Spaceborne GNSS-R Minimum Variance Wind Speed Estimator

Abstract

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).Index Terms-Delay-Doppler map, global navigation satellite systems (GNSS)-reflectometry, minimum variance (MV) estimator, ocean surface wind speed.

Progress in satellite remote sensing for studying physical processes at the ocean surface and its borders with the atmosphere and sea ice

“…The development of the Global Navigational Satellite Systems-Reflectrometry (GNSS-R) promises to provide advances in wind speed and wave monitoring (Clarizia et al, 2014). GNSS-R is a relatively new concept that exploits existing data from GNSS systems including the European Galileo and US Global Positioning System (GPS) systems by turning them into a bistatic radar system.…”

“…The main advantages of GNSS-R lie in the global availability of GNSS signals, which provides a dense coverage over the Earth and offers the potential for significant improvements in spatio-temporal sampling of the ocean winds and waves. The receiver needed to capture the surface reflections is small and cheap, low-weight/low-power, and can be easily accommodated on small satellites or satellites of opportunity, thus reducing the cost of a mission and allowing for constellations of multiple GNSS-R receivers (Clarizia et al, 2014). The GNSS-R capability is already present on some satellites including the UK Disaster Monitoring Constellation (DMC) satellite.…”

Studies on delay-Doppler mapping of GNSS-R signals

“…Therefore, the theoretical models need to be modified to simulate the DDM more accurately. In the recent study, the area around the peak value of the DDM is sensitive to the sea surface roughness [16] , which is called DDMA (Delay Doppler Map Average). Compared with the measured DDM, this part of the measured DDM is the most similar part in shape so that the DDM matching method can be focused on this area.…”

The First Application of Stare Processing to Retrieve Mean Square Slope Using the SGR-ReSI GNSS-R Experiment on TDS-1

“…4(b). Unlike Stare processing, other methods limit DDM pixel selection to a small region around the specular point in delay and Doppler in order to limit the spread of the surface area selection [2], [3]. Alternatively, deconvolution approaches have been applied to recover the scattering cross section for a known surface patch size, but impose antenna masking not possible with the TDS-1 data [18].…”