Compact-Polarimetric SAR Signature Analysis for Wetland Characterization Using RADARSAT Constellation Mission
Bibliographic record
Abstract
Effective monitoring of wetlands plays a pivotal role in comprehending and managing these ecologically vital ecosystems. This study assesses the potential of C-band synthetic aperture radar (SAR) imagery in compact polarization (CP) mode, utilizing the RADARSAT Constellation Mission (RCM), for wetland characterization. We introduce the compact-polarimetric signature (CPS) as a novel descriptor to delineate wetlands, including bog, fen, and marsh classes. In addition, we propose an alternative decomposition technique (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu -\chi $ </tex-math></inline-formula>) to segment the total power into three components: odd-bounce scattering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(P_{s})$ </tex-math></inline-formula>, double-bounce scattering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(P_{d})$ </tex-math></inline-formula>, and random scattering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(P_{v})$ </tex-math></inline-formula>. For our evaluation, we selected a test site in New Brunswick, Canada, and acquired a series of RCM datasets covering this region. The time-series CPS plots yield valuable insights, elucidating the scattering mechanisms of different wetland classes. Notably, these plots reveal that during the active season, characterized by changing vegetation structures, the scattered waves exhibit variations, leading to changes in received power and the purity parameter (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>). Furthermore, the observed variations in the proposed power components demonstrate a significant discriminatory capacity among wetlands. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{s}$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{d}$ </tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{v}$ </tex-math></inline-formula> components effectively distinguish bog, fen, and marsh classes, respectively, capturing the unique characteristics of each wetland type. These findings carry considerable potential for advancing wetland characterization through the RCM CP-SAR mission. The improved discriminative ability among different wetland classes is a valuable contribution to the broader field of wetland ecology and management. This advancement potentially empowers precise wetland classification, facilitating well-informed decision-making in wetland preservation and resource allocation. The applications of these findings extend to ecosystem monitoring, environmental impact assessments, and the long-term evaluation of wetland health. Eventually, this contributes to developing more effective wetland conservation and management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 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".