Daniel Pascual

Since its launch in 2016, a number of wind speed retrieval algorithms have been developed for the NASA CYGNSS satellite observations. We assess their accuracy and precision and characterize the dependence of their performance on environmental factors. The dependence of retrieval uncertainty on the wind speed itself is considered. The triple colocation method of validation is used to correct for the quality of the reference wind speed products with which CYGNSS is compared. The dependence of retrieval performance on sea state is also considered, with particular attention being paid to the long wave portion of the surface roughness spectrum that is less closely coupled to the instantaneous local wind speed than the capillary wave portion of the spectrum. The dependence is found to be significant, and the efficacy of the approaches taken to account for it is examined. The dependence of retrieval accuracy on wind speed persistence (the change in wind speed prior to a measurement) is also characterized and is found to be significant when winds have increased markedly in the ~2 h preceding an observation.

Currently, eight million metric tons of plastic end up in the oceans every year, and microplastics in different forms are present in almost all water systems in the world: streams, rivers, lakes, or oceans, and even in our blood. Detection of marine litter is an urgent task. Some works have recently reported the potential of GNSS-Reflectometry to detect marine plastic litter from space. This study presents the results of a controlled field experiment conducted under the auspices of ESA at the “Atlantic Basin” at the Deltares research institute (Delft, The Netherlands). Several types of wave conditions were created: sinusoidal and with JONSWAP spectrum, with different significant wave heights, and with different types of plastics and marine litter collected from the Dutch coast. Experimental results show the difficulty in detecting marine plastic litter based on a change of the reflected power. However, a statistical analysis of the GNSS-R estimated reflectivities (amplitude and phase) computed with very short integration times (coherent integration time T_coh = 1 ms, and no incoherent averaging: N_incoh = 1) show that it may be possible to detect large accumulations of some types of marine litter that dampen the water waves, such as nets, bottles in a net, food wraps, and bags.

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.

This article presents the methodology for an improved estimation of the sea surface wind speed measured by the cyclone global navigation satellite system (CYGNSS) constellation of satellites using significant wave height (SWH) information as external reference data. The methodology consists of a correcting 2D look-up table (LUT) with inputs: (1) the CYGNSS wind speed given by the geophysical model function (GMF); and (2) the collocated reference SWH given by the WW3 model, which is forced by winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) organization. In particular, the analyzed CYGNSS wind speeds are the fully developed seas (FDS) obtained with the GMF 3.0, and the forcing winds are the ECMWF forecast winds. Results show an increase in sensitivity to large winds speeds and an overall reduction in the root mean square difference (RMSD) with respect to the ECMWF winds from 2.05 m/s to 1.74 m/s. The possible influence of the ECWMF winds on the corrected winds (due to their use in the WW3 model) is analyzed by considering the correlation between: (1) the difference between the ECMWF winds and those from another reference; and (2) the difference between the corrected CYGNSS winds and those from the same reference. Results using ASCAT, WindSat, Jason, and AltiKa as references show no significant influence.

Altimetric performance of Global Navigation Satellite System - Reflectometry (GNSS-R) instruments depends on receiver’s bandwidth and signal-to-noise ratio (SNR). The altimetric delay is usually computed from the time difference between the peak of the direct signal waveform and the maximum of the derivative of the reflected signal waveform. Dual-frequency data gathered by the airborne Microwave Interferometric Reflectometer (MIR) in the Bass Strait, between Australia and Tasmania, suggest that this approach is only valid for flat surfaces and large bandwidth receivers. This work analyses different methods to compute the altimetric observables using GNSS-R. A proposed novel method, the Peak-to-Minimum of the 3rd Derivative (P-Min3D) for narrow-band codes (e.g., L1 C/A), and the Peak-to-Half Power (P-HP) for large bandwidth codes (e.g., L5 or E5a codes) show improved performance when using real data. Both methods are also compared to the Peak-to-Peak (P-P) and Peak-to-Maximum of the 1st Derivative (P-Max1D) methods. The key difference between these methods is the determination of the delay position in the reflected signal waveform in order to compute the altimetric observable. Airborne experimental results comparing the different methods, bands and GNSS-R processing techniques show that centimeter level accuracy can be achieved.

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.

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.

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.

The Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) created the GRSS “Standards for Earth Observation Technical Committee” to advance the usability of remote sensing products by experts from academia, industry, and government through the creation and promotion of standards and best practices. In February 2019, a Project Authorization Request was approved by the IEEE Standards Association (IEEE-SA) with the title “Standard for Spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) Data and Metadata Content.” At present, 4 GNSS constellations cover the Earth with their navigation signals: The United States of America (USA) Global Positioning System GPS with 31 Medium Earth Orbit (MEO) operational satellites, the Russian GLObal'naya NAvigatsionnaya Sputnikovaya Sistema GLONASS with 24 MEO operational satellites, the European Galileo with 24 MEO operational satellites, and the Chinese BeiDou-3 with 3 Inclined GeoSynchronous Orbit (IGSO), 24 MEO, and 2 Geosynchronous Equatorial Orbit (GEO) operational satellites. Additionally, several regional navigation constellations increase the number of available signals for remote sensing purposes: the Japanese Quasi-Zenith Satellite System QZSS with 1 GSO and 3 Tundra-type orbit operational satellites, and the Indian Regional Navigation Satellite System IRNSS with 3 GEO and 4 IGSO operational satellites. On the other hand, there are different GNSS-R processing techniques, instruments and spaceborne missions, and a wide variety of retrieval algorithms have been used. The heterogeneous nature of these signals of opportunity as well as the numerous working methodologies justify the need of a standard to further advance in the development of GNSS-R towards an operational Earth Observation technique. In particular, the scope of this working group is to develop a standard for data and metadata content arising from past, present, and future spaceborne missions such as the United Kingdom (UK) TechDemoSat-1 TDS-1, and the National Aeronautics and Space Administration (NASA) CYclone Global Navigation Satellite System CYGNSS constellation coordinated by the University of Michigan (UM). In this article we describe the scene study, including fundamental aspects, scientific applications, and historical milestones. The spaceborne standard is under development and it will be published in IEEE-SA.

The ALtimetry for BIOMass (ALBIOM) project is an ESA-funded Permanent Open Call Project that aims to retrieve forest biomass using Copernicus Sentinel-3 (S3) altimeter data. The overall goal of ALBIOM is to estimate biomass with sufficient accuracy to be able to increase existing satellite data for biomass retrieval, as well as to improve the global mapping and monitoring of this fundamental variable.  The project core tasks consist of 1) an analysis of the sensitivity of altimetry backscatter data on land parameters; 2) the development and validation of a Sentinel-3 altimeter backscatter simulator, including the effect of both topography and vegetation  and 3) the development and validation of a machine-learning biomass estimation algorithm.Here we present a summary of the results obtained from the project. The sensitivity analysis reveals that both the altimetric waveforms and the corresponding Normalised Radar Cross Sections (NRCSs) can be sensitive to the presence of biomass in the order of 100-400 tons/ha, but they are also influenced by topography and water bodies. Different sensitivities with respect to the different frequencies and resolution modes are observed, highlighting non-linear behaviours of the NRCSs. The use of differential NRCSs, defined as the difference among those calculated over two different bandwidths, was demonstrated to be not necessarily more sensitive to vegetation, as it was instead highlighted by previous studies like [Papa et al., 2003].The tracking window often appears partly or completely misplaced, when the tracking mode is in open-loop mode prescribing a predetermined range, and its size is often not long enough when collecting data over land, especially over regions with complex topography. The length and correct positioning of the tracking window over land represent therefore critical aspects for a study like ALBIOM.The modelling work has been focused on the development of a merged model approach to simulate altimeter waveforms over vegetated areas. The merging is obtained via the simultaneous use of the modifiedTor Vergata Scattering Model (TOVSM) [Ferrazzoli and Guerriero, 1995, 1996] to simulate the waveform of a flat surface covered by forest vegetation, and the use of the Soil And Vegetation Reflection Simulator (SAVERS) [Pierdicca et al., 2014], originally conceived for GNSS-Reflectometry, and here adapted to the Altimetry system. The simulator developed within ALBIOM shows promising ability to reproduce the general characteristics of the S3 waveforms. The simulations related to forested surfaces present at least two peaks, due to the top of canopy and to the ground, but the presence of topography may introduce other peaks in the waveforms, making the identification of vegetation and topographic effects challenging.Initial results on the algorithm development using Artificial Neural Networks (ANN) highlight some promising biomass estimates over specific areas (e.g. Central Africa) but also differences in algorithm performances among different regions. The corrected “ice” backscatter coefficient showed the highest sensitivity to biomass, but its values are often invalid over land, which limits the number of meaningful retrievals. The different altimeter tracking mode of Sentinel-3 over different areas of the globe (i.e., open loop and closed loop) could also be responsible for the differences in results.