Retrieving surface roughness and soil moisture from synthetic aperture radar (SAR) data using neural networks
Bibliographic record
Abstract
An inversion technique based on neural networks has been implemented to estimate surface roughness and soil moisture over bare fields using European Remote Sensing (ERS) and RADARSAT data. The neural networks were trained with a simulated data set generated from the integral equation model. Later the networks were applied to a field data set spanning a wide range of surface roughness and soil moisture, with backscattering coefficients for three radar configurations (VV 23°, HH 39°, and HH 47°). Approaches based on two and three radar image configurations were examined and tested. Although the three-image configuration produces slightly more accurate results, a two-image configuration gives results of comparable accuracy when a favourable combination of incidence angles is adopted. The introduction of a priori information on the range of soil moisture (mv) improves mv estimation. Soil moisture and surface roughness errors were estimated at about 7.6% and 0.47 cm, respectively, using the root mean square error (RMSE).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".