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The potential of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to retrieve a variety of geophysical parameters has already been demonstrated in numerous studies. In 2021, China successfully launched the Fengyun-3E (FY-3E) polar orbit satellite. It carries the GNSS Occultation Sounder-II (GNOS-II) that can simultaneously receive reflected signals from GPS, BeiDou (BDS), and Galileo (GAL) constellations. Multi-constellation measurement significantly reduces the revisit time of the spaceborne GNSS-R data. This offers the possibility to study the sensitivity of the reflected signals from multi-GNSS constellations to surface parameters at global scale. The main objective of this paper is to analyze the sensitivity of FY-3E GNOS-II surface reflectivity (SR) to soil moisture (SM), vegetation, and surface roughness for different GNSS constellations, and different incidence angles. FY-3E data from May 2023 to October 2023 was collected along with SM data from the Soil Moisture Active Passive (SMAP). The SMAP static auxiliary surface roughness parameter SMAP-h was also collected for subsequent comparisons. Furthermore, the effect of vegetation in the reflected signal was accounted for using the Soil Moisture and Ocean Salinity (SMOS) INRA-CESBIO L-band vegetation optical depth (L-VOD). Afterwards, for each GNSS constellation, the SR were binned as a function of SM, SMAP-h and incidence angle. The results indicate that the sensitivity for the different GNSS constellations is consistent, showing similar behavior. Moreover, this study also reports for the first time the experimentally-computed sensitivity of FY-3E SR to SM under different SMAP-h values. Sensitivity of GNOS-II SR to SM is in general agreement with the values in the previous studies, demonstrating the feasibility of using single-pass multi-GNSS constellation GNSS-R data to retrieve surface parameters such as SM, with shorter revisit times. Furthermore, the results of this study re-emphasize, This research was funded by the National Natural Science Foundation of China Projects (Grant No.42074041); Shaanxi Province Science and Technology Innovation Team (Ref. 2021TD-51); The innovation team of ShaanXi Provincial Tri-Qin Scholars with Geoscience Big Data and Geohazard Prevention (2022); and Fundamental Research Funds for the Central Universities (CHD300102263715). This research has also been partially funded 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”. Zhongmin Ma's work was partly supported by the China Scholarship Council (CSC) through a State Scholarship Fund (No. 202306560073)., Peer Reviewed, Postprint (published version)

Lithosphere–atmosphere–ionosphere coupling models have been widely studied as potential earthquake precursors. After the earthquakes, total electron content (TEC) anomalies propagating away from the epicenter have been identified. Recent evidence has also found that electromagnetic phenomena related to earthquakes might also produce ionospheric anomalies before their occurrence. This study analyzes the correlation between ionospheric intensity scintillation, characterized by the S4 index, and seismic activity in the area around the Coral Sea in the year 2022. The S4 intensity scintillation index derived from COSMIC-2 global navigation satellite system radio occultation estimates ionospheric perturbations and identifies anomalies. The methodology involves applying rigorous filters to ensure reliable S4 estimation, including slant TEC thresholding and night-time filtering. To compare the S4 scintillation index with seismic activity, the mean and the 95th percentile of the S4 values are computed at 1-d intervals. Two case studies of earthquakes in 2022 with magnitudes larger than 6 are presented, as well as a statistical analysis based on the calculation of the confusion matrices, and the receiver operating characteristic curves. A positive correlation between the ionospheric intensity scintillation and the occurrence of earthquakes is found, with a probability of detection exceeding 35.7 % and a false alarm rate of 7.1 %, The work of B.-E. Boudriki-Semlali was supported by an FI Grant: 2021 FI_B 00471 from FIAGAUR 2021. This work was supported in part by project “GENESIS: GNSS Environmental and Societal Missions - Subproject UPC” under Grant PID2021-126436OB-C21 sponsored by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación(AEI)/10.13039/501100011033 and in part by EU FEDER “Una manera de hacer Europa”, Peer Reviewed, Postprint (published version)

This study presents a Digital Twin (DT) that is being created to optimize the use of the available hydric resources, and mitigate the effects of the increasing water shortage in irrigated agriculture in fields in the Urgell channel region (Lleida). A DT is "a virtual representation of an object or system that spans its lifecycle, it is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." It will model the water fluxes using the knowledge of the amounts of water taken in, used, and returned to the environment, and other parameters that impact the water budget, such as atmospheric variables (temperature, water vapor deficit, relative humidity, solar radiance…), surface soil moisture, and evapotranspiration maps, etc. Satellite Earth Observation (EO) data, collocated with in-situ data from a network of 20 soil moisture probes and 2 meteo stations will be used to train the DT. Additionally, a rover-based ground penetrating radar will be used for cross-calibration., This work was partially sponsored by project "AI4WATER: Digital Twins for Irrigated Agriculture," Grant TED2021-131877B-100 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU ERDF "A way to make Europe.", Peer Reviewed, Postprint (author's final draft)

Understanding soil moisture (SM) at high spatio-temporal resolution provides crucial insights across various societal disciplines due to its direct impact on environmental and natural disaster monitoring, weather forecasting, agricultural productivity, and water resource management. In recent decades, a variety of algorithms have been developed to improve the spatial resolution of SM maps from passive sensors (~40 km); however, the resulting maps, often with resolutions around 1 km or even hundreds of meters, still lack the necessary resolution for detailed local analysis. This study addresses this gap by presenting a machine learning methodology aimed at estimating SM at 60-m spatial resolution. A feedforward neural network is employed to capture the relationships among 14 different predictors, including several spectral bands and indices from Sentinel-2, land surface temperature from moderate-resolution spectroradiometer, elevation and slope from shuttle radar topography mission, precipitation from the fifth generation of the European Center for Medium-Range Weather Forecasts Reanalysis for Land, and the sand fraction from SoilGrids250m, with European Space Agency (ESA) Climate Change Initiative (CCI) SM serving as the target variable. The model is trained and applied over the central part of the Iberian Peninsula (38.9 °N–42.5 °N and 3.5 °W–7.2 °W) from 2019 to 2021. At 60-m resolution, the SM maps effectively capture the spatial heterogeneity of the terrain. The temporal analysis demonstrates that high-resolution SM maps preserve virtually the same sensitivity as those from the ESA CCI, with a correlation of 0.66, a bias of 0.095 m 3 /m 3 , and an unbiased root-mean-square error of 0.044 m 3 /m 3 on average., This work was supported in part by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) through the project INTERACT under Grant PID2020-114623RB-C32 and in part by the Institut d’Estudis Espacials de Catalunya (IEEC) through the NewSpace project. The work of Amir M. Irawan was supported by “La Caixa” Foundation (ID 100010434) through a fellowship under Grant LCF/BQ/DI21/11860028., Peer Reviewed, Postprint (published version)

This study presents a methodology for disaggregating the ESA Climate Change Initiative (CCI) Soil Moisture (SM) maps from 0.25° to a 60 m grid, using a feedforward neural network. This technique is applied over an area of 66,700 km2 , encompassing parts of Oklahoma and Kansas (US), throughout 2021. The disaggregation approach leverages synergies between different variables, including various Sentinel-2 bands and indices, land surface temperature from MODIS, accumulated precipitation from ERA5-Land, terrain elevation and slope from the STRM, and soil composition. The methodology employs a two-step process: (i) the model is first trained using all the variables at low resolution (0.25°), and (ii) it is then employed to estimate the SM at high resolution using the input variables at 60 m. Results indicate that the model trained at low resolution achieves a reasonably high accuracy over the testing data (RMSE<0.02 m3 ·m-3 and R 2>0.9). The preliminary analysis of the 60 m resolution SM maps and their comparison with two in-situ stations show a strong correlation (R>0.8), and uRMSE close to 0.04 m3 ·m-3 , with a bias ranging from 0.022 m3 ·m-3 to 0.06 m3 ·m-3 ., This research was supported by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011 033), through the project INTERACT “PID2020-114623RBC32. This work was also supported by the Institute d’Estudis Espacials de Catalunya - IEEC., Peer Reviewed, Postprint (author's final draft)

The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum., This work was supported by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI/10.13039/501100011033) through the projects INTERACT (PID2020-114623RB-C32 and PID2020-114623RBC31), the ICM-CSIC Severo Ochoa Excellence Award (CEX2019- 000928-S), the CSIC Interdisciplinary Thematic Platform (PTI) TELEDETECT, and ERDF funds., Peer Reviewed, Postprint (author's final draft)

This study addresses discerning causal relationships in complex systems, a key aspect of interpretable machine learning. It focuses on the unusual and intense early summer weather in South Asia during April and May 2022 that led to an increased number of forest fires. This work employs a Bayesian network (BN), constructed using the NOTEARS algorithm, to analyse the contribution of various land and atmospheric variables on the extent of burned areas. In a scenario analysis using peak values of 300-hPa meridional circulation index and 500-hPa Geopotential Height Anomalies, indicative of a strong atmospheric block, the likelihood of large burned areas (>3.06 log ha or >1150 ha) increases from 36.6% to 41.6%. This is due to a rise of conditional probabilities in the Vapor Pressure Deficit (VPD) (> 5.21 kPa) by 24.8%, and the Land Surface Temperature (LST) (>45.7°C) by 15.6%. In addition, sensitivity and spatial analyses indicate that extreme dry conditions, characterized by high LST and VPD due to the trapping effects of the omega block jet stream pattern, were the primary factors influencing the extent of burned areas during the 2022 South Asia heatwave., The project that gave rise to these results received the support of a fellowship from the ”la Caixa” Foundation (ID 100010434), with fellowship code LCF/BQ/DI21/11860028. This work was also supported by Project PID2020- 114623RB-C32 funded by MCIN/ AEI /10.13039/501100011033. D. Chaparro has also received funding from the XXXIII Ramón Areces Postdoctoral grant., Peer Reviewed, Postprint (author's final draft)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, The use of flexibility in the form of demand response, storage, or power flow control is a desirable feature for modern electricity distribution systems where uncertainty is a major concern. In this respect, we focus on large-scale irrigation systems characterised by seasonal high-power demand. Considering that most of the year there is a large volume of energy available in the form of stored water, demand flexibility, and the possibility of exchanging energy with the distribution grid are a viable way to provide storage-like network services. However, there is a lack of open source software available for the design and operation of these systems considering all of the physical effects involved. Thus, we introduce an open source Python-based tool to optimise the operation of irrigation systems, as well as size new equipment. Our library benefits from block formulation of the Pyomo package to generate large optimisation problems in a modular way. In this document, a detailed description of the developed package is given along with the presentation and analysis of a study case., We would like to thank professor Xavier Escaler from the Department of Fluid Mechanics at the UPC for his insights on hydraulics, EPRI Europe for their comments and Infraestructures de la Generalitat de Catalunya for the data provided., Postprint (author's final draft)

Radars have been implemented using Software Defined Radios (SDR) for more than a decade. SDRs are convenient in terms of hardware development since the signal generation, conditioning, and post-processing can easily be implemented in hardware. However, signal synchronization remains a major issue, and it can seriously impact the final performance. In this study, the synchronization and the calibration of the transmitted signals in an SDR-based radar transmitting PRN modulated pulse is studied. Time synchronization and jitter are compensated using a "Filter Delay" block, and a peak detector in a reference channel. The amplitude and phase of the peak of the cross-correlation of a sample of the transmitted signal is used to calibrate the received echoes allowing to compensate on a pulse-by-pulse basis the amplitude and phase drifts., This research has been funded by ASPIRE 24N240 grant and United Arab Emirates University., Peer Reviewed, Postprint (author's final draft)

Numerous studies have demonstrated the effectiveness of CYGNSS data for flood detection and mapping. However, the vast majority of the studies only focuses on flood extent detection, ignoring the importance of flood water levels in post-disaster relief. Meanwhile, most of the existing studies on inland water levels altimetry using CYGNSS data are based on the CYGNSS raw IF data using either the time-delay method or the phase method for altimetry. Although high accuracy can be obtained, at present the CYGNSS raw IF data is not a standard data, so it cannot be applied to the study of emergency flooding events. Given the above research gaps, this study proposes an effective method to combine CYGNSS L1 standard data with DTM for rapid estimation of flood water levels. The effectiveness of the proposed methodology is validated using the case of the 2022 Pakistan mega-flood. Comparison with ICESat-2 altimetry data gives encouraging results., This research was funded by the National Natural Science Foundation of China Projects (Grant No.42074041); The National Key Research and Development Program of China (Grant No.2020YFC1512000); State Key Laboratory of GeoInformation Engineering (Grant No. SKLGIE2022-ZZ2-07); The Fundamental Research Funds for the Central Universities (CHD300102263715). This research has also been partially funded 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”. Zhongmin Ma's work was partly supported by the China Scholarship Council (CSC) through a State Scholarship Fund (No. 202306560073)., Peer Reviewed, Postprint (author's final draft)

Earth Observation (EO) using Signals of Opportunity (SoOp), the advent of high-performance and customizable Software Defined Radios (SDRs), and small satellites are revolutionizing today many Remote Sensing techniques. One of the most widely used SoOp are the Global Navigation Satellite System signals used for Radio Occultations (GNSS-RO) and Reflectometry (GNSS-R). In this last one, the reflected GNSS signals acquire properties of the surface where they are reflected, and when these signals are compared to the direct ones, one can infer surface roughness and dielectric constant information. Nowadays SDR-based GNSS-R instruments are becoming more cost-effective, power-efficient, and small enough to be adopted as CubeSats payloads. In GNSS-R receivers, all the information resides in the so-called Delay Doppler Map (DDM), which is the cross-correlation between the reflected signal and the direct one (interferometric GNSS-R or iGNSS-R), or a locally generated replica of the direct one (conventional GNSS-R or cGNSS-R) for different delays and Doppler frequency cuts. Producing these DDMs is computationally intensive due to the large number of Fast Fourier Transforms (FFTs) involved. To develop an efficient GNSS-R instrument, this work presents the development of such an instrument— the first of its kind in the United Arab Emirates (UAE)— and introduces its first operational version. Also, it explores the capture of raw GPS L1 signal, and its processing, having as auxiliary or reference information of the PRN codes of the satellites in view, and their central Doppler frequencies. The coherent and incoherent integration times will also be traded off to enhance the spatial resolution., This research has been funded by an ASPIRE grant 24N240., Peer Reviewed, Postprint (author's final draft)

Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technology that uses GNSS signals reflected over Earth’s surface, offering advantages such as high spatio-temporal resolution and cost-effectiveness. GPS navigation signals convey navigation information through the navigation bits, in which phase jumps occur randomly at 20 ms interval. However, navigation information is meaningless in GNSS-R and limits the coherent integration time. This study presents a technique to mitigate the impact of the navigation bits on the generation of DDMs by using the raw GNSS-R data from the Fengyun-3E (FY-3E) satellite. Sample results show that, after correcting for the navigation bits, the signal-to-noise ratio (SNR [dB]) of DDMs increased by ~0.4 dB over sea to up to 3 dB over sea ice, showing that this improvement is very significant for coherent targets. The proposed navigation bit correcting method in this study significantly improves the usability of these DDMs, and opens the door to other applications that require larger integration times., The authors would like to acknowledge the National Satellite Meteorological Center, China Meteorological Administration for providing the FY-3E/GNOS-II data. This research has also been partially funded by the National Natural Science Foundation of China under Grants 42174022 (PI: Prof. Kegen Yu); the Fundamental Research Funds for the Central Universities 2023XSCX047, Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX23_2752, Graduate Innovation Program of China University of Mining and Technology 2023WLKXJ165 (Author: Changyang Wang); 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 EDRF “A way to make Europe”., Peer Reviewed, Postprint (author's final draft)

© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., As technology advances and factories adopt the industry 4.0 paradigm, the demand for low latency and highly reliable wireless connectivity becomes more crucial than ever. To cater to these challenges multi-link operation (MLO) is a new feature introduced in IEEE 802.11be (Wi-Fi 7) that requires scheduling packets in a station across multiple Wi-Fi links. This paper proposes SLA-MLO, which is a novel MLO scheduler for industrial communications designed to manage per-flow Service Level Agreement (SLA), where an SLA is defined as a maximum allowed percentile latency. We evaluate SLA-MLO by means of NS-3 packet-level simulations and compare it against a state-of-the-art MLO mechanism that schedules packets based on instantaneous per-link congestion measurements. Our results show that SLA-MLO achieves an average reduction in SLA deviation of 50 %., This work is supported by the EU’s H2020 5GSmartFact project under the MSCA grant agreement ID 956670. This work was also partly supported by the Spanish the MCIN/AEI/ 10.13039/501100011050 through project PID2019-106808RA-I00 and MINECO and EU PRTR UNICO I+D number TSI-063000-2021- 15-6GSMART-EZ., Peer Reviewed, Postprint (author's final draft)

The attitude determination and control system of a satellite plays a crucial role, especially in optical communication systems due to the narrow field-of-view of the laser. In the last years, the space ecosystem has shown a large interest in optical technology due to its high speed, reduced size, weight, and power, and reliable communications link. However, its narrow field-of-view puts some challenges in terms of connectivity. For this reason, an optical terminal requires a pointing, acquisition, and tracking system to establish the link. This work proposes the use of a dual-plane communications system in which optics and low throughput radiofrequency technologies are combined in a single module. This hybrid system leverages on the quasi-omnidirectional ability of the radiofrequency antennas to enhance the optical pointing system. The improvement is evaluated by means of a simulator that includes the dynamic pointing between two spacecraft. Results demonstrate that the designed hybrid system can heavily reduce the pointing error., This work was supported and funded by the by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union – NextGeneration EU, in the framework of the Recovery Plan, Transformation and Resilience (PRTR) (Call UNICO I+D 5G 2021, ref. number TSI-063000-2021- 5- 6GSatNet-SS), and by the Government of Catalonia in the scope of the NewSpace Strategy for Catalonia. This work has also partially funded by Ministry of Science and Innovation MICIN/Agencia Estatal de Investigación AEI/10.13039/501100811033 and EU Feder through project “GENESIS: GNSS Environmental and Societal missions” grant PID2021-126436OB-C21., Peer Reviewed, Postprint (author's final draft)

Radio Frequency Interference (RFI) are disturbing signals in the radio frequency spectrum, which can arise from various external sources, intentional or not. These disturbances can degrade the performance of other signals present in the band. One of such bands of remarkable importance is the 23.8 GHz band, used to retrieve the water vapor concentration in the atmosphere. The Frequency Allocations in Remote Sensing (FARS) Technical Committee from the Geoscience and Remote Sensing Society (GRSS) tasked the development and testing of a low-power consumption pico-satellite payload to monitor the possible presence of RFI in the 24 GHz band due to 5G services. This paper presents the testing and characterization of the RFI detection payload., 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)

Storage of interception water in the canopy (S c ) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (M g ) and S c . The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. S c -values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal M g cycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that S c maxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify S c and evaporation fluxes independently from EC measurements and field experiments., Peer Reviewed, Postprint (author's final draft)

With CubeSats having evolved beyond their initial educational purpose and now being employed across a diverse range of applications, more and more private enterprises are deploying large constellations for a plethora of applications. This increased popularity and enhanced performance of the nanosatellite form factor has led to heightened demands for reliability and cost efficiency. Presently, commissioning subsystem providers has become challenging, with a prevailing trend towards seeking fully integrated platforms in the market. Paradoxically, CubeSats are now less accessible to universities and research institutions than they were a few years ago.To address these challenges, the PocketQube concept emerged. Making up the picosatellite class, and measuring 50x50x50 mm 3 , they provide a cost-effective alternative, particularly for educational purposes. Students working with PocketQubes encounter design challenges akin to those faced with larger satellites. This paper comes in continuation of the IEEE-GRSS "Open PocketQube Kit" educational initiative, developed by the NanoSat-Lab at the Universitat Politecnica de Catalunya and is focused along the integration and testing of the subsystems, as well as performing a full system validation and an environmental testing.The initiative has resulted in the development of three distinct PocketQube models: Po Cat-1 equipped with a VGA camera, and Po Cat-2 and Po Cat-3 designed for monitoring radio-frequency interference at L-band (1-2 GHz) and K-band (24-25 GHz) respectively. The latter two have undergone a series of drone campaigns in the first half of 2024, sponsored by the Frequency Allocations in Remote Sensing (FARS) Technical Committee of the IEEE Geoscience and Remote Sensing Society (GRSS)., This work 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 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)

ESA SMOS and NASA SMAP missions are measuring surface soil moisture (SM) globally from space using L-band microwave radiometers. Many studies have been conducted to validate the quality of these SM retrievals using data from different SM networks. However, neither the sampling is dense enough, nor the terrain is homogeneous enough within the pixel footprint. There is also experimental evidence that, under the same irrigation conditions, the vertical SM profiles are different because of the soil type, composition, and porosity. Therefore, the L-band brightness temperature measured by the radiometers is sensitive to the SM at different depths, which limits the accuracy of the validation. This study attempts to address this question by analyzing theoretically the contributions to the brightness temperatures of a SM layer at different depths. The study includes the effects of the frequency, polarization, incidence angle, and background SM value., Project sponsored by project “GENESIS: GNSS Environmental and Societal Missions–Subproject UPC”, Grant PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and EU EDRF., Peer Reviewed, Postprint (author's final draft)

Passive Microwave remote sensing is a very energy-efficient and time-proven technology for remote sensing. Microwave Radiometry and Global Navigation Satellite System (GNSS)-Reflectometry are two widely-used examples. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers have sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily rely on the identification and location of the interference sources. Due to the vast amount of degrees of freedom found in these RFIs, classification using classical signal processing techniques becomes problematic, especially if combined RFIs are found within the same bandwidth. In this work, continuous recordings of real RFI events from a real-time system to detect and locate RFI sources are used as training data for an automated classification system, the description and performance of which are presented and analyzed., Research funded by project “GENESIS: GNSS Environmental and Societal Missions—Subproject UPC”, Grant PID2021-126436OB-C21, sponsored by MCIN/AEI/10.13039/501100011033/ and EU ERDF “A way to do Europe”, and grant for recruitment of early stage research staff of Agencia Gestió d’Ajuts Universitaris í de Recerca (AGAUR) Generalitat de Catalunya, Spain (FISDUR2020/105)., Peer Reviewed, Postprint (author's final draft)

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

From 1980 to 2021, earthquakes have caused more than 846 000 casualties and about US$ 661 billion in economic losses. At present, there are no reliable earthquake precursors to generate alerts. Currently, the link between earthquakes and total electron content variations measured by global navigation satellite systems (GNSSs) monitoring ground stations has been studied. However, GNSS ground-based monitoring stations are irregularly disseminated around the globe with significant gaps, particularly in the ocean's regions. In this article, we analyze ionospheric intensity scintillation anomalies as potential proxies of earthquakes. NASA CYGNSS GNSS-R (reflectometry) acquired by CYGNSS/delay Doppler mapping instrument from 2017 to 2021 has been used to calculate and analyze the anomalies in the S 4 parameter over ocean areas affected by earthquakes. More than 30 000 ocean earthquakes within ±40° in latitude and with a magnitude larger than M4 have been examined. The daily planetary geomagnetic index Kp was used to discard data that may be disturbed due to space weather conditions. In addition, a daily sea wind speed mask was used to eliminate sea states that impact the signal reflectivity. The standard deviation and the interquartile time series methods have been used to detect these S 4 anomalies. The confusion matrix, the receiver operating curve, and some other figures of merit have been used for the first time to evaluate and improve the performance of the prediction parameters and identify the optimum configuration to be used as a potential proxy of earthquake occurrence. As a result, a small, but detectable positive S 4 anomaly was detected between 1 and 7 days before the earthquakes under study., The work of Badr-Eddine Boudriki Semlali was supported by an FI Grant: 2021 FI_B 00471 from FI AGAUR 2021. This work was supported in part by the Grant GENESIS, PID2021-126436OB-C21 from the Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021–2023 (Spain) and in part by the European Social Fund, Grant RYC-2016-2018 financed by MCIN/AEI/10.13039/501100011033., Peer Reviewed, Postprint (published version)

Electromagnetic waves propagation through the ionosphere is subject to several effects including refraction, absorption, signal delay, or Faraday rotation. A ray tracer propagator that simulates these effects is an important tool for all satellite missions relying on trans-ionospheric communications and Earth observation. This study presents an update to the 1975 Jones & Stephenson IONORT's code by implementing the use of up-to-date ionospheric, atmospheric, and geomagnetic models (IRI or NeQuick, NRLMSISE-00, IGRF), and a new 3-D model for equatorial plasma bubbles (EPBs). The developed code is part of the Ionosphere Modular Software Package (IMSP) in the context of the European Space Agency (ESA) project SIMIONO. HF radar sounders, Synthetic Aperture Radars (SAR), and GNSS Reflectometry (GNSS-R) missions constitute the main application of this software. The ray tracer has been validated with respect to the original software and by comparing real vs. simulated vertical ionograms in different locations and dates., This work was supported in part by the grant GENESIS PID2021-126436OB-C21 from the Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023 (Spain) and in part by the European Social Fund (ESF), Grant RYC-2016-2018 financed by MCIN/AEI /10.13039/501100011033., Peer Reviewed, Postprint (published version)

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

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)

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)

Synchronizing the local oscillators in multibeam satellites with the objective of coherent communications is still an open challenge. It has to be addressed to implement full-frequency reuse approaches, such as precoding techniques using the already deployed multibeam satellites. This article addresses the required phase synchronization to enable precoding techniques in multibeam satellite systems. It contains the detailed design of a frequency and phase compensation loop based on the proportional-integral controller, which deals with the phase drift introduced by the hardware components. Specifically, the phase noise of the local oscillators used for up and down conversion at each system element (gateway, satellite, and user terminals). The implementation of the two-state phase noise model used to emulate this phase drift is included in the article. Besides, a comparative analysis of several methods to combine the frequency and phase measurements obtained from the user terminals is also included. Finally, the performance of the proposed closed-loop synchronization method is validated through simulations using our in-house developed MIMO end-to-end satellite emulator based on SDR platforms., This research was funded by the Luxembourg National Research Fund (FNR) under the CORE project COHESAT: Cognitive Cohesive Networks of Distributed Units for Active and Passive Space Applications, grant reference [FNR11689919] and the project DISBuS: Dynamic Beam Forming and In-band Signalling for Next Generation Satellite Systems, grant reference [FNR13778945]. For the purpose of open access, the author has applied a Creative Commons Attribution 4.0 International (CCBY4.0) license to any Author Accepted Manuscript version arising from this submission. Additionally, this work has been supported by the European Space Agency under project number 4000122451/18/NL/NR ”Live Satellite Precoding Demonstration - CCN: MEO case (LiveSatPreDem).”, Peer Reviewed, Postprint (published version)

Peer Reviewed, Postprint (author's final draft)

Lithosphere-Atmosphere-Ionosphere coupling models have been widely studied and applied to earthquakes and their potential precursors. Recent evidence has been found that electromagnetic phenomena related to earthquakes might produce ionospheric anomalies even before their occurrence. This study analyzes their correlation focusing on the ionospheric scintillation associated to seismic activities in 2022 in the area around the Coral Sea. Among all the ionospheric indicators, this study analyzes the S 4 scintillation index from COSMIC-2 GNSS Radio Occultation (GNSS-RO), to estimate the ionospheric perturbation and identify anomalies. Two cases of earthquakes in 2022 with magnitudes larger than 6 are presented showing the positive correlation between the ionospheric scintillation and earthquake precursors., 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”., Peer Reviewed, Postprint (author's final draft)

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m 3 •m -3 ), demonstrating the method’s significant potential for deriving high-resolution soil moisture information., This research was supported by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011 033), through the project INTERACT “PID2020-114623RB-C32. This work was also supported by the Institute d’Estudis Espacials de Catalunya - IEEC. David Chaparro received funding from the Fundación Ramón Areces. In addition, Amir M. Irawan received the support of a fellowship from “La Caixa” Foundation (ID 100010434), with the fellowship code LCF/BQ/DI21/11860028., Peer Reviewed, Postprint (author's final draft)

Hyperspectral sensors are increasing in popularity for Earth Observation applications due to their ability to gather data over multiple spectral bands. However, the processing of such amount of information is difficult to handle for the current computing capabilities of small satellites. Several Band Selection methodologies have been developed in the last years; although, some of them demand very low computational resources, they use, at least, Level 1 data products. Therefore, the Level 0 data needs to be processed and the spectral bands coregistered. Artificial Intelligence has shown its potential to reduce the computational burden while achieving high accuracies in EO applications. In this study, a Neural Network-based methodology is proposed to select a spectral band set directly using non coregistered data captured by hyperspectral sensors., 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 (author's final draft)

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)

This study presents a new signal-processing method to mitigate radio-frequency interference (RFI), based on the Karhunen–Loève transform (KLT). The main property of this method is that it is adaptive and RFI-agnostic, that is, it can potentially mitigate any type of RFI waveform. It is based on the estimation of the covariance matrix of the process, and the derivation of a basis that spans the RFI subspace. The methodology to find this basis is explained in detail. The theoretical performance obtained using KLT is explored by simulations, along with the evaluation of the influence of the system parameters. In addition, a couple of examples with real data are provided., This work was supported in part by the Project “GNSS Environmental and Societal Missions (GENESIS)—Subproject Universitat Politécnica de Catalunya (UPC)” under Grant PID2021-126436OB-C21, in part by the Spanish Ministerio de Ciencia e Innovación under Grant MCIN/AEI/10.13039/501100011033, and in part by the European Union “European Regional Development Fund (ERDF).”, Peer Reviewed, Postprint (author's final draft)

ESA’s BIOMASS is the seventh Earth Explorer mission. It will be devoted to the global monitoring of: 1) above ground forest biomass and biomass change maps with an accuracy < 20%, a 100-200 m spatial resolution, and 6-12 months temporal resolution; 2) global forest disturbance maps with a classification accuracy > 90%, a 50-200 m spatial resolution, and 2-12 months temporal resolution; and 3) global forest height maps with an accuracy of 20-30%, 100 m spatial resolution, and 12 months temporal resolution.BIOMASS is based on a P-band SAR (438 MHz center frequency, 6 MHz bandwidth) orbiting in a dawn-dusk, Sun-synchronous orbit at 674 km, that will systematically acquire fully- (quad-) polarized image data in an interferometric mode over all major forested areas on the globe and a tomographic phase (7 images) to retrieve forest vertical structure information. At P-band ionospheric effects are very important and need to be corrected for. This paper describes the current status of the Ionospheric module for the Biomass End-to-end Performance Simulator (BEEPS-IOM)., Peer Reviewed, Postprint (author's final draft)

One of the applications where fifth generation (5G) networks are expected to have a greatest impact is vehicular-to-everything (V2X) communications. The exchange of data among different vehicles on the network will make roads a safer environment. However, the massive usage of V2X communications leads to an increase of data traffic and resources consumption. Moreover, the mobility associated to vehicles may drain the available resources in particular cells, compromising the required quality of service (QoS) of connected users. To avoid these situations, the introduction of machine learning algorithms to perform mobility load balancing between neighboring cells arises as a promising tool to ensure to all connected vehicles their demanded resources. In this paper, we address the cell overload problem by proposing an O-RAN compliant Q-learning algorithm that dynamically adapts the handover offset between two neighboring cells to mitigate a network overload situation. The algorithm performance is assessed using realistic vehicular traces. Results show that the network overload appearing during rush hour can be successfully mitigated and the load is fairly distributed between cells., This research was partially supported by the Spanish Centre for the Development of Industrial Technology and the Ministry of Economy, Industry and Competitiveness under grant/project CER-20191015 / Open, Virtualized Technology Demonstrators for Smart Networks (Open-VERSO) and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union – Next Generation EU, in the framework of the Recovery Plan, Transformation and Resilience (Call UNICO I+D 5G 2021, ref. number TSI063000-2021-29)., Peer Reviewed, Postprint (author's final draft)

Earth observation satellite missions are becoming increasingly important and have been also boosted by the appearance of small satellites, affordable to small companies and universities. Technology miniaturization and the reduction in power consumption have been a key point in the reduction of the dimensions of these spacecrafts, being able to integrate powerful payloads in a very limited space. In missions where the observations are made at lower frequency bands, the antenna dimensions limit the achievable angular resolution. For this reason, significant investments are being made to develop antenna deployment systems for small satellites, and the deployable antennas themselves.This work presents the design and prototyping of a deployable Fresnel Zone Plate antenna for CubeSats. The first model is a technical demonstrator, but it is also meant for a GNSS-Radio Occultations payload on a 6U CubeSat. Due to its characteristics and modularity it could be easily redesigned and modified to be used in communications, etc. at other frequency bands., 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”., Peer Reviewed, Postprint (author's final draft)

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)

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (m g ) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of m g with help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE =0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition., The work of D. Chaparro was supported by the XXXIII Ramón Areces Postdoctoral Fellowship, and by “la Caixa” Foundation (ID 100010434) under Grant LCF/PR/MIT19/51840001 (MIT-Spain Seed Fund). This research was supported also by the Spanish Ministry of Science and Innovation (MCIN/AEI /10.13039/501100011 033), through the coordinated project INTERACT PID2020-114623RB-C32. M. Piles thanks the support of Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital through the project AI4CS CIPROM/2021/56., Peer Reviewed, 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)

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA Payload) [1] is a development of the UPC NanoSat Lab, aimed at Earth Observation (EO). It is one of the winners of the Second IEEE GRSS Student Grand Challenge, and will fly on-board AlainSat-1, a 3U CubeSat developed by the National Space Science and Technology Center (NSSTC) of the United Arab Emirates.RITA hosts three experiments. An L-band microwave radiometer (MWR) will gather data of soil moisture and sea ice thickness and concentration, aided with a Radio-frequency Interference (RFI) detection algorithm. A LoRa transceiver will perform on-demand execution of the EO experiments [2]. Finally, a Near-Infrared (NIR) Hyperspectral Camera will gather data for vegetation monitoring, agriculture applications, hydrology and coastal and inland waters mapping, among others [3].This work is focused on the calibration and validation of the Hyperspectral imager, at optical, electronic and spectral levels, as well as in the verification of its performance to measure Normalized Difference Vegetation Index (NDVI)., 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. It is also 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)

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)