Accuracy of Surface Water Maps Derived from Radar Satellite Imagery Compared to Multispectral Satellite Imagery
Bibliographic record
Abstract
Optical and radar remote sensing are both used to map surface water features. Since use cases range considerably in the literature between applications, a direct comparison is warranted to assess how well each perform in a wide range of geographic settings using a range of classification methods. Thus, surface water maps generated from Sentinel-1 Synthetic Aperture Radar (S1SAR) and Sentinel-2 Multispectral Instrument (S2MSI) imagery were compared across four machine learning techniques and eight diverse image areas in Canada. Additionally, the polarizations and multispectral bands were varied to understand their effect. The results were validated using high resolution satellite imagery, and analysis of variance was calculated. S2MSI consistently produced higher accuracy surface water maps compared to S1SAR. Contrary to previous understanding, the cross-polarization did not produce significantly more accurate surface water maps than like-polarization, and the same was true for dual and single polarization. The introduction of an additional band of multispectral imagery improved accuracy significantly. In flooded conditions, dual polarization produced the best results, and for the detection of ice, cross-polarization produced the best results. These findings will increase the quality and efficient generation of surface water maps for water resource management, climate change impact studies, and other disciplines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".