Mapping near‐surface soil moisture with RADARSAT‐1 synthetic aperture radar data
Bibliographic record
Abstract
An approach for mapping near‐surface soil moisture at the watershed scale from RADARSAT‐1 synthetic aperture radar (SAR) data was developed and tested on seven RADARSAT‐1 SAR images acquired over the northern portion of the Châteauguay River Basin in southwestern Quebec, Canada, dominated by agricultural and herbaceous fields. A soil surface roughness map was first retrieved from a SAR image by inverting an empirical backscatter model with known (or assumed) soil moisture. The resulting map was then used with the backscatter model to recover near‐surface soil moisture for the remaining SAR images. Field campaigns were conducted concurrent to SAR image acquisitions to measure soil moisture and surface roughness in 24 fields. Good agreement was observed between watershed‐scale soil moisture values and measurements averaged for all sampled fields, with a correlation coefficient of 0.96 and an RMS error of 2.2%. However, considerable scatter was found between observed and SAR‐derived soil moisture estimates at the field scale. Although the generated maps reveal reasonable small‐scale soil moisture variability, no definitive conclusions could be drawn as to whether or not the proposed approach can quantify soil moisture at the field or within field scale due to insufficient ground measurements. Furrows and herbaceous and crop vegetation, which are known to affect the radar signal, appeared to have little influence on the ability to retrieve soil moisture at the watershed scale for the images analyzed in this study.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".