Characterizing Near Nadir Ka-band Backscatter for Mapping Water Surfaces With InSAR
Bibliographic record
Abstract
To gain an empirical understanding of the utility of near-nadir Ka-band for mapping surface changes, particularly with respect to water and wet surfaces, this dissertation demonstrates the utility for mapping water surface elevation (WSE) and inundation as observed by the airborne complement of the upcoming Surface Water and Ocean Topography (SWOT), AirSWOT. To produce high-resolution WSE and extents, SWOT and AirSWOT rely on strong backscatter signal returns using the Ka-band radar frequency. However, despite having many theoretical assessments of Ka-band scattering for water surfaces, observation-based knowledge of high-resolution Ka-band scattering for small inland water bodies is limited, consequently limiting the understanding of signal errors and resultant elevation errors. AirSWOT has provided the first Ka-band InSAR observations over diverse land cover and under changing hydrologic conditions by flying through Alaska and Canada during July and August 2017, allowing an unprecedented assessment of Ka-band scattering dynamics. These flights revealed bright radar returns, akin to a theoretical open water return, over vegetation and moist, bare soils, complicating open water classification, a necessary task for the SWOT mission. AirSWOT WSE errors relative to in-situ GPS showed an average bias of -58 cm. After correcting for biases, centimeter-level seasonal hydrologic changes are identified across the study region for the period July-August 2017. Following bias correction, residual errors may be explained by the prevalence of mixed water-vegetation pixels, which may occur in 5% of observations on average, and by wind speeds below 3 m/s (6.7 mph), which reduce water surface roughness.\nThis dissertation uses airborne AirSWOT, LiDAR, model, and in-situ data to (1) demonstrate the utility of the 2017 Ka-band AirSWOT observations, showing that the error-prone dataset can nonetheless observe cm-scale hydrologic spatial and temporal gradients across the ABoVE domain; (2) identify differences and similarities in backscattering values between water and land cover types to assess the likelihood of misclassification, additionally enabling the characterization of vegetation densities alongside the heights estimated from InSAR; and (3) assess atmospheric influences from the wind on Ka-band scattering changes due to water surface roughening, creating a path for assessments at the water-air interface for small water bodies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".