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.

GNSS Reflectometry has been developing rapidly as an L-Band remote sensing technology suitable for small satellites and continues to find applications over ocean, ice and land. The TDS-1 [1] and CYGNSS missions [2] were primarily flown to target wind speed over the ocean but subsequently enabled demonstration of the potential for cryospheric and hydrological applications. The proposed HydroGNSS ESA Scout mission concept [3] has been developed to address land-based hydrological essential climate variables, specifically soil moisture, inundation, freeze / thaw state and biomass, as required by GCOS [4]. New techniques anticipated for HydroGNSS include the use of Galileo signals, coherent reflection sensing, dual polarisation and dual frequency reflectometry exploration.

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.

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from GNSS, which have been reflected off the Earth's surface. This technology has been demonstrated to be applicable to the remote sensing of a number of geophysical surface parameters including ocean wind speed and sea-ice. Using data collected by the UK TechDemoSat-1 mission between 2014 and 2018, the National Oceanography Centre (NOC) has developed a GNSS-R signal processing scheme called the NOC Calibrated Bistatic Radar Equation (C-BRE) processor that features an ocean wind speed inversion algorithm incorporating radiometric calibration submodules and several corrections steps that mitigate effects related to the GNSS system, instrumentm and geometry. The latest version of the NOC GNSS-R processor additionally features updated data quality control mechanisms that include the flagging of radio frequency interference (RFI) and sea-ice detection based on the GNSS-R waveform.

A machine learning technique is implemented for retrieving space-borne Global Navigation Satellite System Reflectometry (GNSS-R) wind speed. Conventional approaches commonly fit a function in a predefined form to matchup data in a least-squares (LS) sense, mapping GNSS-R observations to wind speed. In this study, a feedforward neural network is trained for TechDemoSat-1 (TDS-1) wind speed inversion. The input variables, along with the derived bistatic radar cross-section σ0, are selected after investigating the wind speed dependence and the model performance. When compared to an LS-based approach, the derived model shows a significant improvement of 20% in the root mean square error (RMSE). The proposed neural network demonstrates an ability to model a variety of effects degrading the retrieval accuracy such as the different levels of the effective isotropic radiated power (EIRP) of GPS satellites. For example, the derived Mean Absolute Error (MAE) of the satellite with SVN 34 is decreased by 32% using the machine-learning-based approach.

After developing a wind speed retrieval algorithm, derived winds from measurements of UK TechDemoSat-1 (TDS-1), from May 2015 to July 2017, are compared to wind products of Advanced Scatterometer showing a reliable performance, especially during rain events. However, a rain signature in GNSS-R observations, a decrease in the value of the bistatic radar cross section at low winds, is demonstrated, which can potentially enable the technique to detect precipitation over oceans induced by low-to-moderate winds. This phenomenon is investigated and finally characterized as the rain splash effect altering the ocean surface roughness. To improve the quality of derived winds, a machine learning technique is implemented for the wind speed inversion as a geophysical model function. The trained feedforward neural network shows a significant improvement of 17% in the wind speed RMSE compared to the LS approach. In the end, one can conclude that space-borne ocean monitoring is evolving existing products with a potential for novel geophysical applications.

This paper presents a comprehensive procedure to improve the wind geophysical model function (GMF) for the Global Navigation Satellite System Reflectometry (GNSS-R) instrument onboard the TechDemoSat-1 satellite. The observable used to define the GMF is extracted from the measured delay-Doppler maps (DDMs) by correcting for the nongeophysical effects within the measurements. Besides the instrument and the geometric effects as provided in the bistatic radar equation, a calibration term that accounts for the uncalibrated receiver antenna gain and the unknown transmitter antenna gain is proposed to optimize the calculation of GNSS-R observables. Such calibration term is presented as a function of observing elevation and azimuth angles and is shown to remarkably reduce the measurement uncertainties. First, an empirical wind-only GMF is developed using the collocated Advanced Scatterometer (ASCAT) winds and European Centre for Medium-Range Weather Forecasts (ECMWF) model wind output. This empirical GMF agrees well with the model output. Then, the sensitivity of the observable to waves is analyzed using the collocated ECMWF wave parameters. The results show that it is difficult to include mean square slope (MSS) in the development of an empirical GMF, since the difference between ECMWF MSS and the MSS sensed by GNSS-R varies with incidence angle and wind speed. However, it is relevant to take significant wave height (Hs) in account, particularly for low wind conditions. Consequently, a wind/Hsapproach is proposed for improved wind retrievals.

A formulation of the relationship between sea-surface roughness and extension of the glistening zone (GZ) of a Global Navigation Satellite System Reflectometry (GNSS-R) system is presented. First, an analytical expression of the link between GZ area, viewing geometry, and surface mean square slope (MSS) is derived. Then, a strategy for retrieval of surface roughness from the delay-Doppler map (DDM) is illustrated, including details of data preprocessing, quality control, and GZ area estimation from the DDM. Next, an example for application of the proposed approach to spaceborne GNSS-R remote sensing is provided, using DDMs from the TechDemoSat-1 mission. The algorithm is first calibrated using collocated in situ roughness estimates using data sets from the National Data Buoy Center, its retrieval performance is then assessed, and some of the limitations of the suggested technique are discussed. Overall, good correlation is found between buoy-derived MSS and estimates obtained using the proposed strategy ($r=0.73$ ).

Global Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multi-static radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from a ground-based and airborne experiments, but studies using space-borne data are still preliminary. This work presents a sensitivity study of Using TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e. vegetation covers). Despite the scattering in the data, which can be attributed to the temporal and spatial (footprint size) collocation mismatch with the SMOS and MODIS NDVI data, and errors in the land use data preliminary results show a good correlation with soil moisture.

GEROS-ISS (GEROS hereafter) stands for GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. It is a scientific experiment, proposed to the European Space Agency (ESA) in 2011 for installation aboard the ISS. The main focus of GEROS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) for remote sensing of the System Earth with focus to Climate Change characterisation. The GEROS mission idea and the current status are briefly reviewed.

This paper will examine the potential performances of spread-spectrum-based access techniques via the interaction channel of an Internet protocol (IP)-based multimedia satellite system. In particular, a theoretical analysis of the spread-Aloha technique will be given for the case of forward error correction coded systems. Then, the performance of spread-Aloha access will be confirmed by simulation and compared with that of a conventional time-division multiple-access (TDMA)-based combined free/demand assignment multiple-access system. It appears that spread-Aloha-based techniques, although achieving a lower maximum throughput than the TDMA-based access scheme, may provide a better experience to the user due to the lower transmission latency. Finally, the prospect of a spread-spectrum-based access scheme combining Aloha and reservation mechanisms will be briefly overviewed.