Sea ice detection using UK TDS-1 GNSS-R data

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A sea ice detection algorithm developed using the U.K. TechDemoSat-1 (U.K. TDS-1) global navigation satellite systems (GNSSs)-reflectometry data over the Arctic and Antarctic regions is presented. It is based on measuring the similarity of the received GNSS reflected waveform or delay Doppler map (DDM) to the coherent reflection model waveform. Over the open ocean, the scattered signal has a diffusive, incoherent nature; it is described by the rough surface scattering model based on the geometric optics and the Gaussian statistics for the ocean surface slopes. Over sea ice and, in particular, newly formed sea ice, the scattered signal acquires a coherence, which is characteristic for a surface with large flat areas. In order to measure the similarity of the received waveform or DDM, to the coherent reflection model, three different estimators are presented: the normalized DDM average, the trailing edge slope (TES), and the matched filter approach. Here, a probabilistic study is presented based on a Bayesian approach using two different and independent ground-truth data sets. This approach allows one to thoroughly assess the performance of the estimators. The best results are achieved for both the TES and the matched filter approach with a probability of detection of 98.5%, a probability of false alarm of ~ 3.6%, and a probability of error of 2.5%. However, the matched filter approach is preferred due to its simplicity. Data from AMSR2 processed using the Arctic Radiation and Turbulence Interaction STudy Sea Ice algorithm and from an Special Sensor Microwave Imager/Sounder radiometer processed by Ocean and Sea Ice SAF have been used as ground truth. A pixel has been classified as a sea ice pixel if the sea ice concentration (SIC) in it was larger than 15%. The measurement of the SIC is also assessed in this paper, but the nature of the U.K. TDS-1 data (lack of calibrated data) does not allow to make any specific conclusions about the SIC.Peer Reviewe

Sea ice detection using UK TDS-1 GNSS-R data

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A sea ice detection algorithm developed using the U.K. TechDemoSat-1 (U.K. TDS-1) global navigation satellite systems (GNSSs)-reflectometry data over the Arctic and Antarctic regions is presented. It is based on measuring the similarity of the received GNSS reflected waveform or delay Doppler map (DDM) to the coherent reflection model waveform. Over the open ocean, the scattered signal has a diffusive, incoherent nature; it is described by the rough surface scattering model based on the geometric optics and the Gaussian statistics for the ocean surface slopes. Over sea ice and, in particular, newly formed sea ice, the scattered signal acquires a coherence, which is characteristic for a surface with large flat areas. In order to measure the similarity of the received waveform or DDM, to the coherent reflection model, three different estimators are presented: the normalized DDM average, the trailing edge slope (TES), and the matched filter approach. Here, a probabilistic study is presented based on a Bayesian approach using two different and independent ground-truth data sets. This approach allows one to thoroughly assess the performance of the estimators. The best results are achieved for both the TES and the matched filter approach with a probability of detection of 98.5%, a probability of false alarm of ~ 3.6%, and a probability of error of 2.5%. However, the matched filter approach is preferred due to its simplicity. Data from AMSR2 processed using the Arctic Radiation and Turbulence Interaction STudy Sea Ice algorithm and from an Special Sensor Microwave Imager/Sounder radiometer processed by Ocean and Sea Ice SAF have been used as ground truth. A pixel has been classified as a sea ice pixel if the sea ice concentration (SIC) in it was larger than 15%. The measurement of the SIC is also assessed in this paper, but the nature of the U.K. TDS-1 data (lack of calibrated data) does not allow to make any specific conclusions about the SIC.Peer ReviewedPostprint (author's final draft

Sea ice detection using GNSS-R data from UK TDS-1

This work demonstrates a methodology to detect sea ice presence over the Arctic and Antarctic regions using Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) data obtained with the UK TDS-1 satellite. The algorithm is based on estimating the degree of coherence of the received GNSS reflected waveform or Delay-Doppler Map (DDM). While at open ocean conditions, the scattered signal follows the diffuse scattering model, over flat sea ice it follows the coherent scattering model. In order to measure the degree of coherence of the received waveform or DDM, a correlation with the clean Woodward Ambiguity Function (WAF) is performed. The more similar the received signal is to the WAF, the more coherent is the scattering, and consequently, the more likely a flat sea ice surface is involved. In order to assess the performance of the proposed estimator a probabilistic study based on a Bayesian approach is performed, using the OSISAF Sea Ice Concentration (SIC) maps as ground truth. A probability of detection of 97%, a probability of false alarm of 2%, and a probability of error of 2.5% are the best results obtained for the Arctic region.Peer Reviewe

Comparison Between Sea Surface Wind Speed Estimates From Reflected GPS Signals and Buoy Measurements

Reflected signals from the Global Positioning System (GPS) have been collected from an aircraft at approximately 3.7 km altitude on 5 different days. Estimation of surface wind speed by matching the shape of the reflected signal correlation function against analytical models was demonstrated. Wind speed obtained from this method agreed with that recorded from buoys to with a bias of less than 0.1 m/s, and with a standard derivation of 1.3 meters per second

Sea ice detection using GNSS-R data from UK TDS-1

This work demonstrates a methodology to detect sea ice presence over the Arctic and Antarctic regions using Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) data obtained with the UK TDS-1 satellite. The algorithm is based on estimating the degree of coherence of the received GNSS reflected waveform or Delay-Doppler Map (DDM). While at open ocean conditions, the scattered signal follows the diffuse scattering model, over flat sea ice it follows the coherent scattering model. In order to measure the degree of coherence of the received waveform or DDM, a correlation with the clean Woodward Ambiguity Function (WAF) is performed. The more similar the received signal is to the WAF, the more coherent is the scattering, and consequently, the more likely a flat sea ice surface is involved. In order to assess the performance of the proposed estimator a probabilistic study based on a Bayesian approach is performed, using the OSISAF Sea Ice Concentration (SIC) maps as ground truth. A probability of detection of 97%, a probability of false alarm of 2%, and a probability of error of 2.5% are the best results obtained for the Arctic region.Peer Reviewe

Tutorial on remote sensing using GNSS bistatic radar of opportunity

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.Peer ReviewedPostprint (published version

Wind Speed Measurement from Bistatically Scattered GPS Signals

Instrumentation and retrieval algorithms are described which use the forward, or bistatically scattered range-coded signals from the Global Positioning System (GPS) radio navigation system for the measurement of sea surface roughness. This roughness is known to be related directly to 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 derived from geometric optics. 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

Sea Ice Remote Sensing Using Surface Reflected GPS Signals

This paper describes a new research effort to extend the application of Global Positioning System (GPS) signal reflections, received by airborne instruments, to cryospheric remote sensing. Our experimental results indicate that reflected GPS signals have potential to provide information on the presence and condition of sea and freshwater ice as well as the freeze/thaw state of frozen ground. In this paper we show results from aircraft experiments over the ice pack near Barrow, Alaska indicating correlation between forward-scattered GPS returns and RADARSAT backscattered measurements