This manuscript explores the use of Global Navigation Satellite System–Reflectometry (GNSS-R) data collected by the Soil Moisture Active Passive (SMAP) mission to estimate antenna view angle offsets. A novel methodology for estimating antenna view angle offsets using GNSS-R data is proposed by comparing the received signal-to-noise ratio (SNR) of the reflected GNSS signal with SMAP’s antenna pattern measured prior to launch. To properly compare them, observation angles are defined with respect to the SMAP position and the specular point position, which are computed from the SMAP telemetry and the Global Positioning System (GPS) ephemeris data using a 1-km resolution digital elevation model (DEM) map. A methodology based on a second-order polynomial fit and a linear fit is proposed to estimate the offset by azimuthal sector and at different times of the year. Results are obtained for a total of six years, showing a consistent dependence on the antenna azimuthal look angle and on the time of the year, with an average antenna offset of 0.2° with a standard deviation of 0.06°, which produces a geolocation error of ~3.6 km. Further analysis proposes a nonlinear model to estimate the antenna offset directly from an azimuthal look angle and the time of the year. Model results show a Pearson correlation coefficient of$R$= 0.85 and$R$= 0.80 for descending and ascending passes, respectively. This model can be used by the SMAP team to further correct the antenna footprint location and pointing angle.

This article presents the mathematical background and modeling for full-polarimetric Global Navigation Satellite System Reflectometry (GNSS-R) receivers for nonnegligible cross-polar component in the scattered signal. A signal model is presented to retrieve the Stokes parameters using the Mironov model to estimate the soil surface’s dielectric constant. This article compares data collected by the SMAP-Reflectometry (SMAP-R) receiver and the proposed signal model, emphasizing the need to consider the cross-polar component ($S_{\mathrm {hv}}$). Simulations obtained without considering the cross-polar component have poor agreement in all Stokes parameters. A model is implemented using a complex cross-polar component resulting in notable improvements in the bias and unbiased root mean square difference (ubRMSD) between the modeled and measured SMAP-R Stokes parameters. Furthermore, a methodology is proposed for estimating the$S_{\mathrm {hv}}$component using SMAP-R and a soil moisture (SM) reference dataset. An analysis of$S_{\mathrm {hv}}$reveals a moderate correlation with vegetation water content (VWC) and surface roughness ($\sigma _{\mathrm {slp}}$). We developed an SM model by estimating$S_{\mathrm {hv}}$using both VWC and$\sigma _{\mathrm {slp}}$, showing the potential of full-polarimetric GNSS-R to provide SM estimates upon proper characterization of the cross-polar component. Results show the ubRMSD of 0.09 m3/m3 with respect to the in situ soil moisture network (ISMN). Finally, this study establishes that an RMSD better than 0.02 when estimating$S_{\mathrm {hv}}$is required for an SM product accuracy better than 0.07 m3/m3 for polarimetric GNSS-R SM retrievals.

The shift between frozen and thawed conditions on the Earth’s surface influences climate, hydrology, and ecology. A primary objective of the Soil Moisture Active Passive (SMAP) mission is to estimate the surface binary freeze/thaw (F/T) state for the northern hemisphere above 45° N latitude with a classification accuracy of 80% at 3-km spatial resolution and 2-day intervals. The objective was to be accomplished by the SMAP L-band radar measurements. In July 2015 the radar transmitter suffered an anomaly that prevented it from nominal operation. The mission partially met the goal exceeding the targeted 80% accuracy but at 36 km resolution. After the radar anomaly, the SMAP mission switched the radar receiver band pass filter frequency to GPS L2c, acting as a full-polarimetric Global Navigation Satellite System – Reflectometry (GNSS-R) receiver, known as SMAP-Reflectometer (SMAP-R). This work focusses on using SMAP-R signals to classify the F/T state over the Northern latitudes, within the limitations of the dataset. An algorithm based on the seasonal threshold approach that was originally envisioned for the SMAP radar is applied to the SMAP-R data (i.e., bistatic radar). Then the algorithm is evolved using a Random Forest algorithm to aid threshold selection from the discriminator built in the seasonal threshold approach. This algorithm is applied for years 2016 to 2022 over the Northern Hemisphere terrestrial cryosphere and shows an F/T classification accuracy agreement better than 97% with respect to the classification of the official SMAP F/T Radiometry product, proving the potential of polarimetric GNSSR to derive F/T.

Polarimetric Global Navigation Satellite System-Reflectometry (GNSS-R) is the next natural step for land monitoring using GNSS signals. The Soil Moisture Active Passive (SMAP) radar receiver has been retrieving polarimetric GNSS-R data using two orthogonal linearly polarized antennas since 2015, enabling the study of the polarimetric signature of GNSS-R signals on different Earth’s surfaces. Currently, new instruments and missions are using circularly polarized antennas to retrieve polarimetric GNSS-R information. In this manuscript, synthetic right and left-hand circularly polarized signals are reconstructed using the Stokes parameters of the SMAP L2C GNSS-R data. The signal-to-noise ratio (SNR) of the SMAP-R data at the equivalent RHCP, LHCP, H, and V polarization antennas is retrieved, and normalized to an arbitrary receiver with a noise figure of 2 dB. Appling the Albersheim model, we analyze the probability of detecting a reflection in a 0.5° Lat/Lon box. Results are presented for different configurations of coherent and incoherent integration times and antenna gains, for each possible antenna polarization. We present different receiver configurations capable of detecting more than 70% and 90% GNSS reflections over land. Results show that with a 10-dB antenna and a receiver with a coherent integration time of 4 ms, and an incoherent integration time of 1000 ms would suffice to detect 19.4%, 92.3%, 83.5%, and 79.4% for RHCP, LHCP, H-polarized, and V-polarized antenna, respectively. Detectability improves up to 57.4%, 99.3%, 96.3%, and 96.6% using a 14-dB antenna. Results are then generalized to L1 C/A GNSS-R signals.

Polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) is being proposed by different teams as a solution to directly estimate soil moisture. Up to date, two approaches using polarimetric GNSS-R have been proposed to estimate soil moisture, using the ratio between the right-hand and left-hand circularly polarized received signals, and using the ratio between the linear horizontal and vertical components at a set of incidence angles, known as Hybrid Compact Polarimetric (HCP) GNSS-R. In this manuscript, the necessary calibrations of a received HCP GNSS-R signal are presented. The Stokes parameters of the signal are required to compensate all non-idealities. A methodology to calibrate the receiver effects, scene-antenna polarimetric misalignment, Faraday rotation, and transmitter non-idealities is proposed. Finally, the calibration performance is evaluated using several statistical parameters and polarimetric ratio models based on two different soil moisture products. Results show a correlation coefficient increase of 4.7% with respect to the model derived from the Soil Moisture Active Passive (SMAP) soil moisture product, and a 6.5% improvement with respect to the model derived from the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) monthly average soil moisture product. Furthermore, the proposed calibrations show an unbiased root-mean-square error reduction of 6.3% and 6.8% for the SMAP soil moisture model and the ERA-5 model, respectively. Due to the SMAP antenna and pointing design, SMAP-Reflectometry (SMAP-R) is implicitly insensitive to some of the corrections. More significant corrections should be expected for future polarimetric GNSS-R instruments as European Space Agency (ESA) HydroGNSS.

This manuscript provides the first Hybrid Compact Polarimetric (HCP) Global Navigation Satellite System – Reflectometry (GNSS-R) analysis of the cryosphere. By reconstructing the full-Stokes parameters and the child Stokes parameters from the reflectometry signals collected by the SMAP radar receiver, the sensitivity to the geophysical changes of the Earth’s landscapes of these opportunistic signals are analyzed. This analysis is focused over the Artic Sea ice formation zone, the northern latitudes presenting freeze and thawed transitions, and the Greenland ice sheet. We conduct a sensitivity analysis for each landscape. The signals analyzed over the Arctic Sea show correlation with sea ice concentration (SIC) of 0.77 for the second Stokes parameterS1and 0.7 for the child Stokes parameter χ, which describes the ellipticity of the wave. The signals analyzed over the northern latitudes present a sensitivity to the two states (frozen and thawed). The analysis over Alaskan landscape shows a total reflectivity Γ0difference between freeze and thaw states of ~8 dB with a standard deviation of ~1 dB. Greenland presents a challenge since available datasets are not from the same timeframe, despite of this our initial evaluation shows that there is a ~0.5 correlation between ice thickness and the third Stokes parameterS3and the relative phase δ between the two linear electromagnetic components, h-pol and v-pol. This manuscript provides an extensive view of the HCP GNSS-R signals over the cryosphere and finds initial sensitivities to the seasonal geophysical changes of the different areas.

The Smart Ice Cloud Sensing (SMICES) is an active/passive sensor. SMICES is sponsored by NASA Earth Science Technology Office (ESTO) under Instrument Incubator Program 19 (IIP-19) awarded to Northrop Grumman Corporation (NGC) and Jet Propulsion Laboratory (JPL). The instrument is designed to measure upper tropospheric and lower stratospheric cloud ice and water vapor. SMICES uses a suite of passive radiometers that are constantly conically scanning to locate ice clouds. The ice clouds are located using an artificial intelligence controller that identifies key labels related to the ice cloud. Once an ice cloud is identified, the artificial intelligence controller activates and targets the on-board radar. While the SMICES instrument is currently being developed for an airborne demonstration, the final goal is to deploy it as a small satellite (SmallSat) instrument in low-Earth orbit (LEO). The onboard AI controller will significantly reduce DC power consumption of the satellite mission. This will enable the SMICES system to be hosted on a smaller platform with fewer solar cells and significantly drive down mission costs while maintaining the quality of scientific data. This work presents the latest development on the SMICES microwave radiometer.

The Soil Moisture Active Passive - Reflectometry (SMAPR) is the first polarimetric L2C GNSS-R dataset collected from space. Given that the dataset consists of I/Q raw data, different processing techniques can be applied. In this paper we explore the calculation of the full polarization Stokes derived from the horizontal (H) and vertical (V) polarization measurements of the transmitted right-hand circularly polarized (RHCP) Global Positioning System (GPS) signal. This configuration is known as hybrid compact polarimetry (HCP) radar, in this case HCP of a bistatic radar or a GNSSR instrument. This work describes how to use the collected data to obtain the full Stokes parameters and presents a glimpse to its value in the GNSS-R field, in particular the value of the phase of the cross-correlation signal between the H and V polarization measurements.

Global Navigation Satellite System - Reflectometry has shown promising results to retrieve land-related parameters, such as soil moisture. Polarimetric radar measurements offer additional information about the scattering scenario. SMAP-R is the first polarimetric GNSS-R dataset collected from the space thanks to the reception of the Horizontal and Vertical components of an incident circular polarized signal, allowing the retrieval of the full-Stokes parameter of the reflection. The reflectivity derived from the first Stokes bistatic radar cross section is compared to the SMAP brightness temperature, showing their dependence with vegetated areas by means of a linear equation. Finally, a polarimetry analysis is conducted proposing different polarimetric ratios derived from SMAP-R Stokes parameters, and the MODIS-derived vegetation water content.

SMAP-R is the first polarimetric GNSS-R dataset collected from the space. The reception of the Horizontal and Vertical components of an incident circular polarized signal allows to retrieve the Full-Stokes parameter of this hybrid compact polarimetry radar configuration. In this manuscript, the Stokes parameters of the reflection scenario are defined and calculated. The first Stokes is calibrated to retrieve the normalized bistatic radar cross section as main observable. The SMAP-R dataset is validated by collocating it to CYGNSS measurements. The results show a good agreement with CYGNSS normalized bistatic radar cross section, with an unbiased root-mean-square error of 3.3 dB for a 6 months period, showing also larger sensitivity over vegetated areas.

The Smart Ice Cloud Sensing (SMICES) instrument aims at providing onboard smart autonomous observation of upper tropospheric water vapor and ice particle size distribution in clouds at various local times. SMICES is an active/passive combined sensor with sounding channels at 380 GHz, radiometric channels at 250, 310 and 670 GHz, and a radar instrument operating at 239 GHz. A low-noise, low-power radiometer command and data handling (C&DH) subsystem has been designed to acquire the 24 analog radiometer channels and 8 analog thermistor data. A radiometric power regulation system provides the required power supplies for the other radiometric subsystems of the SMICES instrument. An on-board FPGA provides command and control of other instrument subsystems, performs synchronous data acquisition. The radiometer electronics are designed to fit into less than 2U horizontal dimensions of a CubeSat instrument. An AI controller unit directly interfacing with radar and radiometer C&DH subsystems performs on-board artificial intelligence operations for full system autonomy. The AI unit will control the radar instrument depending on the system health conditions, including the battery level, and based on the observed scene through the radiometer instrument.

The Soil Moisture Active Passive (SMAP) is capable of performing Global Navigation Satellite System Reflectometry (GNSS-R) measurements. The SMAP-R global dataset extends from July 2015 to present and covers latitudes up to ∼82°, which benefit multiyear cryosphere studies. In this paper we perform an initial evaluation of the freeze/thaw (F/T) state over Alaska and the sea ice presence over the Beaufort and Chukchi seas area for the periods January to March 2018 and July to September 2018. This paper investigates the sensitivity of the current fully-calibrated SMAP-R dataset to the changes in the cryosphere.

Shortly after its launch, the Soil Moisture Active Passive (SMAP) mission started using its radar receiver to collect Global Positioning System (GPS) bistatic radar measurements by switching the band-pass center frequency of its radar receiver to the GPS L2C band. We refer to this functionality of the SMAP mission as SMAP-Reflectometry or SMAP-R. The collected SMAP-R dataset represents the first polarimetric Global Navigation Satellite System Reflectometry (GNSS-R) dataset from space and brings unique capabilities as compared to other co-existing GNSS-R missions, i.e., CYclone Global Navigation Satellite System (CYGNSS). This paper formally introduces the SMAP-R dataset in its more current status and provides a wide overview of its science capabilities.

Vegetation canopy height (CH) is one of the important remote-sensing parameters related to forests’ structure, and it can be related to the biomass and the carbon stock. Global navigation satellite system-reflectometry (GNSS-R) has proved capable to retrieve vegetation information at a moderate resolution from space (20–65 km) using L1 C/A signals. In this study, data retrieved by the airborne microwave interferometric reflectometer (MIR) GNSS-R instrument at L1 and L5 are compared to the Global Forest CH product, with a spatial resolution of 30 m. This work analyzes the waveforms (WFs) measured at both bands, and the correlation of the waveform width and the reflectivity values to the CH product. A neural network algorithm is used for the retrieval, showing that the combination of the reflectivity and the waveform width allows to estimate the CH information at a very high resolution, with a root-mean-square error (RMSE) of 4.25 and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual CH.

Global Navigation Satellite System – Reflectometry (GNSS-R) is a promising field with a diverse set of applications. Polarimetric GNSS-R has been shown to be sensitive to different land parameters, such as freeze/thaw, crop growth, or soil moisture. In this manuscript, the polarimetric scenario of a GNSS reflection is defined, and the theoretical basis and the algorithm to retrieve the Stokes parameters of a GNSS-R signal is proposed. The presented algorithms are validated using data collected by the two linear orthogonal polarization channels of the SMAP radar antenna working as GNSS-R, known as SMAP-Reflectometry (SMAP-R). The Stokes parameter retrieval is presented for the SMAP-R case, and the receiver phase offset and intensity are calibrated. The same calibration procedure proposed by the CYclone GNSS (CYGNSS) mission is applied to the SMAP-R first Stokes parameter, allowing the retrieval of the normalized bistatic radar cross section of such measurement. Finally, the SMAP-R data is compared to the CYGNSS Normalized Bistatic Radar Cross Section (NBRCS) over the ocean, showing an unbiased root-mean-square error of 3.3 dB, and a small bias.

FSSCat, the “Federated Satellite Systems/3Cat-5” mission was the winner of the 2017 ESA S^3 (Sentinel Small Satellite) Challenge and overall winner of the Copernicus Masters competition. FSSCat consists of two 6 unit cubesats carrying on board UPC's Flexible Microwave Payload - 2 (FMPL-2), an L-band microwave radiometer and GNSS-Reflectometer implemented in a software defined radio, and Cosine's HyperScout-2 visible and near infrared + thermal infrared hyperspectral imager, enhanced with PhiSat-1, a on board Artificial intelligence experiment for cloud detection. Both spacecrafts include optical and UHF inter-satellite links technology demonstrators, provided by Golbriak Space and UPC, respectively. This paper describes the mission, and the main scientific results of the FMPL-2 obtained during the first three months of the mission, notably the sea ice concentration and thickness, and the downscaled soil moisture products over the Northern hemisphere.

Knowledge about sea ice concentration and extent in polar regions is of great interest both for economic interests, and as a proxy of the climate change. Retrieved maps are based on data from microwave radiometers, which are currently provided by large satellite missions. Nowadays, CubeSats have proven to be a cost-effective alternative. Due to their low cost, they can be launched in large constellations to obtain high spatial coverage and daily revisit. This study presents a neural network approach to generate sea ice concentration and sea ice extension maps using the L-band microwave radiometer, and the GNSS-Reflectometer data from the FMPL-2 instrument onboard3Cat-5/A, one of the two CubeSats of the FSSCat mission. The results obtained during the first 2 months of the mission are presented.

The Flexible Microwave Payload -2 is the microwave remote sensing payload of the FSSCat mission. The instrument is the outcome of years of work of the Universitat Politécnica de Catalunya Passive Remote Sensing Laboratory, and the NanoSat-Lab. The FSSCat mission was launched on board the Vega VV16, on September the 3rd, 2020. The instrument was executed for the very first time on September the 16th, 2020, and it was commissioned less than two weeks later. This extended abstract presents the geo-located FMPL-2 measurements, which consist of a Global Navigation Satellite System - Reflectometer and an L-band radiometer. The performance of the instrument and preliminary level 1C results are presented, showing that CubeSats can to perform passive microwave remote sensing scientific missions.

This work presents the first scientific results over land from the Flexible Microwave Payload −2 (FMPL-2), onboard the FSSCat mission. FMPL-2 is composed of an L-band microwave radiometer and a Global Navigation Satellite System - Reflectometer (GNSS-R). Two separate ANNs models are trained using the first three months of collected data of both observations, with the objective to retrieve global soil moisture maps. The first network addresses the coarsely-resolved FMPL-2 antenna footprint in a downscaling approach. Predicted values resulted in good agreement with those obtain from the SMAP mission, with an error smaller than 9.6%, and a bias smaller than 0.001 m3/m3. The second network is implemented to estimate soil moisture exclusively on GNSS-R data. In this second case, the combination of multiple GNSS-R measurements in a single track allows to retrieve soil moisture data with an error standard deviation with respect to SMAP lower than 0.056 m3/m3, with a bias smaller than 0.0007 m3/m3.

The Microwave Interferometric Reflectometer (MIR) is an airborne GNSS-R instrument developed by Universitat Politècnica de Catalunya. In 2018, it was flown twice over the agricultural Yanco area, New South Wales, Australia, once after a very dry period, and a further time the day after a strong rain event. This rain event resulted in many crop fields being entirely flooded, producing a saturation in the GNSS-R reflectivity value. In this work, the received data set is processed to identify the optimum integration time with the goal to minimize pixel blurring. This issue is assessed for airborne conditions, and then extra-polated to the spaceborne case. The presented results show that the blurring of the GNSS waveform is produced even from an airborne sensor with short integration times. Following the determination of an optimal integration time for the platform in use, the surface roughness term in the reflectivity equation can be isolated due to the signal saturation during very wet surface conditions. The final results from the two channels (L1 C/A and L5) are subsequently presented. In this case, it is shown that most reflectivity variations in GNSS-R measurements are linked to surface roughness and Speckle noise fluctuations rather than soil moisture changes.

The Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) payload is one of the Remote Sensing payloads selected by the 2nd GRSS Student Grand Challenge in 2019 to fly on board of a 3U satellite that is being developed at the National space Science and Technology Center (NSSTC), United Arab Emirates University. RITA has been designed as an academic mission with a strong focus on Earth Observation techniques and technologies. This payload is equipped with a Software-Defined Radio for microwave radiometry and RFI detection, a hyperspectral camera (25 bands from 600 to 975 nm), and a LoRa transceiver, which will work in tandem to produce vegetation-related measurements with improved accuracy. Soil moisture measurements, for example, will be derived from a Total Power Microwave Radiometer working at L-band and Normalized Difference Vegetation Index (NDVI) measurements in a technique known as Pixel Downscaling. In this work, recent advances in the payload design will be presented, as well as a more in depth mission analysis.

FSSCAT mission was the 2017 ESA Sentinel Small Satellite (S^3) Challenge winner, and the Copernicus masters competition overall winner, and it will be launched in late March 2020. FSSCAT data will provide soil moisture data, sea ice cover and coarse resolution sea ice thickness. FSSCat will be launched for Kourou Space port on June 18th, 2020, 10:51 PM local time. MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) campaign is a one-year long field experiment to study the climate system and the impact of the climate change in the Arctic sea. GNSS-R data from the second leg has been received recently. At the conference the first results of the FMPL-2 payload (combined GNSS-R and microwave radiometer instrument) on board FSSCat will be presented. Preliminary results on the PYCARO-2 GNSS-R instrument in MOSAIC, and model results fed with in-situ auxiliary data are presented in this paper.

As compared to the using L1C/A signals, L5/E5a Global Navigation Satellite System - Reflectometry (GNSS-R), gives improved resolution over the Earth's surface due to the sharper auto-correlation function. Furthermore, the larger transmitted power (+3dB with respect to L1 C/A), and correlation gain (+40dB) allows the reception of weaker reflected signals. If high directivity antennas are used, very short incoherent integration times are needed to have enough signal-to-noise (SNR) ratios, allowing the reception of multiple specular reflection points such as crest of consecutive waves without the blurring induced by long incoherent integration times. This study presents for the first time experimental evidence of the wind and swell waves signatures in the GNSS-R waveforms, and compares them with models.

Global Navigation Satellite Systems Reflected (GNSS-R) signals exhibit an incoherent and a coherent components [1], [2]. Current models assume that one or the other are dominant, and the calibration, and geophysical parameter retrieval (eg. wind speed, soil moisture ...) are developed accordingly. Even the presence itself of the coherent component of a GNSS reflected signal has been a matter of discussion in the last years. In this work, the method used in [3] to separate the leakage of the direct signal from the reflected one is applied to a set of GNSS signals reflected collected over the ocean by the MIR [4], [5], an airborne dual-band (L1/E1 and L5/E5a), multi-constellation (GPS and Galileo) GNSS-R instrument with two 19-elements array with 4 beam-steered each. The results presented demonstrate the feasibility of the proposed technique to untangle the coherent and incoherent components in GNSS reflected signals. This technique allows the processing of these components separately, which will increase the calibration accuracy (as today both are mixed together), and allows high resolution applications since the spatial resolution of the coherent component is determined by the size of the first Fresnel zone [6] (300-500 meters from a LEO satellite), and not by the size of the glistening zone (~25 km from a LEO satellite).

The Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) payload is one of the Remote Sensing payloads selected by the 2nd GRSS Student Grand Challenge to fly on board of a 3U satellite from the United Arab Emirates' National Space Science and Technology Center (NSSTC). The main objectives of RITA are to perform microwave radiometry measurements at L-band, vegetation analysis using a hyper-spectral camera, Radio-Frequency Interference (RFI) detection and classification, and a technology demonstration of sensor networks using a custom LoRa transceiver. Radiometry measurements, RFI detection, and the LoRa experiment will be performed using a Software-Defined Radio (SDR) with a frontend designed as a modified version of the Flexible Microwave Payload 1 (FMPL-1) used in the3Cat-4 mission, whereas an hyper-spectral camera will be used primarily for vegetation measurements. This work discusses the description and design of the payload, and emphasizes on the techniques used for the generation of the scientific outcome.

Weather forecast relies to a large extent on data acquired by satellite. LEO polar satellites provide global coverage, but poor spatial resolution. GEO satellites provide a better coverage and revisit time, at expenses of poorer spatial resolution. LEO constellations of small satellites offer the promise of global coverage with good spatial resolution and revisit time. However, hosted payloads on aircrafts offer the potential of very high resolution and very low revisit time for regional applications. In this work, the idea of equipping commercial airliners with low-cost Global Navigation Satellite System - Reflec-to meter (GNSS-R) receivers is explored, with emphasis on real-world data and the implications the expected scientific yield would offer.

During these years, novel Distributed Satellite Systems (DSS) have addressed the new Earth Observation (EO) requirements (e.g. near-real time access to data, or multi-point observations). One of the DSS proposals is the concept of Federated Satellite System (FSS) which has explored the benefits of sharing available and unused resources between satellite to maximize the system utility. The possibility to use additional downlink opportunities thanks to federations is a resource that could improve current EO missions, and thus achieving the required performance. For that reason, the FSS Experiment payload has been implemented as a FSS proof-of-concept between 6-Unit CubeSats in the FSSCAT mission. This article presents the design, and the test results of this payload demonstrating its feasibility for future EO missions.

Global Navigation Satellite System Reflectometry and L-band microwave radiometry have been used for soil moisture, biomass, and cryosphere studies. Combining both technologies in a low-power and cost-effective solution could largely improve current Earth observations.3Cat-4 mission is a 1-Unit CubeSat technology demonstrator of the Flexible Microwave Payload - 1, a reduced size payload that combines these two technologies. This work presents the objectives of the3Cat-4 mission and the details of the spacecraft architecture and performance. Each spacecraft subsystem is detailed at hardware and software levels. In addition, the presented results indicate that the current spacecraft design will survive the flight conditions (i.e. thermal and structural ones).

The FSSCat mission is a two nano-satellite Earth Observation mission based on 6-unit CubeSats. This paper is focused on the description of one of the remote sensing payloads: the Flexible Microwave Payload 2 (FMPL-2), on board the3Cat-5/A. The payload design is based on a Software Defined Radio, aiming to demonstrate the capabilities of nano-satellites to provide valuable scientific data. Two different instruments are integrated into a single payload: a multi-constellation (GPS and Galileo) Global Navigation Satellite System Reflectometer (GNSS-R), and a Total Power Radiometer (TPR) with Radio Frequency Interference (RFI) detection and mitigation capabilities. This paper presents the payload design, and the tests conducted during the qualification campaign.

The Flexible Microwave Payload 2 (FMPL-2) is a Remote Sensing payload carried on board3Cat-5/A, one of the two 6-unit CubeSats of the FSSCat mission. FMPL-2 is a payload based on a Software Defined Radio (SDR), integrating two instruments in a single platform: a multi-constellation (GPS and Galileo) Global Navigation Satellite System Reflectometer (GNSS-R), and a Total Power Radiometer (TPR) with Radio Frequency Interference (RFI) detection and mitigation capabilities. This paper focuses on the description of the software developed for the platform, with a detailed description on the optimizations used for the two instruments.

FSSCAT is an innovative mission concept consisting of two federated 6U Cubesats in support of the Copernicus Land and Marine Environment services. The first CubeSat carries the Flexible Microwave Payload-2 (FMPL-2), a dual microwave payload (a GNSS-Reflectometer and an L-band radiometer with interference detection/mitigation), and the second one a hyper-spectral optical payload. The combined use of these payloads will allow to measure soil moisture, ice extent, and ice thickness, and to detect melting ponds over ice. FSSCAT also includes radio and optical inter-satellite links to test some of the techniques and technologies for the upcoming satellite federations. FSSCAT will be the precursor of a constellation of federated small satellites for Earth observation achieving high temporal resolution and moderate spatial resolution in a cost-effective manner.

The3Cat-4 mission aims to demonstrate the capabilities of nano-satellites plus the versatility of a Software Defined Radio for passive Earth Observation. Three different microwave payloads are integrated into a single unit CubeSat platform: a multi-constellation (GPS and Galileo) and a dual-band (L1 and L2) Global Navigation Satellite System - Reflectometer receiver, a total power radiometer including a novel Radio Frequency Interference (RFI) detection and mitigation technique, and an Automatic Identification System receiver for vessels tracking. Being able to validate these technologies in a CubeSat enables their fast adoption as hosted payloads or in more performing dedicated platforms in the future. This paper shows a novel approach for embedding multiple passive microwave payloads in a single platform.