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 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> and 0.48 kgm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".