C‐band SAR backscatter characteristics of Arctic sea and land ice during winter
Bibliographic record
Abstract
Abstract Synthetic Aperture Radar (SAR) data has become an important tool for studies of polar regions, due to high spatial resolution even during the polar night and under cloudy skies. We have studied the temporal variation of sea and land ice backscatter of twenty‐four SAR images from the European Remote Sensing satellite (ERS‐1) covering an area in Lady Ann Strait and Jones Sound, Nunavut, from January to March 1992. The presence of fast ice in Jones Sound and glaciers and ice caps on the surrounding islands provides an ideal setting for temporal backscatter studies of ice surfaces. Sample regions for eight different ice types were selected and the temporal backscatter variation was studied. The observed backscatter values for each ice type characterize the radar signatures of the ice surfaces. This time series of twenty‐four SAR images over a 3‐month period provides new insights into the degree of temporal variability of each surface. Ice caps exhibit the highest backscatter value of ‐3.9 dB with high temporal variability. Valley glacier ice backscatter values decrease with decreasing altitude, and are temporally the most stable, with standard deviations of 0.08–0.10 dB over the 90‐day period. First‐year ice and lead ice show a negative trend in backscatter values in time and a positive correlation of up to 0.59 with air temperature over the 90‐day period. For first‐year ice and lead ice, episodes of large temperature fluctuations (±12°C) are associated with rapid changes in backscatter values (±2 dB). We attribute the backscatter increase to a temperature‐induced increase in brine volume at the base of the snow pack. Multi‐year ice, conglomerate ice and shore ice are relatively stable over the 3‐month period, with a backscatter variation of only a few dBs. An observed lag time of up to three days between backscatter increase/decrease and air temperature can be attributed to the insulation effect of the snow cover over sea ice. The net range of the backscatter values observed on the most temporally stable surface, valley glacier ice, of about 0.30 dB indicates that the ERS‐1 SAR instrument exceeds the 1 dB calibration accuracy specified for the Alaska SAR Facility processor for the three winter months.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".