An Integrated Surface Parameter Inversion Scheme Over Agricultural Fields at Early Growing Stages by Means of C-Band Polarimetric RADARSAT-2 Imagery
Bibliographic record
Abstract
Many research studies have investigated surface parameter inversion for bare soils. This paper attempts to take into account the agricultural fields with crop residues and fields under low vegetation cover in addition to bare soil fields. An integrated surface parameter inversion scheme (ISPIS) is proposed to invert surface parameters in these agricultural fields based on the analysis of H-α parameters at the early crop growing stages, in which the calibrated integral equation model (CIEM) is adopted to invert surface parameters for bare soils, and an adaptive two-component decomposition combined with the CIEM and a simplified adaptive volume scattering model is developed for fields with crop residues and under low vegetation cover. Fully polarimetric RADARSAT-2 data with ground truth collected on April 29 and May 9 in 2013 and from May to June in 2014 are used for validation. Compared with other methods, the derived volumetric soil moisture (MV) and surface roughness (KS) of all agricultural fields are consistent with verifiable observations with the lowest overall root mean square error: 6.12 [vol.%] and 0.48, respectively, when all sample sites are considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".