Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness
Bibliographic record
Abstract
Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".