Comparative Analysis of UAVSAR Polarimetric Decompositions for Wetland Aboveground Biomass Mapping Using Machine Learning Models
Bibliographic record
Abstract
Abstract. Wetlands play a vital role in carbon sequestration, biodiversity conservation, and water regulation, making their accurate monitoring essential for environmental management. Synthetic Aperture Radar (SAR) is particularly effective for assessing wetland ecosystems due to its ability to penetrate vegetation and capture biomass dynamics under various weather conditions. This study leverages UAVSAR quad-polarization data to estimate aboveground biomass (AGB) in the wetlands of southern Louisiana, USA, a region with diverse wetland types and significant ecological importance. A total of 103 features were extracted from UAVSAR data using various polarimetric decomposition methods, including Zhang, Huynen, Van Zyl, and others. Three machine learning models, including Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HGB) were employed to evaluate the effectiveness of these decompositions. Results indicated that the Zhang decomposition, combined with HGB, achieved the highest accuracy with an R2 of 0.74 and an RMSE of 183.95 g m−2, outperforming other decomposition methods and classifiers. Additionally, RF showed strong performance, while SVM consistently underperformed. These findings highlight the potential of UAVSAR-derived polarimetric features for wetland biomass estimation, demonstrating that targeted decomposition selection and advanced machine learning models can enhance accuracy. This study provides valuable insights for improving wetland monitoring and conservation efforts, supporting ecosystem management, and climate change mitigation strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".