Sea surface oil slick detection and wind field \nmeasurement using global navigation satellite \nsystem reflectometry
Bibliographic record
Abstract
In this thesis, research for improving sea surface remote sensing using the Global \nNavigation Satellite System-Reflectometry (GNSS-R) signals is presented. Firstly, a \nmethod to enable the simulation of GNSS-R delay Doppler Map (DDM) of an oil \nslicked sea surfaces under general scenarios is proposed. The DDM of oil slicked \nsea surface under general scenarios is generated by combining the mean-square slope \nmodel for oil slicked/clean surfaces and the GNSS-R Zavorotny-Voronovich (Z-V) \nscattering model. The coordinate system transformation appropriate for general- \nelevation-angle scenarios is also incorporated. Secondly, a technique to detect sea \nsurface oil spills using reflections from Global Navigation Satellite System (GNSS) \nsatellites is presented. This technique is implemented by compensating the distor- \ntion induced during the DDM deconvolution process of scattering coefficient retrieval \nand employing the spatial integration approach (SIA) to retrieve the scattering co- \nefficients unambiguously using the DDMs obtained by two separate antenna beams. \nA performance characterization including retrieval accuracy and resolution is demon- \nstrated with respect to the signal-to-noise ratio and the size of oil slicks, respectively. \nSimulation based on the oil slick distribution of the Deepwater Horizon oil spill ac- \ncident shows that the retrieval error can be reduced by the SIA after the distortion \ncorrection. The technique proposed here can be used to map oil slick extent on the \nocean surface or it may be applied generically to produce physical surface maps of the bistatic scattering coefficient from multiple DDM’s from a single space-based platform. \nLastly, a novel method is presented to retrieve sea surface wind speed and direction \nby fitting the two-dimensional simulated GNSS-R DDMs to measured data. An 18- \nsecond incoherent correlation is performed on the measured signal to reduce the noise \nlevel. Meanwhile, a variable step-size iteration as well as a fitting threshold are used \nto reduce the computational cost and error rate of the fitting procedure, respectively. \nUnlike previous methods, all the DDM points with normalized power higher than the \nthreshold are used in the least-square fitting. An optimal fitting threshold is also \nproposed. To validate the proposed method, the retrieval results based on a dataset \nfrom the United Kingdom Disaster Monitoring Constellation satellite are compared \nwith the in-situ measurements provided by the National Data Buoy Center, and good \ncorrelation is observed between the two.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".