enhance system coverage and multiplexing capacity, and its potential for localization and sensing applications is also explored. The project aims to model and simulate a complete system consisting of a base station, an IRS, and multiple transmitting objects. It involves the development of strategies to control the reflection elements of the IRS and the design of estimators to determine the position and power of transmitting objects based on reflected signals., El TFG se centra en la investigación y desarrollo de técnicas de localización espacial y detección asistida por Superficies Reflectantes Inteligentes (IRS) en el contexto de las redes de comunicación 6G. Dado el aumento en las frecuencias de operación en las redes 6G, que incluyen bandas milimétricas, el proyecto aborda los desafíos y oportunidades de la comunicación a alta frecuencia. El uso de IRS pasivas se considera una solución para mejorar la cobertura y la capacidad de multiplexación del sistema, y también se explora su potencial para aplicaciones de localización y detección. El proyecto tiene como objetivo modelar y simular un sistema completo que consta de una estación base, un IRS y múltiples objetos emisores, desarrollar estrategias para controlar los elementos de reflexión del IRS, y diseñar estimadores para determinar la posición y la potencia de los objetos emisores en función de las señales reflejadas., El TFG es centra en la investigació i desenvolupament de tècniques de localització espacial i detecció assistida per Superfícies Reflectores Intel·ligents (IRS) en el context de les xarxes de comunicació 6G. Donat l'augment de les freqüències d'operació a les xarxes 6G, que inclouen bandes mil·limètriques, el projecte aborda els reptes i oportunitats de la comunicació a alta freqüència. L'ús d'IRS passives es considera una solució per millorar la cobertura i la capacitat de multiplexació del sistema, i també s'explora el seu potencial per a aplicacions de localització i detecció. El projecte té com a objectiu modelar i simular un sistema complet que consta d'una estació base, un IRS i múltiples objectes emissors, desenvolupar estratègies per controlar els elements de reflexió de l'IRS, i dissenyar estimadors per determinar la posició i la potència dels objectes emissors en funció de les senyals reflectides.

The current lack of continuous and temporal monitoring of Chlorophyll- a in certain regions is evident. There are some platforms that collected this data in the past, without current update. For this reason, a model based on a Random Forest classification was trained using the limnological classification based on chlorophyll- a concentration from SeaWiFS data and extrapolated to Landsat-7. The model achieved an accuracy of 62.29% with a moderate kappa coefficient. This approach allowed the development of a well-performing model capable of efficiently monitoring and updating chlorophyll-a data in a region lacking in-situ sensors for this variable., Peer Reviewed

Les descàrregues submarines d'aigua subterrània (SGD) són determinants per a la conservació del bon estat dels ecosistemes costaners en aportar nutrients, no obstant això, també poden ser font de contaminants. A Catalunya pocs estudis es desenvolupat respecte a les SGD, a causa de la dificultat que impliquen els seus patrons espacials heterogenis i difusos i la seva complexa dinàmica temporal. En aquest treball es van determinar 3 noves zones potencials de SGD mitjançant la identificació d'anomalies tèrmiques en imatges de la temperatura de la superfície de la mar (SST) obtingudes del processament d'imatges satel·litàries termals Landsat 8-9 TIRS. La metodologia d'identificació de les anomalies es va realitzar a través de 2 anàlisi; un d'imatges úniques i un altre d'imatges multitemporals amb el càlcul píxel-a-píxel de la desviació estàndard i el rang de la SST, paràmetres estadístics els valors baixos dels quals indiquen baixa variabilitat, la qual cosa és un indicatiu de l'aportació d'aigües subterrànies amb temperatures més estables en el temps., Las descargas submarinas de agua subterránea (SGD) son determinantes para la conservación del buen estado de los ecosistemas costeros al aportar nutrientes, sin embargo, también pueden ser fuente de contaminantes. En Cataluña pocos estudios se desarrollado con respecto a las SGD, debido a la dificultad que implican sus patrones espaciales heterogéneos y difusos y su compleja dinámica temporal. En este trabajo se determinaron 3 nuevas zonas potenciales de SGD mediante la identificación de anomalías térmicas en imágenes de la temperatura de la superficie del mar (SST) obtenidas del procesamiento de imágenes satelitales termales Landsat 8-9 TIRS. La metodología de identificación de las anomalías se realizó a través de 2 análisis; uno de imágenes únicas y otro de imágenes multitemporales con el cálculo pixel-a-pixel de la desviación estándar y el rango de la SST, parámetros estadísticos cuyos valores bajos indican baja variabilidad, lo cual es un indicativo del aporte de aguas subterráneas con temperaturas más estables en el tiempo., Submarine Groundwater Discharges (SGDs) are crucial for preserving the health of coastal ecosystems by supplying nutrients; however, they can also be a source of contaminants. In Catalonia, few studies have been conducted on SGD due to the difficulty posed by their heterogeneous and diffuse spatial patterns and their complex temporal dynamics. In this work, three new potential SGD zones were identified by detecting thermal anomalies in Sea Surface Temperature (SST) images obtained from processing Landsat 8-9 TIRS thermal satellite imagery. The anomaly identification methodology involved two analyses: one using single images and another using multi-temporal images with pixel-by-pixel calculation of SST standard deviation and range, where low values indicate low variability, indicative of groundwater contributions with more stable temperatures over time.

Aquest és un treball final del Grau en Enginyeria de Sistemes Audiovisuals cursat a la Universitat Politècnica de Catalunya. Està directament relacionat amb els coneixements adquirits a la matèria de Modelització, Complexitat i Sostenibilitat. Consisteix primerament en la creació d’un model basat en agents centrat en el cas de mobilitat urbana de la zona de vianants del barri Centre de la ciutat de Terrassa, codificant des de zero la totalitat d’un programa capaç de modelitzar situacions realistes en un context com aquest. S’enfoca en poder observar els patrons de mobilitat que es poden generar a partir d’interaccions entre vianants i vehicles rodats mecànics i/o elèctrics d’ús individual. Al tractar-se d’un procés de modelització, els resultats obtinguts en el procés s’exposen com a possibles escenaris representatius d’una situació hipotètica. En cap cas s’intenta afirmar que les dades han d’igualar-se a la realitat de forma directa. Posteriorment, es realitza un estudi exhaustiu de les dades obtingudes a partir de les execucions del model creat, anàlisi amb la qual es pretén conèixer els factors del model més importants que afecten al nombre d’accidents produïts a les simulacions i alhora identificar les possibles maneres més eficients de reduir-los. El projecte s’emmarca en el context d’un tipus de problema de mobilitat que actualment és molt present a ciutats grans i urbanitzades, i que fa que es generin notables problemes socials i de salut pública. En conseqüència, aquest treball d’enginyeria s’emmarca en l’àmbit de la sostenibilitat, la modelització i la complexitat, en un context global d’augment demogràfic i de densitat de població concentrada a grans ciutats., This is a final project for the Degree in Audiovisual Systems Engineering taken at the Universitat Politècnica de Catalunya. It is directly related to the knowledge acquired in the subject of Modelling, Complexity and Sustainability. It consists first of all in the creation of an agent-based model focused on the case of urban mobility in the pedestrian area of the Centre neighborhood from Terrassa city, coding from scratch the entirety of a program capable of modeling realistic situations in a context like this. It focuses on being able to observe the mobility patterns that can be generated from interactions between pedestrians and mechanical and/or electric vehicles for individual use. As it is a modeling process, the results obtained in the process are presented as possible scenarios representative of a hypothetical situation. In any case is it attempted to affirm that the data must equal reality directly. Subsequently, an exhaustive study of the data obtained from the executions of the created model is carried out, an analysis with which it is intended to know the most important factors of the model that affect the number of accidents produced in the simulations and at the same time to identify the most efficient ways to reduce them. The project is framed in the context of a type of mobility problem that is very present in large and urbanized cities, and which generates notable social and public health problems. Consequently, this engineering work is framed in the field of sustainability, modeling and complexity, in a global context of demographic increase and population density concentrated in large cities.

Rural areas are pivotal in the global economy, hence rural development planners and decision makers face significant technical and information management challenges, hindering their ability to assess sustainable development and formulate strategies. These challenges, coupled with shifts in traditional rural activities, aging populations, and climate change impacts, create inequalities and threaten rural communities. Therefore, developing approaches for the assessment of changes in rural areas are required. In recent years, remote sensing has emerged as a useful tool for monitoring the complex dynamics of rural regions, with a notable increase in the accessibility and use of freely available satellite data and platforms for tracking changes in land use and land cover (LULC). In light of this trend, this article analyzes the spatio-temporal trends in eight different development regions in Japan, Germany, Poland and Spain and integrates them with population data. This research relied on the utilization of openly available datasets - satellite imagery from Landsat and WorldPop population data, processed in Google Earth Engine (GEE). The time series of Landsat imagery were employed to analyze changes in LULC over a 20-year period from 2000 to 2020 in each test site. Unique combinations (i.e. each pixel history) were systematically examined and categorized based on established rules, with improbable scenarios marked as "uncertain." The comparison demonstrates that all eight regions have unique spatial, temporal and thematic characteristics. The most significant changes observed include the transformation of tree-covered areas into built-up areas in the case of Tsukuba and into agricultural lands in Shibushi. In the Bodzentyn and Polaniec communes in Poland, agricultural lands have transitioned into forested areas. In Spain, there is an increase in built-up areas, particularly at the outskirts of established towns. In Germany, the results indicate an expansion of built-up, Peer Reviewed, Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles, Postprint (author's final draft)

La conservación de las zonas naturales es esencial para mantener el equilibrio ecológico de nuestro planeta y preservar la biodiversidad. Estas áreas desempeñan un papel fundamental en la regulación del clima y la provisión de hábitats para innumerables especies. En este contexto, la teledetección se ha convertido en una herramienta valiosa para la gestión sostenible de estos espacios. Específicamente, este trabajo aborda la evaluación del estado de conservación de un bosque de laurisilvas a partir de datos multiplataforma (satélite, avión y dron) de alta y muy alta resolución. Se ha realizado un detallado estudio y evaluación de índices de vegetación, se han aplicado los correspondientes procesados, usando modelos avanzados de corrección y clasificación y se ha generado información relativa al estado de salud del ecosistema, a la estructura del dosel arbóreo y a la distribución de los sistemas naturales de vegetación predominantes (bosques de laurisilva y de fayal-brezal). Los resultados de este trabajo están siendo utilizados para el seguimiento ecológico del Parque y para la identificación de las zonas de desvitalización y de pérdida de vigor vegetal., The conservation of natural areas is essential to maintain the ecological balance of our planet and preserve biodiversity. These areas play a critical role in regulating the climate and providing habitats for countless species. In this context, remote sensing has become a valuable tool for the sustainable management of these spaces. Specifically, this work addresses the evaluation of the conservation status of a laurel forest based on high and very high resolution multiplatform data (satellite, plane and drone). Basically, a detailed study and evaluation of vegetation indices has been carried out, the corresponding processing has been applied, using advanced correction and classification models, and information has been generated regarding the state of health of the ecosystem, the structure of the tree canopy and the distribution of the predominant natural vegetation systems (laurisilva and fayal-brezal forests). These results are being used for the ecological monitoring of the Park and for the identification of areas of devitalization and loss of plant vigor., Este trabajo ha sido financiado por el Organismo Autónomo Parques Nacionales (Proyecto SPIP2022-02897)., Peer Reviewed, Postprint (published version)

Los desprendimientos rocosos representan un riesgo significativo para la seguridad de las personas y las infraestructuras, son los procesos de inestabilidad de laderas que causan un mayor número de víctimas en España y considerables pérdidas económicas. A pesar de la evidente importancia de este fenómeno, hay numerosas investigaciones relacionadas con los desprendimientos rocosos, pero pocas abordan específicamente el análisis de la exposición y la vulnerabilidad a estos acontecimientos. Se considera la necesidad de avanzar en el conocimiento y la cuantificación de la exposición a estos fenómenos naturales para minimizar los daños en personas y bienes. Dado el alto nivel de vulnerabilidad humana en ciertas áreas, es indispensable un análisis detallado del factor exposición para evaluar el riesgo geológico y tomar medidas de mitigación apropiadas. La complejidad del cálculo de desprendimientos rocosos involucra múltiples factores interrelacionados, incluidos topografía del terreno, la geología subyacente y las características geomecánicas de las formaciones rocosas en la zona de estudio. Estos elementos determinan la estabilidad de los escarpes y la posible generación de desprendimientos. Realizar un análisis completo de todos estos factores en zonas de estudio extensas, representa un gran desafío ya que suele requerir un trabajo de campo detallado que implique la recopilación de datos geológicos, topográficos y geotécnicos. Dado que puede ser un trabajo costoso y consumir mucho tiempo, una solución más práctica es adoptar enfoques simplificados y metodologías computacionales para evaluar el riesgo de desprendimientos rocosos, que permitan obtener resultados significativos sin la necesidad de un análisis detallado en el terreno. En este proyecto se sigue una metodología basada en los trabajos previos de Campiño (2021) y Zaldívar (2023) para la evaluación cuantitativa del riesgo de desprendimientos rocosos. Se emplea la herramienta de simulación de desprendimientos Ro

The Earth surface Mineral dust source InvesTigation (EMIT) is a remote visible to shortwave infrared (VSWIR) imaging spectrometer that has been operating onboard the International Space Station since July 2022. This article describes EMIT's on-orbit spectroradiometric calibration and validation. Accurate spectroscopy is vital to achieve consistent mapping results with orbital imaging spectrometers. EMIT takes a unique approach to this challenge, with just six optical elements, no shutter, and no onboard calibration systems. Its simple design focuses on uniformity and stability to enable vicarious spectroradiometric calibration. Our experiments demonstrate that this approach is successful, approaching the fidelity of manual field spectroscopy in some cases, and enabling new and more accurate products across diverse Earth science disciplines. EMIT achieves several notable firsts for an instrument of its class. It demonstrates successful on-orbit adjustments of Focal Plane Array (FPA) alignment with sub-micron precision. It offers spectral uniformity better than 98%. Optical artifacts in the measurement channels are at least three orders of magnitude below the primary solar-reflected surface signals. Its noise performance enables percent-level discrimination in the depths of mineral absorption features. In these aspects, EMIT satisfies the stringent performance needs for the next generation of VSWIR imaging spectrometers to observe the Earth's ecosystems, geology, and water resources., EMIT is supported by the National Aeronautics and Space Administration Earth Venture Instrument program, under the Earth Science Division of the Science Mission Directorate. K. Dana Chadwick is supported by the NASA Applied Sciences Program. Carlos P´ erez García- Pando and María Gonçalves Ageitos acknowledge support from the European Research Council (ERC) Consolidator Grant FRAGMENT (grant agreement No. 773051), and the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center funded by the AXA Research Fund. Martina Klose has received funding through the Helmholtz Association’s Initiative and Networking Fund (grant agreement No. VH-NG-1533). We thank Jeffrey Czapla-Myers and the University of Arizona team for their maintenance and operation of the Railroad Valley automated calibration facility. This research was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. We acknowledge the support and assistance of NASA’s International Space Station Program. The USGS authors’ contribution to this published Work was prepared by U.S. federal government employees as part of their official duties and constitutes a “work of the United States government,” and is considered to be in the public domain and therefore domestic copyright does not apply. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Copyright 2024 California Institute of Technology. All rights reserved. US Government Support Acknowledged., Peer Reviewed, Article signat per 56 autors: David R. Thompson, Robert O. Green, Christine Bradley, Philip G. Brodrick, Natalie Mahowald, Eyal Ben Dor, Matthew Bennett, Michael Bernas, Nimrod Carmon, K. Dana Chadwick, Roger N. Clark, Red Willow Coleman, Evan Cox, Ernesto Diaz, Michael L. Eastwood, Regina Eckert, Bethany L. Ehlmann, Paul Ginoux, María Gonçalves Ageitos, Kathleen Grant, Luis Guanter, Daniela Heller Pearlshtien, Mark Helmlinger, Harrison Herzog, Todd Hoefen, Yue Huang, Abigail Keebler, Olga Kalashnikova, Didier Keymeulen, Raymond Kokaly, Martina Klose, Longlei Li, Sarah R. Lundeen, John Meyer, Elizabeth Middleton, Ron L. Miller, Pantazis Mouroulis, Bogdan Oaida, Vincenzo Obiso, Francisco Ochoa, Winston Olson-Duvall, Gregory S. Okin, Thomas H. Painter, Carlos Pérez García-Pando, Randy Pollock, Vincent Realmuto, Lucas Shaw, Peter Sullivan, Gregg Swayze, Erik Thingvold, Andrew K. Thorpe, Suresh Vannan, Catalina Villarreal, Charlene Ung, Daniel W. Wilson, Sander Zandbergen., Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Postprint (published version)

The Global Navigation Satellite Systems (GNSS), including the US’s GPS, China’s BDS, the European Union’s Galileo, and Russia’s GLONASS, offer real-time, all-weather, any-time, anywhere and high precision observations by transmitting L band signals continuously, which have been widely used for positioning, navigation and timing. With the development of GNSS technology, it has been found that GNSS-reflected signals can be used to detect Earth’s surface characteristics together with other signals of opportunity. In this paper, the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry (GNSS-R) in theory, methods, techniques and observations. New developments and progresses in GNSS-R instruments, theoretical modeling, and signal processing, ground and space-/air-borne experiments, parameters retrieval (e.g. wind speed, sea surface height, soil moisture, ice thickness), sea surface altimetry and applications in the atmosphere, oceans, land, vegetation, and cryosphere are given and reviewed in details. Meanwhile, the challenges in the GNSS-R development of each field are also given. Finally, the future applications and prospects of GNSS-R are discussed, including multi-GNSS reflectometry, new GNSS-R receivers, GNSS-R missions, and emerging applications, such as mesoscale ocean eddies, ocean phytoplankton blooms, microplastics detection, target recognition, river flow, desert studies, natural hazards and landslides monitoring., This work was supported by the Henan International Science and Technology Cooperation Key Project (Grant No. 241111520700) and Strategic Priority Research Program Project of the Chinese Academy of Sciences (Grant No. XDA23040100)., Peer Reviewed, Postprint (published version)

This article proposes and experimentally characterizes two implementations of a novel front-end circuit for three-wire connected resistive sensors with a wire-resistance compensation. The first implementation relies on two twin diodes, whereas the second on a switch; in both cases, those devices are non-remote (i.e., they are placed at the circuit end). The two circuit proposals have a square-wave input excitation so that a constant current with the two polarities is alternatively generated. Then, depending on that polarity, the current goes through either the sensor and the wire parasitic resistances or just the parasitic resistances. This generates a square-wave bipolar output signal whose average value, which is obtained by a low-pass filter, is proportional to the sensor resistance and only depends on the mismatch between two of the three wire resistances involved. Experimental tests applied to resistances related to a Pt100 thermal sensor show a remarkable linearity. For example, the switch-based front-end circuit offers a non-linearity error lower than 0.01% full-scale span, and this is practically insensitive to both the presence and the mismatch between the wire resistances., Peer Reviewed, Postprint (published version)

The multi-temporal polarimetric SAR (PolSAR) data contains the scattering change information during the growth of crops. However, the current classification methods usually directly use the addition of features extracted at single-temporal or use the temporal and spatial variations of certain features, not really exploring the complete scattering variation information. The specific data representation models for multi-temporal PolSAR data should combine time with polarimetry to characterize the scattering variations. However, the characterization and utilization of such kind of models are inadequate. In this article, we construct data representation model based on the power form of coherence matrix to comprehensively represent all kinds of scattering mechanism variation, which is full-rank positive semi-definite Hermitian matrix. We extract new time-variant scattering features and design vision transformer classifier accordingly for crop classification. Experiment results on RADARSAT-2 datasets show that the proposed power representation model outperforms other models., Peer Reviewed, Postprint (published version)

This thesis investigates the use of remote sensing techniques to study the conditions of ballast water in the northwestern port of Emden, Germany. Sentinel-2 data of the seaport has been collected and used in order to retrieve the suspended particulate matter (SPM), evaluating its distribution and checking for any time or spatial periodicity over the past 5 years. The aim of the study is to provide insights into the variability of ballast water conditions and identify potential areas of concern when loading the tanks of their ships, avoiding the spread of invasive aquatic species into new bioregions. Overall, this research highlights the potential of remote sensing techniques as an alternative to ground infrastructure in monitoring and managing ballast water conditions in ports and other aquatic environments.

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)

This article introduces a MATLAB-based radiometric model as a crucial initial step in evaluating the feasibility Differential Absorption Lidar (DIAL) systems for remote sensing of minor gaseous atmospheric constituents such as methane, a potent greenhouse gas. This model offers flexibility in parameter customization, considering factors like target gas, wavelength range, and atmospheric conditions, enabling to optimize system parameters, assess performance, and gain insights into improving measurement accuracy. We validate the model and find it capable of retrieving lidar signals for methane sensing concentrations of $2.085$ ppm over 800-meter distances using an OPO lidar system at around $3 \ \mathrm{\mu m}$. We explore two DIAL approaches, range-resolved and IPDA, each suited for specific scenarios. Furthermore, we highlight that this simulation framework extends beyond methane, from $\mathrm{CO_2}$ to alcohol vapors. This article serves as a valuable resource for researchers and practitioners in atmospheric science and remote sensing, offering insights into DIAL system feasibility and optimization.

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA) payload was selected in 2019 the 2nd GRSS Student Grand Challenge to fly onboard AlainSat-1, a 3U CubeSat developed at the National Space Science and Technology Center (NSSTC) in United Arab Emirates. RITA payload includes a passive Microwave Radiometer (MWR), a LoRa transceiver, and an hyper-spectral camera. The objective of the payload is to retrieve Earth Observation (EO) parameters such as: soil moisture, sea-ice thickness and concentration, among others. A Radio Frequency Interference (RFI) detection algorithm is also included to the payload to create interference maps in the received signals., This project is supported and funded by the IEEE Geoscience and Remote Sensing Society (GRSS), as one of the winners of the 2nd IEEE GRSS Student Grand Challenge, and supported grants for recruitment of early-stage research staff of Agencia Gestió d’Ajuts Universitaris i de Recerca (AGAUR) Generalitat de Catalunya, Spain (FI2019/1183, FISDUR2020/105, FI2022/809, and FI2023/239). This article was part of the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigacion (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”., Peer Reviewed, Postprint (author's final draft)

The reduction of phytosanitary products in current agriculture is one of its top priorities for the future. To achieve this, technologies are being developed to reduce phytosanitary volumes in crops. One of these technologies is PIVOS (Intelligent Spraying for Sustainable Vineyard and Olive Growing), a project developed by the Agricultural Mechanization Unit of the Polytechnic University of Catalonia (UMA-UPC). It is based on a multi-line sprayer for the vineyard equipped with ISO-02 nozzles and a system of wide-pulse modulation valves (PWM) that emit the same working pressure but regulate the flow that exits the nozzle according to the volume of vegetation in the vines in the area. This is known as the duty cycle (CT) related to the pulse width at a frequency of 10 Hz, indicating that the valve operates within intervals of 100 milliseconds (ms). The machine's detection of vegetation volume is made possible thanks to the incorporation of an onboard computer loaded with a prescription map based on the vigor of the plot's vegetation to be worked on. This prescription map allows us to divide the plot into two zones: high vigor zone and low vigor zone, where the application method varies depending on the zone we are in. This application method is known as Variable Rate Application (VRA), and its behaviour and effectiveness have been studied compared to the reference application done by the farmer every campaign, evaluating the biological efficacy of both treatments. Two treatments were conducted during the summer season using the aforementioned methodologies, the first in June and the second in July. The results of both trials were positive as no signs of fungal diseases such as Powdery Mildew or Downy Mildew were found on the vine leaves, both for the reference treatment and the VRA. This has allowed us to affirm that the VRA treatment using PWM technology is equally effective as the reference treatment, but with the difference in the phytosanitary product used, where, La reducción de productos fitosanitarios en la agricultura actual es uno de los objetivos prioritarios para el futuro de esta, para ello se están desarrollando tecnologías que permiten reducir los volúmenes de fitosanitario en los cultivos. Una de estas tecnologías es PIVOS (Pulverización inteligente para un viñedo y olivar sostenibles), un proyecto desarrollado por la Unidad de Mecanización Agraria de la Universidad Politécnica de Cataluña (UMA-UPC) que se basa en un pulverizador multifila para el viñedo equipado con boquillas ISO-02 y con un sistema de válvulas de modulación de pulsos anchos (PWM) que emiten la misma presión de trabajo pero regulando el caudal que sale por la boquilla según el volumen de vegetación de las cepas que haya en la zona. Esto se conoce como ciclo de trabajo (CT) que viene relacionado con el ancho del pulso a una frecuencia de 10 Hz), que nos quiere decir que la válvula trabaja dentro de intervalos de 100 milisegundos (ms). El volumen de vegetación que detecta la máquina lo hace posible gracias a la incorporación de un ordenador a bordo que lleva cargado un mapa de prescripción según la vigorosidad de la vegetación de la parcela a trabajar. En este mapa de prescripción nos permite dividir la parcela en dos zonas: zona de vigor alto y zona de vigor bajo, donde la manera de aplicar es variable en función de la zona en la que nos encontremos. Este método de aplicación se conoce como VRA y se ha estudiado su comportamiento y eficacia frente a la aplicación de referencia que hace el agricultor todas las campañas, donde se ha valorado la eficacia biológica de ambos tratamientos. Se han realizado dos tratamientos durante la temporada de verano utilizando las dos metodologías mencionadas anteriormente, el primero se realizó en el mes de junio y el segundo fue en julio. Los resultados de los dos ensayos fueron positivos ya que no se han encontrado indicios de enfermedades fúngicas como Oídio o Mildiu en las hojas de las viñas tanto para el tratam, La reducció de productes fitosanitaris a l'agricultura actual és un dels objectius prioritaris per al futur d'aquesta. Per a aconseguir-ho, s'estan desenvolupant tecnologies que permeten reduir els volums de fitosanitari en els cultius. Una d'aquestes tecnologies és PIVOS (Pulverització intel·ligent per a un conreu de vinya i olivera sostenible), un projecte desenvolupat per la Unitat de Mecanització Agrària de la Universitat Politècnica de Catalunya (UMA-UPC). Es basa en un atomitzador multifila per a la vinya equipat amb brolladors ISO-02 i amb un sistema de vàlvules de modulació de polsos amples (PWM) que emeten la mateixa pressió de treball, però regulant el cabal que surt pel brollador segons el volum de vegetació de les ceps de la zona. Això es coneix com a cicle de treball (CT) i està relacionat amb l'amplada del pols a una freqüència de 10 Hz, el que significa que la vàlvula treballa dins d'intervals de 100 mil·lisegons (ms). El volum de vegetació que detecta la màquina és possible gràcies a la incorporació d'un ordinador a bord que porta carregat un mapa de prescripció segons la vigorositat de la vegetació de la parcel·la a treballar. Aquest mapa de prescripció ens permet dividir la parcel·la en dues zones: zona de vigor alt i zona de vigor baix, on la manera d'aplicar és variable segons la zona en la qual ens trobem. Aquest mètode d'aplicació es coneix com a VRA i s'ha estudiat el seu comportament i eficàcia enfront de l'aplicació de referència que fa l'agricultor a totes les campanyes, on s'ha avaluat l'eficàcia biològica d'ambdós tractaments. S'han realitzat dos tractaments durant la temporada d'estiu utilitzant les dues metodologies esmentades anteriorment. El primer es va realitzar al mes de juny i el segon va ser al juliol. Els resultats dels dos assaigs van ser positius ja que no s'han trobat indicis d'afectacions fúngiques com l'Oïdi o el Míldiu a les fulles de les vinyes, tant pel tractament de referència com pel VRA. Això ens ha permès afirmar que, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Objectius de Desenvolupament Sostenible::17 - Aliança per a Aconseguir els Objetius

The versatile Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm exploits the advantages of synergic ground-based aerosol observations such as radiometric (sensitive to columnar aerosol optical and microphysical properties) and lidar (sensitive to vertical distribution of the optical properties) observations. The synergy is possible when the complementary data is mutually constrained by GRASP parametrization that includes, for the first time ever, the degree of linear polarization (DoLP) parameter measured by a polarized sun-sky-lunar AERONET photometer (380, 440, 500, 675, 870, 1020, and 1640 nm) in synergy with the vertical profiles from an elastic lidar (355, 532, and 1064 nm). First, a series of numerical tests is performed using simulated data generated using a climatology of data and ground-based measurements. The inversions are performed with and without random noise for five different combinations of input data, starting from the AERONET-like dataset and increasing to the complex one by adding more information for three aerosol scenarios: I—high aerosol optical depth (AOD) with dominant coarse mode; II—low AOD with dominant coarse mode; III—high AOD with dominant fine mode. The inclusion of DoLP improves (i) the retrieval accuracy of the fine-mode properties when it is not dominant; (ii) the retrieval accuracy of the coarse-mode properties at longer wavelengths and that of the fine-mode properties at shorter wavelengths; (iii) the retrieval accuracy of the coarse-mode real part of the refractive index (up to 36% reduction), but has no effect on the retrieval of the imaginary part; (iv) reduces up to 83% the bias of the sphere fraction (SF) retrieval in coarse-mode dominated regimes; and (v) the root mean square error (RMSE) of the retrieval for most of the parameters in all scenarios. In addition, the addition of more photometer channels in synergy with a three-wavelength elastic lidar reduces the RMSE for the real part (67% in the coa, Funding: This research was funded by: the Spanish Ministry of Science and Innovation (grant no. PID2019-103886RB-I00); H2020 (grant nos. 654109, 778349, 871115 and 101008004); and Horizon Europe REALISTIC project (grant no. 101086690)., Peer Reviewed, Postprint (published version)

El problema de la congestión del tráfico tiene un impacto negativo en el desarrollo sostenible y la habitabilidad de las ciudades, por lo tanto, abordar el problema de la congestión del tráfico es crucial para el futuro desarrollo de la ciudad. Este estudio tiene como objetivo explorar la aplicación innovadora de la teledetección y los sistemas de información geográfica en el campo del desarrollo sostenible de las ciudades, con el fin de proporcionar una comprensión más profunda del sistema de transporte urbano. Mediante el uso de la capacidad de análisis integral de la teledetección y los sistemas de información geográfica, este estudio se centra en la recopilación de datos abiertos del gobierno de Barcelona y realiza un análisis exhaustivo utilizando la tecnología QGIS. A través de este proceso de análisis, podemos evaluar a fondo las causas fundamentales de la congestión del tráfico y los accidentes de tráfico, y determinar con precisión la interrelación entre los diferentes factores de influencia. Basándonos en estos resultados de evaluación, proporcionamos medidas viables para abordar los problemas de accidentes y congestión del tráfico en Barcelona, sentando así las bases para el desarrollo sostenible del sistema de transporte urbano. Los logros importantes de este estudio también incluyen la identificación de direcciones de investigación prometedoras para el futuro.

This study explores the potential of RGB image data for forest fire detection using deep learning models, evaluating their advantages and limitations, and discussing potential integration within a multi-modal data context. The research introduces a uniquely comprehensive wildfire dataset, capturing a broad array of environmental conditions, forest types, geographical regions, and confounding elements, aiming to reduce high false alarm rates in fire detection systems. To ensure integrity, only public domain images were included, and a detailed description of the dataset’s attributes, URL sources, and image resolutions is provided. The study also introduces a novel multi-task learning approach, integrating multi-class confounding elements within the framework. A pioneering strategy in the field of forest fire detection, this method aims to enhance the model’s discriminatory ability and decrease false positives. When tested against the wildfire dataset, the multi-task learning approach demonstrated significantly superior performance in key metrics and lower false alarm rates compared to traditional binary classification methods. This emphasizes the effectiveness of the proposed methodology and the potential to address confounding elements. Recognizing the need for practical solutions, the study stresses the importance of future work to increase the representativeness of training and testing datasets. The evolving and publicly available wildfire dataset is anticipated to inspire innovative solutions, marking a substantial contribution to the field, Postprint (published version)

Los sistemas forestales son considerados de alta importancia como sumideros de carbón, dada a la capacidad de acumular el CO2 en su biomasa de alrededor de 0,5 kg de carbono por kg de biomasa (IPCC, 2022). De ahí que la gestión de los bosques sea relevante para potenciar la capacidad de mitigar el cambio climático. Por tanto, estimar la biomasa es considerado por el REDD+ (2023) como de alta importancia para el estudio herramientas que posibiliten la gestión forestal, el aumento de espacios naturales y depósitos de carbón en la lucha contra la emergencia climática y la conservación de la biodiversidad. Existes diferentes metodologías estudiadas para estimar la biomasa aérea (AGB), pero que por su complejidad, duración o alto coste económico, dificulta su aplicación. Es por esto que, en este trabajo se usó la teledetección, juntos con sistemas de aprendizaje automático para la investigación del monitoreo forestal, desarrollando modelos de estimación de la AGB que usan desde una sola banda espectral, hasta índices de vegetación de imágenes satelitales Landsat y multiespectral de alta definición, para estimar valores de AGB. Estos modelos se evaluaron mediante la metodología del error de raíz cuadrática media (RMSE) y como variables de campo fue utilizados los levantamientos realizados en el Inventario Nacional forestal (INF4), además de la AGB calculada en campo de la finca Can Poc Olí, donde se lleva a cabo un proyecto de renaturalización del ayuntamiento de Manresa. Como resultado, el mejor modelo Landsat 8 fue el que utilizó la corrección DOS1, con un RMSE = 25,9 Tn/ha. Además, las variables más determinantes en este modelo fueron los índices de vegetación NDVI_P y GNDVI_P, contrario a lo mostrado en el modelo obtenido con la imagen multiespectral, puesto que las variables más importantes en este caso fueron las bandas Red y Nir. Asimismo, se evidenció una mejora utilizando imágenes multiespectral, puesto que se logró RMSE = 5,78 Tn/ha, Forest systems are considered of high importance as carbon sinks, given the capacity to accumulate CO2 in their biomass of about 0 ,5 kg of carbon per kg of biomass (IPCC, 2022). Hence, forest management is relevant to enhance the capacity to mitigate climate change. Therefore, estimating biomass is considered by REDD+ (2023) to be of high importance for the study of tools to enable forest management, the enhancement of natural areas and carbon stocks in the fight against climate emergency and biodiversity conservation. There are different methodologies studied to estimate aboveground biomass (AGB), but due to their complexity, duration or high economic cost, it is difficult to apply them. This is why, in this work, remote sensing was used together with machine learning systems for forest monitoring research, developing AGB estimation models that use from a single spectral band to vegetation indices from Landsat satellite images and high-definition multispectral images to estimate AGB values. These models were evaluated using the methodology of root mean square error (RMSE) and the field variables used were the surveys carried out in the National Forest Inventory (INF4), in addition to the AGB calculated in the field of the Can Poc Olí estate, where a renaturation project of the Manresa City Council is being carried out. As a result, the best model Landsat 8 was the one using the DOS1 correction, with an RMSE = 25.9 Tn/ha. In addition, the most important variables in this model were the vegetation indices NDVI_P and GNDVI_P, contrary to what was shown in the model obtained with the multispectral image, since the most important variables in this case were the Red and Nir bands. Also, an improvement was evidenced using multispectral images, since RMSE = 5.78 Tn/ha

The radio occultation (RO) measurements of the Global Navigation Satellite System’s (GNSS’s) signals onboard a Low Earth Orbiting (LEO) satellite enable the computation of the vertical electron density profile from the LEO satellite’s orbit height down to the Earth’s surface. The ionospheric extension experiment performed by the GNSS Receiver for Atmospheric Sounding (GRAS) receiver on board MetOp-A provides opportunities for ionospheric sounding but with the RO measurements only taken with an impact parameter height below 600 and 300 km within two different experiments, although MetOp-A was flying at an orbit height of about 800 km. Here, we present a model-assisted RO inversion technique for electron density retrieval from such kind of truncated data. The topside ionosphere and plasmasphere above the LEO orbit height are modelled by a Chapman layer function superposed with an exponential decay function representing the plasmasphere. Our investigation shows that the model-assisted technique is stable and robust and can successfully be used to retrieve the electron density values up to the LEO height from the truncated MetOp-A data, in particular when observations are available until 600 km. Moreover, this model-assisted technique is also successful with the availability of a small number of observations of the topside above the peak density height. For observations available only up to 300 km, the accuracy of the retrieved profile is comparable to the one obtained by the data truncated at a 600 km height only when the peak electron density lies below the 250 km altitude level., Peer Reviewed, Postprint (published version)

In this work, a dedicated campaign by MetOp-A satellite is conducted to monitor the ionosphere based on radio-occultation (RO) measurements provided by the onboard GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding (GRAS). The main goal is to analyze the capabilities of the collected data to represent the bending angle and scintillation profiles of the ionosphere. We compare the MetOp-A products with those generated by other RO missions and explore the spatial/temporal distributions sensed by the MetOp-A campaign. Validation of dual frequency bending angles at the RO tangent points, S4 index, and Rate of the Total electron content Index (ROTI) is performed against independent products from Fengyun-3D and FORMOSAT-7/COSMIC-2 satellites. Our main findings constitute the following: (1) bending angle profiles from MetOp-A agree well with Fengyun-3D measurements; (2) bending angle distributions show a typical S-shape variation along the altitudes; (3) signatures of the sporadic E-layer and equatorial ionization anomaly crests are observed by the bending angles; (4) sharp transitions are observed in the bending angle profiles above ~200 km due to the transition of the daytime/nighttime in addition to the transition of the bottom-side/top-side; and (5) sporadic E-layer signatures are observed in the S4 index distributions by MetOp-A and FORMOSAT-7/COSMIC-2, with expected differences in magnitudes between the GPS (Global Positioning System) L1 and L2 frequencies., Peer Reviewed, Postprint (published version)

La Ciénaga Grande de Santa Marta es un humedal ubicado en la parte norte de Colombia, dentro de la cuenca del río Magdalena. El ecosistema se ve afectado constantemente por eventos de sequía debido a sus condiciones hidrometeorológicas, pero también debido a la gestión de los recursos hídricos en la región, y particularmente por la operación de embalses relacionadas con la producción de energía hidroeléctrica aguas arriba de la Ciénaga. El objetivo principal del estudio era la caracterización de los eventos de sequía, para comprender tanto las condiciones actuales del ecosistema, como las dinámicas del mismo, en especial las relacionadas con los flujos de agua. Este proceso se llevó a cabo evaluando diferentes índices tradicionales de sequía, entre los cuales se encuentran el Índice de Palmer y el SPI, y diversas fuentes de información obtenidas a través de sensores remotos. Los parámetros se evaluaron para analizar su desempeño en la identificación de la ocurrencia de las sequías y en la evaluación de su duración y severidad. A través de este análisis, fue posible identificar el PDSI y la salinidad del agua como los parámetros más efectivos para caracterizar los eventos. El uso simultáneo de estos parámetros mejoraría la gestión del riesgo de sequía y de los recursos hídricos en la zona, ya que el Índice de Palmer podría ser utilizado en la modelación y predicción de eventos, mientras que la salinidad del agua se podría utilizar para la categorización y la evaluación de sus impactos, Además de la caracterización de la sequía, el estudio se centró en el análisis de parámetros externos y no tradicionales que podrían tener un impacto relevante en el equilibrio natural del sistema y en la propuesta de los lineamientos básicos para la creación de una plan de gestión de riesgos (Plan de Manejo de Sequías). Durante el desarrollo de la investigación se evaluaron las complejas dinámicas de los eventos de sequía en la región y las implicaciones de los planes de manejo de los, The Ciénaga Grande de Santa Marta is a wetland located in the northern part of Colombia inside of the Magdalena River basin. The ecosystem experiences recurrent drought events due to hydrometeorological conditions and water management practices, particularly reservoir operations related to hydropower production. The main objective of the research was the characterization of the drought events in order to understand the conditions and dynamics of the system. This process was conducted evaluating different traditional drought indices, among which the Palmer Index and the SPI, and diverse sources of remote sensing. These parameters were evaluated in order to analyze their performance in the identification of the occurrence of the droughts and in the evaluation of their duration and severity. Through this analysis it was possible to identify the Palmer Drought Severity Index and water salinity as the most effective parameters for characterizing droughts, outperforming the traditional methodologies. The simultaneous use of these parameters would improve the drought risk and the water management in the area, as the Palmer Index aids event forecasting, while water salinity provides insights into impact categorization while also improving the understanding of the water dynamics of the system. Besides the drought characterization, the study focused on the analysis of external and non-traditional parameters that could have a relevant impact on the natural equilibrium of the system and in the proposal of the guidelines for the creation of a risk management strategy (Drought Management Plan). The research assessed the complex dynamics of droughts in the region, while also discussing the implications on droughts of the regional water management practices in the Magdalena River Basin, setting a possible starting point for future studies and discussions about the translation of the existing knowledge into practical actions for the conservation of the CGSM.

In the last decades, the number of wireless devices has notably increased, and improper RF filtering has resulted in an increase in Radio Frequency Interference (RFI). At L-band this is due to a plethora of reasons: from VHF/UHF harmonics, higher frequency band (e.g. WiFi boosters) emissions, or even intentional RFI created by Personal Privacy Devices (PPD), all these have resulted in a degradation of both Active and Passive Microwave Remote Sensing [1], [2]. Almost simultaneously, there has been an explosion of smallsats, notably following the CubeSat and PocketQube [3] form factors, both being excellent hands-on project topics for introducing students into the aerospace domain [4].In these circumstances, the IEEE Geoscience and Remote Sensing (GRSS) Open PocketQube Kit [5] initiative was started. The goal is to develop a set of flight ready Picosatellites with Earth Observation payloads complete with assembly, integration and testing tutorials and video tutorials. Each of these PocketQubes, while identical in all other regards, will house different Remote Sensing oriented payloads. One of them, and the focus of this work, is a L-band (1-2 GHz) Radio Frequency Interference Monitor, notably for the L1, L2 and L5-bands of GNSS [6] systems, and the 1400-1427 Mhz band reserved for passive observations., This payload has been developed as part of the IEEE Geoscience and Remote Sensing Society ”Open PocketQube Kit” educational initiative and it will be openly distributed when completed. Additionally, it was part of the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”., Peer Reviewed, Postprint (author's final draft)

In recent years, rooftops have been used as productive spaces for local resource (e.g. food, water, or energy) production. The main objective of this study is to demonstrate the potential of hyperspectral data to identify roof materials in cities. The case study area is located in a Mediterranean region north of Barcelona (Spain); diverse building typologies, building uses (residential, industrial, office, services and retail) and population densities constitute this study area. Two hyperspectral remote sensors (HRSs) were used to obtain hyperspectral images. Seven roof classes were identified using the k-means clustering algorithm. Most roofs were identified as metals (33 percent), followed by ceramic tiles (22 percent). The findings showed that 51 percent of roofs could be suitable for food, water, or energy production. Nevertheless, for a more reliable assessment of this potential, more criteria are needed., This work is part of the Fertilecity II project supported by the Spanish Ministry of Economy and Competitiveness (CTM2016-75772-C3-3-R and CTM2016-75772-C3-1-R, AEI/FEDER, UE); from the Spanish Ministry of Science, Innovation and Universities; through the María de Maeztu program for Units of Excellence (CEX2019-000940-M) and supported by MOVE4EDU. Authors want to thank to the University of Guadalajara (Mexico) for awarding a research scholarship to Perla Zambrano-Prado, and to the reviewers for their valuable comments., Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Postprint (author's final draft)

Due to the recent licensing of the 26 GHz 5G communications bands, the passive microeaves Remote Sensing community is concerned that spurious out-of-band emissions could interfere with the measurements acquired in the 23.8 GHz water vapor resonance band. For this reason, the Frequency Allocations in Remote Sensing (FARS) Technical Committee from the IEEE Geoscience and Remote Sensing Society (GRSS) has procured the development of a RFI monitoring payload to detect possible interferences in the 24 to 25 GHz band. This payload is compatible with the PocketQubes being developed in the framework of the "IEEE GRSS Open PocketQube Kit" educational initiative, and it will be used to measure and determine the occurrence of possible interferences close to the 23.8 GHz water vapor band. This work presents the design, implementation and testing of the 24 to 25 GHz RFI monitoring payload designed for a 1P PocketQube., This work was sponsored by the Frequency Allocations in Remote Sensing (FARS) Technical Committee of the IEEE Geoscience and Remote Sensing Society (GRSS). This project was also sposored in part by the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”., Peer Reviewed, Postprint (author's final draft)

Detection of marine litter is an urgent task given the large amount of plastics that end up in all water systems. This manuscript provides the description and results obtained from a ground-based Global Navigation Satellite System - Reflectometry (GNSS-R) system built in a controlled indoor environment intended to test the capabilities of GNSS-R techniques for marine litter detection, when different wave conditions and plastic litter were used. Results show that it may be possible to detect the damping of waves produced by the accumulations of some types of plastic., This article was part of the project ”GENESIS: GNSS Environmental and Societal Missions – Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU FEDER “Una manera de hacer Europa”. Amadeu Gonga (FI2022/809) and Adrian Perez-Portero (FISDUR2020/105) were supported by grants for recruitment of early-stage research funded by the Agencia Gestió d’Ajuts Universitaris i de Recerca (AGAUR) Generalitat de Catalunya, Spain., Peer Reviewed, Postprint (author's final draft)

This thesis is about exploring the possibility to make a system that could be implemented in e-scooters to help their users to use them in a secure manner. The objective of this thesis is to find if it is possible to train a detector to distinguish between pedestrians, bikes and e-scooters and being able to track them. I trained an object detector that classifies between the 3 categories mentioned above and used a multi-object tracking algorithm. The results from the Yolov5 detector results were 0.97 of map0.5 and 0.80 of map0.5:0.95, which for this case are good results. The result from the StrongSORT tracker regarding the HOTA metric was 90.87. We can conclude that the results are good. It is possible to detect, classify and track pedestrians, cyclists, and e-scooter users, but the time taken is too large for an implementation in real-time., Esta tesis es sobre explorar la posibilidad de crear un sistema se pudiese implementar en patinetes eléctricos para ayudar a sus usuarios a hacer un uso seguro de ellos. El objetivo de esta tesis es descubrir si es posible entrenar un detector para que sea capaz de distinguir entre peatones, ciclistas y usuarios de patinete eléctrico y realizar un seguimiento entre imágenes (tracker). He entrenado un detector de objetos que clasifica entre las 3 categorías mencionadas anteriormente y he usado un algoritmo de seguimiento de objetos. Los resultados del detector Yolov5 han sido el map0.5 0,97 y el map0.5:0.95 0,80, los cuales en este caso son buenos resultados. El resultado del tracker StrongSORT respecto a la métrica HOTA fue 90,87. Podemos concluir que los resultados han sido buenos. Es posible detectar, clasificar y realizar un track de peatones, ciclistas y usuarios de patinete eléctrico, pero el tiempo requerido es demasiado largo para una implementación en tiempo real., Amb aquesta tesi es vol explorar la possibilitat de fer un sistema que es pugui implementar en patinets elèctrics per ajudar els seus usuaris a utilitzar-los de forma més segura. L'objectiu d'aquesta tesi és trobar si és possible entrenar un detector per tal que distingeixi entre vianants, bicis i patinets elèctrics i fer un seguiment (tracker). He entrenat un detector d'objectes que classifica entre les tres categories mencionades abans i he fet servir un algorisme de seguiment d'objectes. Els resultats del detector Yolov5 han sigut 0,97 de map0.5 i 0,80 de map0.5:0.95, els quals en aquest cas són bons resultats. El resultat del tracker StrongSORT, respecte a la mètrica HOTA ha sigut 90.87. Podem concloure que els resultats són bons. És possible detectar, classificar i fer un seguiment de vianants, ciclistes i usuaris de patinets elèctrics, però el temps requerit és massa llarg per una implementació en temps real.

Over 14 million tons of plastic end up yearly in the ocean, originating from polluted waterways such as rivers, sewers, or storm waters, or from industrial and nautical activities with improper garbage disposal methods. Due to its long decomposition time, and the presence of currents and gyres, most of plastic debris freely floating in the ocean end up forming large extensions of marine litter known as garbage patches. An example is the Great Pacific garbage patch located in the central North Pacific Ocean covering an area larger than 1.6 million squared kilometers. Some works have recently reported the potential of GNSS-Reflectometry to detect marine plastic litter from space. This study presents the results of a controlled field experiment conducted under the auspices of ESA at the "Atlantic Basin" at the Deltares research institute (Delft, The Netherlands)., Research funded by ESA project GLobal MonItoring of MicroPlastics using GNSS-Reflectometry (GLIMPS) 40001324 88/20/NL/GLC through a subcontract with Deimos Space UK Ltd “GLIMPS Subcontract DMU-UPC Ref. DMU-CP51 no. 2021-015; project “GENESIS: GNSS Environmental and Societal Missions—Subproject UPC”, Grant PID2021-126436OB-C21, sponsored by MCIN/AEI/10.13039/501100 011033/ and EU ERDF “A way to do Europe”, and grant for recruitment of early stage research staff of Agència Gestió d’Ajuts Universitaris i de Recerca (AGAUR) Generalitat de Catalunya, Spain (FISDUR2020/105 and FI2022/809)., Peer Reviewed, Postprint (author's final draft)

En la actualidad, es cada vez más frecuente que la población reciba información de acuerdo con sus preferencias, por lo que cada vez es más común la planificación de dispositivos que faciliten la captación de datos para mejorar el análisis de las preferencias. En los centros comerciales, hoy en día se ha visto la eficiencia en los sistemas de gestión que ofrecen información a través de sensores y cámaras de seguridad, lo que implica la posibilidad de estudiar los diferentes indicadores asociados con las actividades realizadas por los clientes, para entender mejor los movimientos dentro del centro comercial. Con base en ello, se planteó como objetivo realizar un estudio que permita la mejora del layout y tránsito de persones en comercios y centros comerciales. Lo que permitió la realización de un estudio estilo propuesta, y así se consideró que la planificación de un modelo de negocios inmerso en la creación de un sistema de dispositivos que permitan la monitorización de la información seg-mentada sobre patrones de movimiento y compra en centros comerciales y pequeños comercios. Esto, en líneas generales, podría ser beneficioso para la población y podría ser de utilidad para garantizar el crecimiento económico dentro de dichos negocios.

Marine dynamics largely affects the phytoplankton community com- position. The distribution characteristics of phytoplankton can reflect spatio-temporal variability in the marine ecosystem, on the other way around. In this work, we study the relation between remote sensing satellite observations, environmental factors and phytoplankton com- munities. First, we employ network-based unsupervised clustering approaches to identify representative communities using metabarcoding data that has been collected both across depth and surface. Next, we investigate the relation between the detected phytoplankton communities and the environmental parameters (e.g., temperature, salinity, nutrients and so on). Our results show that phytoplankton communities are segregated based on the depth and basin. Additionally, for communities where the majority of samples are gathered from the Atlantic ocean, the nutrient levels are much higher than other communities. To extend this analysis to other years, a scientific ship should collect water samples in different years. This would be very costly and even infeasible for many applications such as analyzing the seasonal changes in plankton communities. Therefore, in the second part of our work, we utilize the fact that the reflected light from the ocean's surface that is captured by a remote sensing satellite has a specific relationship with the plankton composition. To this end, we first cluster the samples that are collected at the surface of the ocean. Next, we apply several machine learning algorithms to classify these representative communities from satellite data. Our top performing classifier reached 0.94 accuracy in leave-one-out cross vali- dation setting. The results show three top important features in predict- ing communities are surface temperature, chlorophyll and particulate organic carbon.

We conducted a study to assess the GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding (GRAS) ionospheric data quality by processing Radio Occultation (RO) observations of ionospheric products. The main objective of the study is to validate ionospheric data generated at EUMETSAT, such as ionospheric bending angle profiles, amplitude and phase scintillations, topside Total Electron Content (TEC) from MetOp-A GRAS instrument as well as generating and validating new ionospheric products derived from GRAS RO observations such as the TEC, rate of TEC and vertical electron density profiles. The assessment is conducted by comparing and evaluating the systematic differences between similar products from other Low Earth Orbit (LEO) satellite missions or from ground-based ionospheric measurements. The study confirms that the GNSS topside and RO observations recorded by the GRAS instrument onboard MetOp satellites are of good quality and are a valuable source of data for ionospheric research., Peer Reviewed, Postprint (published version)

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), together with NASA’s Soil Moisture Active Passive (SMAP) mission, is providing a wealth of information to the user community for a wide range of applications. Although both missions are still operational, they have significantly exceeded their design life time. For this reason, ESA is looking at future mission concepts, which would adequately address the requirements of the passive L-band community beyond SMOS and SMAP. This article proposes one mission concept, TriHex, which has been found capable of achieving high spatial resolution, radiometric resolution, and accuracy, approaching the user needs. This is possible by the combination of aperture synthesis, formation flying, the use of general circular orbits, and alias-free imaging., Peer Reviewed, Postprint (author's final draft)

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the ca, This research received no external funding. The author F.R. was supported by the grant PID2021-124272OB-C22 funded by MCIN/AEI/10.13039/501100011033/ of the Spanish government and by “ERDF A way of making Europe” of the European Union., Peer Reviewed, Postprint (published version)

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas., This research was supported in part by the National Natural Science Foundation of China (Grant No.41874044 and Grant No. 42004011), in part by project G2HOTSPOTS (PID2021-122142OB- I00) from the MCIN /AEI /10.13039 /501100011033 /FEDER, UE and in part by China Postdoctoral Science Foundation (Grant No. 2020M671646). At the same time, the research was also funded by the Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project (B20046) and National Key R&D Program of China (Grant No. 2022YFE0102600)., Peer Reviewed, Postprint (published version)

CubeSats are now serving a wide range of applications beyond their original educational intent. Private companies are deploying large constellations for Earth observation and machine–to–machine communications. Their growing popularity and increased performance have raised the demand for reliability and costs. Today, it is becoming increasingly difficult to find subsystems providers, and the trend is to find fully integrated platforms on the market. Therefore, paradoxically, CubeSats are becoming less accessible to universities and research institutions than a few years ago. To overcome these problems, the PocketQube concept was invented. PocketQubes measure 50 × 50 × 50 mm³ and offer a cost-effective option, notably for education. These picosatellites can perform simple missions like Internet of Things communications or upper-atmosphere observations, ionosphere studies, or signal integrity tasks, while students face design challenges similar to larger satellites. This article presents the IEEE Geoscience and Remote Sensing Society (GRSS) “Open PocketQube Kit” educational initiative. Developed by the NanoSat Lab at the Polytechnic University of Catalonia (UPC), it is an affordable open source educational kit featuring a complete PocketQube structure with all the subsystems: an electrical power supply (EPS), attitude determination and control system (ADCS), an STM32-based onboard computer (OBC), long-range (LoRa) communications, and payload. Three different PocketQube models have been developed: Po Cat-1, with a video graphics array (VGA) camera, and Po Cat-2 and Po Cat-3 for monitoring radio-frequency interference (RFI) at the L-band (1–2 GHz) and K-band (24–25 GHz) to track 5G spectrum emissions., This work was sponsored by the GRSS Directorate of Education and FARS Technical Committee., Peer Reviewed, Postprint (published version)