Can GNSS reflectometry detect precipitation over oceans?

For the first time, a rain signature in Global Navigation Satellite System Reflectometry (GNSS‐R) observations is demonstrated. Based on the argument that the forward quasi‐specular scattering relies upon surface gravity waves with lengths larger than several wavelengths of the reflected signal, a commonly made conclusion is that the scatterometric GNSS‐R measurements are not sensitive to the surface small‐scale roughness generated by raindrops impinging on the ocean surface. On the contrary, this study presents an evidence that the bistatic radar cross section σ0 derived from TechDemoSat‐1 data is reduced due to rain at weak winds, lower than ≈ 6 m/s. The decrease is as large as ≈ 0.7 dB at the wind speed of 3 m/s due to a precipitation of 0–2 mm/hr. The simulations based on the recently published scattering theory provide a plausible explanation for this phenomenon which potentially enables the GNSS‐R technique to detect precipitation over oceans at low winds

On-line scheduling of small open shops

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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 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 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

Management of radical change A case study of Hungarian telecommunications

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