Several companies are developing massive satellite constellations aimed to deliver global broadband internet and provide in the near future benefits and opportunities. These opportunity signals also provide additional capabilities if exploited for remotes sensing purposes. The present study shows first results of an Open Space Innovation idea supported by the European Space Agency (ESA), where the concept of microwave reflectometry using signals of opportunity from the OneWeb satellites constellation will be used for wind speed measurements over the sea surface. This big constellation of satellites operate in the Ku band, which is well suited for detecting sea surface geophysical parameters as well as soil soil moisture. This work focuses on the simulation of the normalized bistatic radar cross section scattered from the sea surface using a full polarimetric approach in first-order small slope approximation.

This paper gives a summary of the HydroGNSS mission and status in the preparations up to launch. Some of the advancements in instrument are presented, including on-board gain adjustment, geolocation. Also the ground processing Payload Data Ground Segment is introduced and some of the processing advances, including antenna pattern modelling for EIRP estimation and bounding of measurement areas.

The algorithms to detect the presence of water on land surfaces using the Global Navigation Satellite System (GNSS) reflected signals collected aboard of the future ESA Scout 2-satellite mission HydroGNSS are presented. HydroGNSS will be ready for launch in H2/2024, into polar orbits, and it will operate at dual frequency, dual-polarization, and in two simultaneous acquisition modes (low-rate power and high-rate complex signal modes). The overall strategy to generate level-2, point-by-point along track water classification using all these signals is introduced, yet currently available datasets only permit the validation of the algorithms applied to one single frequency and polarization. Two signal coherence indicators are selected per each acquisition mode, and together with geolocation, large-scale surface roughness, and land cover type are the inputs of the random forest classifier, which is an ensemble learning method, with the monthly Global Surface Water (GSW) as a reference target. The algorithms are tested with NASA/CYGNSS low-rate power delay-Doppler maps (DDMs) and with CYGNSS and U.K./TechDemoSat-1 (TDS-1) raw intermediate frequency (IF) sampled signals. The results of the validation are analyzed at different scales, with results achieving the mission’s 90% accuracy requirement. Final retraining and validation using actual dual-polarization and dual-frequency HydroGNSS data will be conducted once the satellites are in-orbit.

This study focuses on the retrieval of soil moisture (SMC) and forest Aboveground Biomass (AGB), and on the classification of fire disturbances in forests by using the NASA’s Cyclone GNSS (CyGNSS) data over land. Retrieval and classification algorithms, based on machine learning (ML) techniques, as Supported vector machines (SVM), Artificial Neural Networks (ANN) and Random Forests are implemented and validated against reference data from in-situ measurements and EO products.The research, which was carried out in the framework of two ESA project, has the twofold aim of further assessing the potential of GNSS-R for land applications and of defining retrieval concepts to be applied to the ESA’s SCOUT 2 HydroGNSS satellite mission.

HydroGNSS is the second mission supported by the European Space Agency (ESA) under the Scout program, which is a new framework (3 years from KO to launch, cost ≤ 30 M€) by which ESA aims to demonstrate disruptive sensing techniques or incremental science, while retaining the potential to be subsequently scaled up in larger missions or implemented in future ESA Earth Observation programmes. HydroGNSS consists of a scientific demonstrator that primarily addresses land bio-geophysical variables. The mission is comprised of one satellite (with an option on the second) flying at a low-Earth orbit to collect Global Navigation Satellite System reflections (i.e., Delay Doppler Maps, DDMs) near continuously over the globe. The DDMs are used to generate Level 2 products related to Essential Climate Variables (ECV s), whose estimation defines the primary scientific goal of the mission. In this contribution we outline a review of the ECVs targeted by HydroGNSS, showing some representative results achieved during preliminary studies about the mission. Special emphasis is given to the monitoring of soil freeze-thaw state and soil moisture. ECVs are of great interest and support the sustainable development agenda adopted by the United Nations members in 2015.

The GNSS reflectometry (GNSS-R) potential for the monitoring of hydrological parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely proved in recent years. In this study, algorithms based on Artificial Neural Networks (ANN) have been developed for the retrieval of both SM and AGB from GNSS-R observations. This activity has been carried out in view of the ESA's HydroGNSS mission. Waiting for HydroGNSS data, the algorithms have been implemented and validated by using the NASA's Cyclone GNSS (CyGNSS) land observations, confirming a promising potential of GNSS-R for the monitoring of both SM and AGB.

This letter investigates the sensitivity of the ocean surface bistatic scattering cross section measured by the Cyclone Global Navigation Satellite System (CYGNSS) to wind direction using the kurtosis of the delay-Doppler map (DDM) samples within a given area. The azimuthal dependence of the kurtosis is modeled by a cosine expansion of the relative wind direction, as is done in scatterometry for the radar cross section. The harmonic coefficients of the model depend on wind speed and incidence angle. Results show a coefficient of determination ($R^{2}$) between 0.6 and 0.9 for wind speeds between 4 and 10 m/s and negligible sensitivity outside this range. This study opens the door to the potential of using CYGNSS data for wind direction estimation.

ALtimetry for BIOMass (ALBIOM) is a Permanent Open Call Project funded by the European Space Agency (ESA) to explore the possibility of forest biomass retrieval by using Copernicus Sentinel-3 (S-3) Synthetic Aperture Radar Altimeter (SRAL) in low- and high-resolution mode at Ku- and C-bands. It represents an original work in the research of new techniques for vegetation observation using altimetry data. Because of the complexity of the land surfaces, no algorithm has been developed for a specific retracking of the altimetric land waveform. This calls for the development of a model able to reproduce the acquisition system and the target scattering phenomena to simulate the interaction of the radar pulse with the land. In this first work we present the electromagnetic simulator of S-3 SRAL altimeter measurements over bare soil scenarios realized through a modification of the SAVERS (Soil And Vegetation Reflection Simulator) simulator developed by the team for GNSS-R reflectometry over land. The impact of topography has been also taken into account. We demonstrate that SAVERS for S-3 SRAL proved its capability to reproduce altimeter waveforms’ main attributes for both flat and topography scenarios.

Forests play a crucial role in the climate change mitigation by acting as sinks for carbon and, consequently, reducing the CO2 concentration in the atmosphere and slowing global warming. For this reason, above ground biomass (AGB) estimation is essential for effectively monitoring forest health around the globe. Although remote sensing-based forest AGB quantification can be pursued in different ways, in this work, we discuss a new technique for vegetation observation through the use of altimetry data that have been introduced by the ESA-funded ALtimetry for BIOMass (ALBIOM) project. ALBIOM investigates the possibility of retrieving forest biomass through Copernicus Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) measurements at the Ku- and C-bands in low- and high-resolution modes. To reach this goal, a simulator able to reproduce the altimeter acquisition system and the scattering phenomena that occur in the interaction of the radar altimeter pulse with vegetated surfaces has been developed. The Tor Vergata Vegetation Scattering Model (TOVSM) developed at Tor Vergata University has been exploited to simulate the contribution from the vegetation volume via the modeling of the backscattering of forest canopy through a discrete scatterer representation. A modification of the Soil And Vegetation Reflection Simulator (SAVERS) developed by the team for Global Navigation Satellite System Reflectometry over land has also been taken into account to simulate the soil contribution.

ALtimetry for BIOMass (ALBIOM) is a research project funded by the European Space Agency to study the possibility of estimating above ground biomass by means of low- and high-resolution Sentinel-3 altimetry data. We present preliminary results of a sensitivity analysis, developed to assess in what extent waveforms and altimetry observables are affected by the presence of forests. Ku- and C-band altimetry data have been collected and processed to properly select well-tracked waveforms over land, which are then related to collocated biomass data. A statistical analysis has been performed, highlighting a sensitivity of the estimated normalized radar cross section with respect to forest biomass, even though it is strongly disturbed by the soil topography within the radar footprint. Additionally, the inaccurate positioning of the time-tracking window limits the number of useful data.

An approach that may be suitable to detect and quantify plastics in the ocean is the use of Global Navigation Satellite System-Reflectometry (GNSS-R). Measurements of the roughness of the sea surface, known as Mean Square Slopes (MSS) can be derived from GNSS-R. The measured MSS can be compared to the expected surface roughness derived from an empirical model, with the difference being computed as the ‘MSS anomaly’. It may be possible to use this MSS anomaly to identify regions where the sea surface roughness is lower than expected. A lower-than-expected sea surface roughness can be due to a variety of different physical phenomena, but may under certain circumstances be indicative of the location of floating matter, including plastic. We will present the preliminary analysis of the sensitivity of this MSS anomaly to the concentration of ocean plastic measured in-situ and predicted by ocean circulation models. This analysis of sensitivity to plastic will be carried out in the context of sensitivity to other parameters such as the wind stress and ocean swell.

In recent years, the global climate change increased significantly the occurrence and severity of forest disturbances due to fires, causing important alterations in forest ecosystems that also impact on climate and affecting the forest capability of providing resources for human needs. This paper aims at exploiting the potential of Global Navigation Satellite System Reflectometry (GNSS-R), based on L band signals, for the detection of forest disturbances due to fires. The study focused on the forested part of Angola that was largely affected by fires during the summer 2019 and exploited the data collected by the NASA Cyclone GNSS (CyGNSS) constellation. As reference data for developing and testing the method, the ESA CCI decadal burned areas maps have been considered. A simple approach based on the temporal gradient of the GNSS-R observables, namely Signal to Noise Ratio (SNR) and Equivalent Reflectivity ($\Gamma$), allowed identifying satisfactorily the burned areas with respect to the reference data, by enabling the generation of maps every ten days.

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title “Standard for Global Navigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content”. A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grss-ieee.org/community/technical-committees/standards-or-earth-observations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.

This study aims at assessing the potential of the NASA's Cyclone GNSS (CyGNSS) data for observing SM and forest biomass. As reference values for the comparison, global datasets of Vegetation Optical Depth (VOD) and SM derived from NASA's Soil Moisture Active and Passive mission SMAP have been considered. The results of the sensitivity analysis suggested exploiting the CyGNSS capabilities in estimating VOD and SM by setting-up prototype retrieval algorithms based on Artificial Neural Networks (ANN).

In this article, a statistical methodology to estimate wind speed from CYGNSS observables is proposed and implemented. The approach uses the cumulative distribution function (cdf) of the observable and of the ground-truth reference winds. It depends only on the statistical distributions of the CYGNSS data and the wind speed, and therefore, is simpler to implement than alternative approaches requiring coincident matchups between the data and the ground truth. This cdf matching method produces retrieved winds with a probability density function that is very close to that of the ground-truth winds. When compared to the current CYGNSS baseline winds for fully developed seas, the cdf matching winds show better behavior and agreement with reference wind speeds over the low to medium wind speed range, which constitutes the majority of the wind population that drives the statistics used by the algorithm. The performance is robust with respect to measurement geometry and transmitter and receiver hardware parameters, with the exception of a dependence of the error on the GPS satellite identifier (ID), probably due to uncorrected variations in GPS equivalent isotropically radiated power (EIRP). Validation using modeled winds and winds measured by other satellites reveals that CYGNSS winds behave in a very similar manner as the winds modeled by the Global Data Assimilation System (GDAS).

The estimate of forest biomass on a global scale is of great relevance for many purposes related to the carbon cycle and the climate change.In this research work, the capability of GNSS sensors for evaluating forest biomass has been investigated by using data coming from two satellite sensors, i.e. TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and the NASA’s Cyclone GNSS (CyGNSS).Two reflectivity parameters were identified and compared to global forest biomass values obtained through ALOS2 and SMAP VOD. The sensitivity analysis provided interesting results with correlation coefficients (R) > 0.65, thus allowing the implementation of a retrieval algorithm based on a Neural Network approach. The results have been encouraging, showing R>0.8 and RMSE<0.2 on the area of Manaus.

The development and implementation of a GPS-Reflectometry (GPS-R) simulator for the ocean target detection application is proposed, with the objective of assessing and characterizing the capability of GPS-R as a new method for sea surface target detection. The novel idea is to use a backscattering configuration to enhance the electromagnetic return from the target compared to the surrounding ocean. Hence the backscattering simulator simulates images of GPS echoes from one or more targets embedded in an ocean clutter, and its outputs will be analysed as a function of hardware parameters, geometry, and signal processing approaches to determine the target detectability. Some preliminary results related to the backscattering simulations from both ocean and target will be presented.

An analysis of CYGNSS data is presented, with the objective of assessing the potentials of these data for land applications. The GPS-Reflections over land acquired by the CYGNSS observatories are exploited to detect properties of the land soil moisture. A land reflectivity observable, derived from CYGNSS Delay/Doppler Maps, is computed using a calibration approach suitable for land reflections. The sensitivity of the reflectivity observable to the soil moisture parameter is investigated through a comparison with soil moisture data from the SMAP satellite. Some preliminary results on the correlation between the CYGNSS reflectivity and the SMAP soil moisture are presented. This work is being conducted within the framework of the European Space Agency Project “Potential of Spaceborne GNSS-R for Land Applications”.

The processing procedures used for the calibration and validation of Level 2 ocean surface wind speed data product for the CYGNSS mission will be presented in this work. The validation process is planned against a series of ground truth matchups which include buoy measurements, other existing satellite counterparts such as scatterometers, radiometers, altimeters and data from global forecast models.

An overview of the wind speed retrieval algorithm used to generate the first CYGNSS Level 2 wind speed products is presented. The algorithm uses two observables derived from Level 1b calibrated Delay/Doppler Maps, and constructs a geophysical model function which maps the observable value and its associated incidence angle into a wind speed value. The wind estimates from the two observables are also combined to form a best-weighted estimator, which represents our final retrieved wind speed. Here the major steps needed to implement the algorithm are reviewed, with a focus on some new aspects, such as the characterization of the dependence of the observables on incidence angle based on real data. An analysis of the algorithm performance is also presented.

This presentation will include an overview of the recently launched NASA CYGNSS mission Level 1 calibration algorithms and their on-orbit validation [1], [2]. The validation of the Level 1 calibration will be performed in several steps, including a) a detailed noise floor analysis to assess the observed on-orbit noise power levels over the open ocean, b) multiple consistency checks using a forward model and co-located ocean wind and wave truth reference data and c) a term by term error analysis of all the non-ocean corrections applied to the final sigma0 estimates. An outline of the Level 1a (calibration from raw Level 0 instrument counts to units of watts for the received power) and the Level 1b (calibration from watts to bistatic scattering cross section) algorithms are each shown below. Three key components of the Level 1a calibration will be presented, namely, an analysis of the instrument (alone) and antenna noise characteristics over the ocean, a study of the range of received power levels from the surface, and comparisons with a forward model. The key components of the Level 1b calibration presented here will include validation of the main corrections applied to arrive at a surface sigma0 estimate, including receiver antenna gain, GPS transmitter and scattering area corrections.

In this paper the Two-Scale Model has been used to derive the theoretical Normalized Radar Cross Section (NRCS) for sea clutter in the L-band, taking into account also the circular polarization of GPS signals. Using this theoretical model and the theoretical formula for NRCS, authors aim to investigate the possibility to extend target detection through GPS signals in backscattering configuration by varying the incidence angle and the wind speed, whereas results obtained previously were related only to a single value of the incidence angle and to two values of the wind speed. Target Scattered Power and Sea Clutter Power are derived and compared finding that: 1) smaller targets can be detected as the incidence angle increases; 2) the Sea Clutter Power is lower than the one estimated when the “worst case” of a VV-polarization signal is chosen to derive the NRCS value from experimental data. Some final considerations are made as future work.

A method for defining the spatial resolution of a Global Navigation Satellite System reflectometry delay-Doppler map (DDM) and of any derived geophysical product is proposed. An effective spatial resolution is derived as a function of measurement geometry and delay-Doppler (DD) interval, and as a more appropriate representation of resolution than the geometric resolution previously used in the literature. The definition more accurately accounts for variations in the scattered power across different pixels of the DDM and more accurately includes the power spreading effect caused by the Woodward ambiguity function. The dependence of the effective resolution on incidence angle, receiver altitude, and DD interval is analyzed and compared with the dependence of the geometric resolution with similar parameters.

A retrieval algorithm is presented for the Level 2 ocean surface wind speed data product of the Cyclone Global Navigation Satellite System (CYGNSS) mission. The algorithm is based on the approach described by Clarizia et al ., 2014. The approach is applied to the specific orbital measurement geometry, antenna, and receiver hardware characteristics of the CYGNSS mission. Several additional processing steps have also been added to improve the performance. A best weighted estimator is used to optimally combine two different partially correlated estimates of the winds by taking their weighted average. The optimal weighting dynamically adjusts for variations in the signal-to-noise ratio of the observations that result from changes in the measurement geometry. Variations in the incidence angle of the measurements are accounted for by the use of a 2-D geophysical model function that depends on both wind speed and incidence angle. Variations in the propagation time and signal Doppler shift at different measurement geometries affect the instantaneous spatial resolution of the measurements, and these effects are compensated by a variable temporal integration of the data. In addition to a detailed description of the algorithm itself, the root-mean-square wind speed retrieval error is characterized as a function of the measurement geometry and the wind speed using a detailed mission end-to-end simulator.

This paper develops and characterizes the algorithms used to generate the Level 1 (L1) science data products of the Cyclone Global Navigation Satellite System (CYGNSS) mission. The L1 calibration consists of two parts: the Level 1a (L1a) calibration converts the raw Level 0 delay-Doppler maps (DDMs) of processed counts into received power in units of watts. The L1a DDMs are then converted to Level 1b DDMs of bistatic radar cross section values by unwrapping the forward scattering model and generating two additional DDMs: one of unnormalized bistatic radar cross section values (in units of square meters) and a second of bin-by-bin effective scattering areas. The L1 data products are generated in such a way as to allow for flexible processing of variable areas of the DDM (which correspond to different regions on the surface). The application of the L1 data products to the generation of input observables for the CYGNSS Level 2 (L2) wind retrievals is also presented. This includes a demonstration of using only near-specular DDM bins to calculate a normalized bistatic radar cross section (unitless, i.e., m2/m2) over a subset of DDM pixels, or DDM area. Additionally, an extensive term-by-term error analysis has been performed using this example extent of the DDM to help quantify the sensitivity of the L1 calibration as a function of key internal instrument and external parameters in the near-specular region.

The Global Navigation Satellite System (GNSS) reflectometry, i.e. GNSS-R, is a novel remote-sensing technique first published in [1] that uses GNSS signals reflected from the Earth's surface to infer its surface properties such as sea surface height (SSH), ocean winds, sea-ice coverage, vegetation, wetlands and soil moisture, to name a few. This communication discusses the scientific value of GNSS-R to (a) furthering our understanding of ocean mesoscale circulation toward scales finer than those that existing nadir altimeters can resolve, and (b) mapping vegetated wetlands, an emerging application that might open up new avenues to map and monitor the planet's wetlands for methane emission assessments. Such applications are expected to be demonstrated by the availability of data from GEROS-ISS, an ESA experiment currently in phase A [2], and CyGNSS [3], a NASA mission currently in development. In particular, the paper details the expected error characteristics and the role of filtering played in the assimilation of these data to reduce the altimetric error (when averaging many measurements).

The backscatters power single-look waveform recorded by a synthetic aperture radar altimeter is approximated in a closed-form model. The model, being expressed in terms of parameterless functions, allows for efficient computation of the waveform and a clear understanding of how the various sea state and instrument parameters affect the waveform.

A Minimum Variance (MV) wind speed estimator for Global Navigation Satellite System-Reflectometry (GNSS-R) is presented. The MV estimator is a composite of wind estimates obtained from five different observables derived from GNSS-R Delay-Doppler Maps (DDMs). Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions that are derived from NDBC buoy wind matchups with collocated overpass measurements made by the GNSS-R sensor on the United Kingdom-Disaster Monitoring Constellation (UK-DMC) satellite. The MV estimator exploits the partial decorrelation that is present between residual errors in the five individual wind retrievals. In particular, the RMS error in the MV estimator, at 1.65 m/s, is lower than that of each of the individual retrievals. Although they are derived from the same DDM, the partial decorrelation between their retrieval errors demonstrates that there is some unique information contained in them. The MV estimator is applied here to UK-DMC data, but it can be easily adapted to retrieve wind speed for forthcoming GNSS-R missions, including the UK's TechDemoSat-1 (TDS-1) and NASA's Cyclone Global Navigation Satellite System (CYGNSS).

We present the implementation of a facet-based simulator to investigate the forward scattering of L-band signals from realistic sea surfaces and its application to spaceborne ocean Global Navigation Satellite System (GNSS) Reflectometry. This approach provides a new flexible tool to assess the influence of the ocean surface roughness on scattered GNSS signals. The motivation stems from the study by Clarizia, which revealed significant differences between delay–Doppler maps (DDMs) obtained from UK-DMC satellite data and DDMs simulated with the Zavorotny–Voronovich (Z-V) model. Here, the scattered power and polarization ratio (PR) are computed for explicit 3-D ocean wave fields, using a novel implementation of the Kirchhoff approximation (KA), which we call the Facet Approach (FA). We find that the FA is consistent with the full KA and the Geometrical Optics (GO) used in the Z-V model, while being less computationally expensive than the KA and able to represent polarization effects not captured by the GO. Instantaneous maps of the bistatic normalized radar cross section computed with the FA show clear patterns associated with the underlying waves. The wave field is particularly visible in the PR, indicating that the scattering is generally dominated by the HH component, particularly from ocean wave troughs. Polarization effects show, for the first time, a strong correlation to the explicit sea surface from which the scattering originated. DDMs of the scattered power computed with the FA reveal patchy patterns and power distributions that differ from those obtained with Z-V and show closer similarities with observed DDMs from UK-DMC.

Global Navigation Satellite System Reflectometry (GNSS-R) is a new approach for earth observation using signals of opportunity in a bistatic configuration. The system, in the configuration of interest, exploits the 10 bits PN sequence of the GPS system to generate Delay/Doppler maps that are useful for monitoring the sea state. One of the open problems is that the achievable delay and Doppler resolution is limited by the GPS waveform. We will show that, exploiting a MUSIC-based algorithm, it is possible to move further the limits for the achievable delay and Doppler resolution.

We present a new approach to the retrieval of sea surface roughness using GNSS-R. The steps through the simulation of the whole end-to-end microwave scattering of GNSS signals from the sea surface are explained, with emphasis on how to generate a linear sea surface and to implement the Kirchhoff Approximation (KA), as the large-scale part of the full scattering model. We illustrate some examples of radar cross sections calculated using the Kirchhoff scattering model, and how they change with respect to different polarizations. Their variations with geometry, sea state and spatial resolution are investigated and discussed.

In this paper we analyse the GPS signals reflected by the surface of the ocean to retrieve information about the sea surface roughness, expressed in statistical terms by means of the sea surface Mean Square Slopes (MSS). Particularly, we perform Delay-Doppler mapping of real scattered GPS signals from the Surrey Satellite Technolody Ltd UK-DMC mission, and we simulate Delay-Doppler Maps (DDMs) using the Zavorotny-Voronovich model of the GPS power scattered from the ocean surface, as a function of the geometrical properties of the transmitter and receiver, as well as statistical properties of the scattering surface. Subsequently, we fit simulated DDMs to the measured ones, to retrieve the optimal MSS of the scattering surface, and we compare GPS-derived MSS with theoretical and in situ MSS, calculated using the Elfouhaily et al. wave spectrum and co-located buoy spectra of the National Data Buoy Center (NDBC).