Robust retrieval of surface soil moisture across wide-ranging incidence angles over short crops: for application to NI-SAR
Bibliographic record
Abstract
We present our ongoing efforts to deliver surface soil moisture information at agricultural field scales using airborne or satellite synthetic aperture radar (SAR) data through the development and inversion of physical models for forward radar scattering from vegetation surfaces. While the past successful results were validated at 40-deg incidence angle for the Soil Moisture Active passive mission, the current work extends the incidence angle range from 30 to 50 degs so that the algorithm may apply to the future L-band NASA-ISRO SAR (NI-SAR) mission. NI-SAR aims at providing global soil moisture data at 200m resolution every 6 days. The soil moisture retrievals were validated over agriculture sites in Canadian Prairies using L-band airborne SAR data, where the fields experienced entire crop growth stages and two cycles of wetting and drydowns. The forward models were developed over NI-SAR’s incidence angle range of 30 to 50 degs for individual crops. The estimates are accurate to unbiased rmse of 0.053, 0.058 and 0.047 m3/m3 in volumetric water content for soybean, wheat, and pasture fields respectively over diverse conditions of vegetation growth and soil wetness. Surface roughness and vegetation amount were retrieved simultaneous to the soil moisture solutions. The roughness estimates are realistic. There was no significant effect of the local incidence angle on the retrieval performance, most likely because the path length of the radar wave through the vegetation (and therefore extinction of the soil moisture signal) did not vary much with incidence angle. The results are encouraging for successful soil moisture mapping for the NI-SAR mission.
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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 | 0.000 |
| 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) | 0.001 | 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".