Identifying paddy fields with dual-polarization ALOS/PALSAR data
Bibliographic record
Abstract
Accurate documentation of paddy-rice cultivation areas is valuable in estimating rice production for food security and in assessing the environmental impacts of rice ecosystems such as water consumption, soil degradation, and river eutrophication. This study explores the feasibility of dual-polarization L-band advanced land observing satellite/phased array-type L-band synthetic aperture radar (ALOS/PALSAR) imagery acquired during three growing stages in delineating paddy fields from other agricultural land uses. The study area was in the Yangtze River Delta of east China, a rapidly developing region where land has been intensively used. Among a set of arithmetic outputs of PALSAR backscatter, the amplitude ratio (HH/HV) and product (HH×HV) in the rice-transplanting and -heading stages significantly enhanced the backscatter difference between rice and nonrice fields. With these outputs, a segmentation-based decision-tree classifier successfully extracted paddy-rice fields from other land uses in the study area. Both random-point accuracy assessment and area comparison with a high-resolution Quickbird image showed that the paddy-rice map reached accuracies higher than 90%. The simplicity and generality of the approach in this study indicated that it may serve as an efficient tool for rice mapping in the highly fragmented agricultural region in southeast China.
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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".