Assessment of l-band and c-band scatterometry for agricultural and Arctic remote sensing
Bibliographic record
Abstract
Climate change is drastically affecting agricultural practices and the Arctic environment as warmer conditions and extreme weather events are becoming increasingly common. Agricultural practices are having to adapt to longer periods of drier conditions and heavy rain events. At the same time, warmer global temperatures and decreasing sea ice concentrations are allowing for increased marine traffic throughout the year, posing the threat of an oil spill. The application of microwave radar systems for environmental monitoring is not a new practice but the conditions that are being monitored are continuing to evolve with the changing climate. This thesis aims to determine the usability of C-band (5.5GHz) and L-band (1.26 GHz) ground-based polarimetric microwave radar systems for agricultural monitoring (C-band and L-band) as well as detecting diesel fuel in the Arctic Ocean (C-band). Findings of the agricultural monitoring study suggest that both C-band and L-band scatterometers can detect changes in vegetation height, while C-band was only correlated with soil moisture at 5cm whereas L-band had correlations at all soil moisture depths that were monitored. Results of the diesel fuel study determined that there were significant changes to received C-band backscatter during the transition from open water to sea ice, as well as when diesel fuel emerged to the surface of the sea ice. These studies will contribute to a baseline of data that can be used to better understand satellite data for agricultural remote sensing purposes. The results of the agricultural remote sensing studies l further the understanding of the sensitivity of L-band microwave radar to vegetation cover as well as the optimal radar system parameters to determine soil moisture from radar backscatter values. The outcome of the diesel-contaminated sea ice experiments will improve and aid northern Canadian communities’ preparedness and response protocols to an oil spill in the Arctic marine environment in partnership with the Canadian Standards Association Group.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".