Crop Biophysical Parameters Estimation with a Multi-Target Inversion Scheme using the Sentinel-1 SAR Data
Bibliographic record
Abstract
In this paper, a multi-target inversion scheme is adopted for joint estimation of crop biophysical parameters from dual-pol SAR data. The single-output support vector regression (SVR) method is extended to a multi-output support vector regression (MSVR) method to estimate biophysical parameters. The MSVR is implemented for simultaneous retrieval of plant area index (PAI) and crop biomass from the Sentinel-l C-band dual-pol (VV + VH) data. In this particular study, the inversion algorithm is trained and validated for the canola crop using in-situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) Manitoba campaign. The validation results indicate a good correlation coefficient (r) of 0.72 and 0.85, with a RMSE of 0.35 m2m-2and 0.48 kgm-2for PAI and wet biomass respectively. In addition, the mapped PAI and wet biomass values at flowering stage of canola capture the variability in crop growth from Sentinel-l data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".