Soil moisture retrieval using L-band time-series SAR data from the SMAPVEX12 experiment
Bibliographic record
Abstract
The algorithms for retrieving soil moisture contents within the top 5 cm of the soil using the L-band multipolarized radar data from the future Soil Moisture Active and Passive (SMAP) mission were applied to the data sets obtained by the recent aircraft field campaign in Winnipeg Canada in 2012 (SMAPVEX12), and the algorithm performance was evaluated. Two algorithms are: the time-series inversion of radar scattering forward models data-cubes and the change detection method. The SMAPVEX12 data sets include airborne synthetic aperture radar (SAR) data and ground-based measurements of soil and vegetation. These data were collected over fields with diverse crops and a wide range of moisture and vegetation conditions. In general, volumetric soil moisture and backscattering coefficients showed a positive relationship and the vegetation effects were significant for corn and beans. Assessed over all available fields of corn, beans, pasture, and wheat, the datacube time-series inversion resulted in a retrieval rmse of 0.050 to 0.090 cm3/cm3, and correlations of 0.5 to 0.9. Compared with the change detection approach, the data-cube inversion performed better in the presence of significant vegetation growth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".