Peatland Subsurface Water Flow Monitoring Using Polarimetric L-Band PALSAR
Bibliographic record
Abstract
The potential of L-band PALSAR for monitoring water flow beneath the peat surface is demonstrated on a bog near Lac Saint Pierre (Canada). Two polarimetric ALOS acquisitions collected at spring and fall under different water conditions are used. The Touzi decomposition, which was shown to be very promising for peatland characterization using the C-band Convair 580 SAR, is applied. Like in, the information provided by the multi-polarization (HH, HV, and VV), the scattering type magnitude (the Cloude alpha or the Touzi alpha(sub s)), the single scattering eigenvalues and the entropy, cannot detect the presence of water underneath the peat surface. The Touzi scattering phase phi(sub alpha(sub s)) is shown to be the only target scattering decomposition parameter that can detect water flow variations beneath the peat surface. The fall acquisition that took place after two days rain permits demonstrating that the wave can penetrate deep into the acrotelm layer to detect the rain water that has sinked rapidly into the peat layer of high hydraulic conductivity. The spring acquisition at dry conditions permits better discrimination of poor fen from bog. Similar performance have been observed in a subarctic peatland in the Wapusk National Park using PALSAR data collected between June and September 2010. While the multi-polarization information could not detect any hydraulic changes in a sedge bulrush fen, phi(sub alpha(sub s)) can detect the peatland subsurface water level variations between the June starting permafrost melting season (13 cm active layer) and the more advanced July melting seasons (27 cm active layer). However, the scattering type phase could not detect the water level change between July and August of more advanced melting conditions (active layer thickness of 60 cm). The L-band wave does not go so deep into the fen to detect the presence of the subsurface (deeper than 27 cm) water.
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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 | 0.000 |
| 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 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".