AlpSAR 2012-13, a Field Experiment on Snow Observations and Parameter Retrievals with Ku- and X-Band Radar
Bibliographic record
Abstract
In order to support the development of observation techniques and inversion algorithms for the retrieval of physical snow parameters from high frequency radar backscatter data, field campaigns have been performed in previous winters in Northern Finland, Canada and United States. In winter 2012-13 additional field campaigns take place in the Austrian Alps within the AlpSAR experiment. AlpSAR is aimed at enhancing the experimental data base on radar backscatter by covering different snow regimes and terrain types than in previous campaigns. A main objective of the campaign is the testing and validation of algorithms for retrieval of the water equivalent (SWE) of winter snow cover over different land cover types in Alpine regions. The activities are contributing to feasibility studies for the ESA Earth Explorer Candidate Mission CoReH2O (Cold Regions Hydrology High-resolution Observatory). The proposed satellite sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations. In the AlpSAR experiment backscatter data are collected with an airborne polarimetric SAR sensor (SnowSAR) which operates at the same frequencies as the CoReH2O SAR. Field measurements are performed at three test sites in different elevation zones in order to cover various snow regimes: Leutasch at 1200 m elevation, where the land cover is dominated by meadows and coniferous forest, Rotmoostal at about 2200 m elevation in the Alpine tundra region, and Mittelbergferner, an Alpine glacier. Measurement campaigns are carried out on three different dates during the winter period: mid-November to catch the start of the snow season, January in mid-winter, and late February before the start of the melting period. In autumn 2012 automated measurement stations were deployed in the test sites to record snow, soil and atmospheric parameters. During the flight campaigns intensive snow measurements are performed, including the measurement of vertical profiles of snow parameters in a network of snow pits, GPR transects on snow depth and stratification, terrestrial laser scanner measurements, and UAV imaging. The data analysis deals with the evaluation of backscatter properties in dependence of snow parameters to validate backscatter forward models, and with the inversion of the SnowSAR backscatter images to derive and validate maps of snow water equivalent.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".