Monitoring Bedfast Ice and Ice Phenology in Lakes of the Lena River Delta Using TerraSAR-X Backscatter and Coherence Time Series
Bibliographic record
Abstract
Thermokarst lakes and ponds are major elements of permafrost landscapes, occupying up to 40% of the land area in some Arctic regions. Shallow lakes freeze to the bed, thus preventing permafrost thaw underneath them and limiting the length of the period with greenhouse gas production in the unfrozen lake sediments. Radar remote sensing permits to distinguish lakes with bedfast ice due to the difference in backscatter intensities from bedfast and floating ice. This study investigates the potential of a unique time series of three-year repeat-pass TerraSAR-X (TSX) imagery with high temporal (11 days) and spatial (10 m) resolution for monitoring bedfast ice as well as ice phenology of lakes in the zone of continuous permafrost in the Lena River Delta, Siberia. TSX backscatter intensity is shown to be an excellent tool for monitoring floating versus bedfast lake ice as well as ice phenology. TSX-derived timing of ice grounding and the ice growth model CLIMo are used to retrieve the ice thicknesses of the bedfast ice at points where in situ ice thickness measurements were available. Comparison shows good agreement in the year of field measurements. Additionally, for the first time, an 11-day sequential interferometric coherence time series is analyzed as a supplementary approach for the bedfast ice monitoring. The coherence time series detects most of the ice grounding as well as spring snow/ice melt onset. Overall, the results show the great value of TSX time series for monitoring Arctic lake ice and provide a basis for various applications: for instance, derivation of shallow lakes bathymetry, evaluation of winter water resources and locating fish winter habitat as well as estimation of taliks extent in permafrost.
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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) | 0.000 | 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".