Estimating Sea Ice Concentration From SAR: Training Convolutional Neural Networks With Passive Microwave Data
Bibliographic record
Abstract
Historically, sea ice concentration (SIC) has been measured through the use of passive microwave sensors, as well as human interpretation of synthetic aperture radar (SAR). Although passive microwave data are processed automatically, it suffers from poor spatial resolution and the higher frequency channels are sensitive to weather conditions. Deep learning has demonstrated its ability to perform complex and accurate analysis of images; here, we apply deep learning to estimate ice concentration from SAR scenes. We developed a deep convolutional neural network (CNN) that predicts SIC from SAR, trained upon passive microwave data. The model achieves a 5.24%/7.87% error on its train and test set, respectively. To assess the real-world applicability, we performed an independent validation on 18 SAR scenes (from two distinct geographical regions), not previously seen during training or test. Comparing against human-generated ice analysis charts, we achieved an L1 error of 0.2059, competitive with passive microwave (E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L1</sub> = 0.1863) for the Canadian Arctic Archipelago. For the Gulf of Saint Lawrence region, we achieved an L1 error of 0.2653, significantly better than the passive microwave result (E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L1</sub> = 0.3593). By using novel techniques for model training, as well as training entirely upon passive microwave data, we present an accessible and robust method of developing similar systems for processing SAR. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our results suggest that with further postprocessing, CNNs are accurate and robust enough to be used for operational tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".