-In traditional GNSS applications, signals arriving at a receiver's antenna from nearby reflecting surfaces (multipath) interfere with the signals received directly from the satellites which can often result in a reduction of positioning accuracy. About two decades ago researchers produced an idea to use reflected GNSS signals for remote-sensing applications. In this new concept a GNSS transmitter together with a receiver capable of processing GNSS scattered signals of opportunity becomes bistatic radar. By properly processing the scattered signal, this system can be configured either as an altimeter, or a scatterometer allowing us to estimate such characteristics of land or ocean surface as height, roughness, or dielectric properties of the underlying media. From there, using various methods the geophysical parameters can be estimated such as mesoscale ocean topography, ocean surface winds, soil moisture, vegetation, snowpack, and sea ice. Depending on the platform of the GNSS receiver (stationary, airborne, or spaceborne), the capabilities of this technique and specific methods for processing of the reflected signals may vary. In this tutorial, we describe this new remotesensing technique, discuss some of the interesting results that have been already obtained, and give an overview of current and planned spacecraft missions.

Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity

Instrumentation and retrieval algorithms are described which use the forward scattered range-coded signals from the global positioning system (GPS) radio navigation system for the measurement of sea surface roughness. This roughness has long been known to be dependent upon the surface wind speed. Experiments were conducted from aircraft along the TOPEX ground track and over experimental surface truth buoys. These flights used a receiver capable of recording the cross-correlation power in the reflected signal. The shape of this power distribution was then compared against analytical models, which employ a geometric optics approach. Two techniques for matching these functions were studied. The first recognized the most significant information content in the reflected signal is contained in the trailing edge slope of the waveform. The second attempted to match the complete shape of the waveform by approximating it as a series expansion and obtaining the nonlinear least squares estimate. Discussion is also presented on anomalies in the receiver operation and their identification and correction.

/pdf/wind-speed-measurement-using-forward-scattered-gps-signals-1ucbqkpe6o.pdf

Wind speed measurement using forward scattered GPS signals

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 experiment in 2003. Examples of onboard-processed delay Doppler Maps reveal excellent data quality for winds up to 27.9 m/s. Collocated ASCAT scatterometer winds are used to develop and evaluate a wind speed algorithm based on Signal-to-Noise ratio (SNR) and the Bistatic Radar Equation. For SNR greater than 3 dB, wind speed is retrieved without bias and a precision around 2.2 m/s between 3–18 m/s even withoutcalibration. 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, low-power GNSS-R receivers ahead of their launch on the NASA CYGNSS constellation in 2016.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015GL064204

Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat‐1 mission

[1] We present the first two aircraft Global Positioning System (GPS)-reflection altimetry measurements, the most precise GPS ocean-altimetry measurement, and demonstrate the altimetric precision and spatial resolution necessary to map mesoscale eddies. Our first experiment demonstrated a 14-cm precision single-satellite ocean altimetry measurement while our more recent experiment demonstrates 5 cm altimetric precision with 5-km spatial resolution. The new results show significant improvement over our previous effort, due to improved modeling, greater aircraft altitudes and velocities, improved receiver positioning, and better experimental control. Plans to further reduce speckle and refine models to obtain 5-cm altimetric accuracy are presented.

/pdf/5-cm-precision-aircraft-ocean-altimetry-using-gps-32fo2rjo09.pdf

5-cm-Precision aircraft ocean altimetry using GPS reflections

http://center.shao.ac.cn/geodesy/publications/Jin_2011ASR.pdf

Remote sensing using GNSS signals: Current status and future directions

Global positioning system (GPS) signals reflected from the ocean surface can be used for various remote sensing purposes. Some possibilities include measurements of surface roughness characteristics from which the rms of wave slopes, wind speed, and direction could be determined. In this paper, reflected GPS measurements that were collected using aircraft with a delay mapping GPS receiver are used to explore the possibility of determining ocean surface wind speed and direction during flights to Hurricanes Michael and Keith in October 2000. To interpret the GPS data, a theoretical model is used to describe the correlation power of the reflected GPS signals for different time delays as a function of geometrical and sea-roughness parameters. The model employs a simple relationship between surface-slope statistics and both a wind vector and wave age or fetch. Therefore, for situations when this relationship holds there is a possibility of indirectly measuring the wind speed and the wind direction. Wind direction estimates are based on a multiple-satellite nonlinear least squares solution. The estimated wind speed using surface-reflected GPS data collected at wind speeds between 5 and 10 ms 21 shows an overall agreement of better than 2 m s 21 with data obtained from nearby buoy data and independent wind speed measurements derived from TOPEX/Poseidon, European Remote Sensing (ERS), and QuikSCAT observations. GPS wind retrievals for strong winds in the close vicinity to and inside the hurricane are significantly less accurate. Wind direction agreement with QuikSCAT measurements appears to be at the 308 level when the airplane has both a stable flight level and a stable flight direction. Discrepancies between GPS retrieved wind speeds/directions and those obtained by other means are discussed and possible explanations are proposed.

https://journals.ametsoc.org/doi/pdf/10.1175/1520-0426(2004)021%3C0515%3AROOSWS%3E2.0.CO%3B2

Retrieval of Ocean Surface Wind Speed and Wind Direction Using Reflected GPS Signals

Exploiting GPS as a New Oceanographic Remote Sensing Tool

Global Positioning System (GPS) signals reflected from the ocean surface have potential use for various remote sensing purposes. Some possibilities are measurements of surface roughness characteristics from which wave height, wind speed and direction could be determined. 

In this paper, recent reflected GPS measurements collected via aircraft with a delay mapping GPS receiver, are used to explore the possibility of determining ocean surface wind speed and direction during Hurricanes Michael and Keith in October, 2000. To interpret the GPS data, a theoretical model is used to describe the correlation power of the
reflected GPS signals for different time delays as a function of geometrical and environmental parameters. 

Wind direction estimates are based on a multiple satellite non-linear least squares solution. The estimated wind speed using surface-reflected GPS data collected at a variety of wind speed conditions shows an overall agreement better than 2 m/s with data obtained from
nearby buoy data and independent wind speed measurements derived from TOPEX/Poseidon, ERS, and QuikSCAT observations. Wind direction agreement with QuikSCAT measurements appear to be at the 30 degree
level. In addition to the actual results we also present an error analysis describing the expected performance of the GPS technique.

Developments in Using GPS for Oceanographic Remote Sensing: Retrieval of Ocean Surface Wind Speed and Wind Direction

Initial estimates of ocean surface wind speed and direction based on observations of reflected GPS signals are presented and compared to QuikSCAT wind fields. The two wind speed estimates are generally in agreement to within 2-3 m/s, and under favorable conditions of well developed seas and stable winds, direction estimates agree to within 10 deg. An overview of the GPS technique is presented as well as a presentation and discussion of these first results.

/pdf/a-comparison-of-gps-and-scatterometer-sensing-of-ocean-wind-38ul38yrf9.pdf

A comparison of GPS and scatterometer sensing of ocean wind speed and direction

Previous papers have demonstrated the ability to determine wind speed and direction from ocean-reflected GPS signals observed at aircraft altitudes to within 2 m/s and 20 degrees of in situ measurements. This paper presents a preliminary step towards extending GPS-based wind vector retrievals to spacecraft altitudes. The work takes the form of visibility, sensitivity, and noise analyses. The visibility analysis reveals the number of ocean-reflected GPS signals visible per day, a value that drives the uncertainty in wind vector estimates. The sensitivity analysis determines which delay/Doppler cells of a glistening zone visible from space are most sensitive to changes in wind speed and direction. Finally, the noise analysis uses results from the previous two analyses to determine the uncertainty in wind vector retrievals using the cells identified in the sensitivity analysis.

M. Armatys

Papers

Retrieval of Ocean Surface Wind Speed and Wind Direction Using Reflected GPS Signals

Exploiting GPS as a New Oceanographic Remote Sensing Tool

Developments in Using GPS for Oceanographic Remote Sensing: Retrieval of Ocean Surface Wind Speed and Wind Direction

A comparison of GPS and scatterometer sensing of ocean wind speed and direction

GPS-based remote sensing of ocean-surface wind speed from space