Détection du gel et non-gel du sol en utilisant le radar polarimétrique à synthèse d’ouverture
Bibliographic record
Abstract
Radar polarimetry is a recent field, which offers increased detection possibilities compared with the monopolarisation radar sensors. The goal of this study is to evaluate the polarimetric data potential to monitor soil freezing in an agricultural environment. With this intention, three agricultural fields were selected, in two experimental stations of Agriculture and Agri-Food Canada: Chapais and Harlaka. Data on soil temperature at three depths (1, 5, and 15 cm), as well as certain characteristics of the snow cover (density, snow height, and temperature), were collected during polarimetric image acquisitions using polarimetric C-band synthetic aperture radar (SAR) system on board Convair-580 aircraft operated by Environment Canada. Several approaches of polarimetric treatments were explored and compared: Pauli decomposition, Cloude and Pottier decomposition, polarimetric signature, and copolarized phase difference. The results obtained show that some soil surface conditions (frozen–unfrozen) can be discriminated by radar polarimetry. Indeed, with the Cloude and Pottier decomposition, this difference appears, when the soil freezes, by a decrease of the entropy values (H) and the α angle, and an increase of the anisotropy (A). The copolarisation signatures comparison shows well the differences between the pedestal height and the signature shape in the case of a frozen soil and an unfrozen one. Even if SAR polarimetry appears as a mean to discriminate soil freezing, it seems necessary to acquire a fall image, in a period without freezing, so we can compare it with the values of the parameters obtained when the soil is frozen.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".