Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine
Bibliographic record
Abstract
Rice phenology information is important in supporting planning systems, land management, and making the right decisions to sustainably carry out rice production. This study aimed to determine the best rice phenology classification model by combining VV and VH polarizations on Sentinel-1 images, which produce polarization indices such as the ratio polarization index (RPI), normalized different polarization index (NDPI), and average polarization index (API) using the ensemble random forest (RF) using the Google Earth Engine (GEE) application. This research was conducted in the rice fields of PT Sang Hyang Seri, Subang Regency, West Java. The research data comprised Sentinel-1 SAR GRD satellite imagery data with acquisition modes interferometric wide swath (IW) for 2021–2022 obtained from the GEE platform. In this study, the performance of two machine learning methods for classification was compared: classification and regression trees (CART) and RF. This study found that the best rice phase classification model could be acquired from the RF method with four predictors, namely, API, RPI, NDPI, and slope, with a statistical value of kappa of 98.22%. The RF classification model has better accuracy than the CART classification model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".