Rice growth monitoring using simulated compact polarimetric C band SAR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, a set of nine compact polarimetric (CP) images were simulated from polarimetric RADARSAT-2 data acquired over a test site containing two types of rice field in Jiangsu province, China. The types of rice field in the test site were (1) transplanted hybrid rice fields, and (2) direct-sown japonica rice fields. Both types have different yields and phenological stages. As a first step, the two types of rice field were distinguished with 94% and 86% accuracy respectively through analyzing CP synthetic aperture radar (SAR) observations and their behavior in terms of scattering mechanisms during the rice growth season. The focus was then on phenology retrieval for each type of rice field. A decision tree (DT) algorithm was built to fulfill the precise retrieval of rice phenological stages, in which seven phenological stages were discriminated. The key criterion for each phenological stage was composed of 1–4 CP parameters, some of which were first used for rice phenology retrieval and found to be very sensitive to rice phenological changes. The retrieval results were verified at parcel level for a set of 12 stands of rice and up to nine observation dates per stand. This gave an accuracy of 88–95%. Throughout the phenology retrieval process, only simulated CP data were used, without any auxiliary data. These results demonstrate the potential of CP SAR for rice growth monitoring applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 it