Joint use of the Sentinel-1–derived Kennaugh parameters and Sentinel-2 data for temporal landcover dynamics over the Indian Sundarbans region
Bibliographic record
Abstract
Rapid changes in the surrounding areas of the Indian Sundarbans substantially influence local habitats and the larger ecosystem, impacting biodiversity, water quality, and environmental stability. Obtaining real-time ground truth data might be challenging due to minimal human involvement. Therefore, evaluating the temporal dynamics in landcover is crucial in these regions. We use the Sentinel-1–derived Kennaugh matrix elements and Sentinel-2 data to classify 13 land cover types over this region. To monitor seasonal and inter-annual fluctuations, we focused on the pre-monsoon (April) and post-monsoon (October) times throughout 6 years from 2018 to 2023. Three machine learning algorithms, extreme gradient boosting (XGB), random forest, and light gradient boosting machine, are utilized for classification purposes. With an overall classification accuracy of 98% by combining optical bands with Kennaugh components, XGB outperformed the other methods in precision. In contrast, individual features resulted in an accuracy range of only 50% to 90%. This approach offers a practical solution for understanding wetland dynamics without ground truth data, making it highly adaptable and scalable for wetland monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".