First results are presented for ocean surface wind speed retrieval from reflected GPS signals measured by the low Earth orbiting UK TechDemoSat‐1 satellite (TDS‐1). Launched in July 2014, TDS‐1 provides the first new spaceborne Global Navigation Satellite System‐Reflectometry (GNSS‐R) data since the pioneering UK‐Disaster Monitoring Mission (UK‐DMC) experiment in 2003. Examples of onboard‐processed delay‐Doppler maps reveal excellent data quality for winds up to 27.9 m/s. Collocated Advanced Scatterometer (ASCAT) winds are used to develop and evaluate a wind speed algorithm based on signal‐to‐noise ratio (SNR) and the bistatic radar equation. For SNRs greater than 3 dB, wind speed is retrieved without bias and a precision around 2.2 m/s between 3 and 18 m/s even without calibration. Exploiting lower SNR signals, however, requires good knowledge of the antenna beam, platform attitude, and instrument gain setting. This study demonstrates the capabilities of low‐cost, low‐mass, and low‐power GNSS‐R receivers ahead of their launch on the NASA Cyclone GNSS (CYGNSS) constellation in 2016.

HydroGNSS (Hydrology using Global Navigation Satellite System reflections) has been selected as the second European Space Agency (ESA) Scout earth observation mission to demonstrate the capability of small satellites to deliver science. This article summarizes the case for HydroGNSS as developed during its system consolidation study. HydroGNSS is a high-value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement the existing observations and are high in ESAs earth observation scientific priorities. The mission delivers the observations of four hydrological essential climate variables as defined by the global climate observing system using the new technique of GNSS reflectometry. These will cover the world's land mass to 25 km resolution, with a 15-day revisit. The variables are soil moisture, inundation or wetlands, freeze/thaw state, and above-ground biomass.

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.

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/Hs approach is proposed for improved wind retrievals.

We present the first examples of Global Navigation Satellite Systems‐Reflectometry (GNSS‐R) observations of hurricanes using spaceborne data from the UK TechDemoSat‐1 (TDS‐1) mission. We confirm that GNSS‐R signals can detect ocean condition changes in very high near‐surface ocean wind associated with hurricanes. TDS‐1 GNSS‐R reflections were collocated with International Best Track Archive for Climate Stewardship (IBTrACS) hurricane data, MetOp ASCAT A/B scatterometer winds, and two reanalysis products. Clear variations of GNSS‐R reflected power (σ⁰) are observed as reflections travel through hurricanes, in some cases up to and through the eye wall. The GNSS‐R reflected power is tentatively inverted to estimate wind speed using the TDS‐1 baseline wind retrieval algorithm developed for low to moderate winds. Despite this, TDS‐1 GNSS‐R winds through the hurricanes show closer agreement with IBTrACS estimates than winds provided by scatterometers and reanalyses. GNSS‐R wind profiles show realistic spatial patterns and sharp gradients that are consistent with expected structures around the eye of tropical cyclones.

This paper presents the temporal evolution of Global Navigation Satellite System Reflectometry (GNSS-R) ocean wind speed retrieval performance during three years of the UK TechDemoSat-1 (TDS-1) mission. TDS-1 was launched in July 2014 and provides globally distributed spaceborne GNSS-R data over a lifespan of over three years, including several months of 24/7 operations. TDS-1 wind speeds are computed using the NOC Calibrated Bistatic Radar Equation algorithm version 0.5 (C-BRE v0.5), and are evaluated against ERA5 high resolution re-analysis data over the period 2015–2018. Analyses reveal significant temporal variability in TDS-1 monthly wind speed retrieval performance over the three years, with the best performance (~2 m∙s⁻¹) achieved in the early part of the mission (May 2015). The temporal variability of retrieval performance is found to be driven by several non-geophysical factors, including TDS-1 platform attitude uncertainty and spatial/temporal changes in GPS transmit power from certain satellites. Evidence is presented of the impact of the GPS Block IIF Flex mode on retrieved GNSS-R wind speed after January 2017, which results in significantly underestimated ocean winds over a large region covering the North Atlantic, northern Indian Ocean, the Mediterranean, the Black Sea, and the Sea of Okhotsk. These GPS transmit power changes are shown to induce large negative wind speed biases of up to 3 m∙s⁻¹. Analyses are also presented of the sensitivity of TDS-1 wind speed retrieval to platform attitude uncertainty using statistical simulations. It is suggested that a 4° increase in attitude uncertainty can produce up to 1 m∙s⁻¹ increase in RMSE, and that TDS-1 attitude data do not fully reflect actual platform attitude. We conclude that the lack of knowledge about the GNSS-R nadir antenna gain map and TDS-1 platform-attitude limits the ability to determine the achievable wind speed retrieval performance with GNSS-R on TDS-1. The paper provides recommendations that accurate attitude knowledge and a good characterisation of GNSS-R nadir antenna patterns should be prioritised for future GNSS-R missions.