Assessment of the Potential Use of Sentinel-1 C-Band SAR Sigma-Naught and Gamma-Naught Features to Support Rice Monitoring Activities
Bibliographic record
Abstract
The availability of the cloud-free and publicly accessible Sentinel-1 C-band SAR data allows the development of large-scale and continuous remote sensing (RS)-based rice monitoring activities. This study examines backscatter values derived using ground-range radar cross-section (sigma-naught) and slant-range perpendicular radar cross-section (gamma-naught) on both polarization channels as features for classifying the rice transplanting period on rice fields in Subang Regency, Indonesia, using Classification and Regression Trees, Support Vector Machine, Random Forest, and Gradient Boosting classifiers. Overall, the gamma-naught features produced higher backscatter values over a rice-growing cycle, i.e., ranging from 5.8 to 8.8 percent and from 9.2 to 15.8 percent higher for VH and VV channels, respectively, than that generated by sigma-naught. Furthermore, overall accuracy (OA) and kappa coefficient (K) of gamma-naught features were superior to those derived by sigma-naught in all observed classifiers. Subsequently, the accuracy increment is higher in K than in OA, ranging from 3.2 to 18.6 percent for OA and from 5.1 to 41.1 percent for K. To conclude, the gamma-naught features have much higher potential than sigma-naught in classifying the rice transplanting period, further aiding SAR-supported RS-based rice monitoring activities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".