Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter
Bibliographic record
Abstract
Information on the timing of melt onset over sea ice is important for understanding the Arctic's changing climate. The daily temporal resolution of passive microwave brightness temperatures provides the most widely utilized observations to detect melt onset but are limited to a spatial resolution of 25 km. Wide-swath synthetic aperture radar (SAR) imagery provides a much higher spatial resolution (20–100 m) but melt onset detection remains challenging because of i) insufficient temporal resolution to facilitate accurate melt onset detection, ii) inconsistent viewing geometries and iii) limited image availability across the Arctic. Here, we construct high temporal resolution composite gamma nought backscatter products (1 day, 1–2 day and 2–4 day) using Sentinel-1 and RADARSAT-2 over a close-to-seamless revisit region located in northern Canadian Arctic and Greenland for estimating melt onset over Arctic sea ice in 2016 and 2017. We employ the necessary radiometric terrain flattening and local resolution weighting techniques to generate normalised backscatter over the entire study region, removing restrictions limiting analysis to a single sensor or track's swath width by integrating both ascending and descending passes into the composite products. Results indicate that higher temporal resolution multi-sensor composite gamma nought products (1 day) that make use of the most imagery provide a robust temporal evolution of the backscatter. This allows for more representative estimates of melt onset as it is easier to separate the melt onset threshold from winter variability that is otherwise a considerable challenge for SAR based melt onset algorithms because of inconsistent temporal resolution. Multi-sensor composite gamma naught melt onset detection is in good agreement with melt onset estimates derived from the Advance Scatterometer (ASCAT) backscatter values and passive microwave brightness temperatures over homogenous sea ice regions but very noticeable improvement was found within narrow channels and regions with more heterogeneous sea ice. In anticipation of the availability of data from even more SAR satellites with the launch of the RADARSAT Constellation Mission, the multi-sensor composite gamma nought approach presented here may offer the most robust approach to estimate the timing of melt onset over sea ice across the Arctic using high spatiotemporal resolution SAR.
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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) | 0.001 | 0.001 |
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".