Remote sensing-based rice mapping in Brazil: Identifying the best approach for segmenting different spectral compositions using deep learning
Bibliographic record
Abstract
This study explored the mapping of irrigated rice fields using remote sensing data and deep learning techniques, focus- ing on the evaluation of various spectral band combinations and polarizations from Sentinel-1 and Sentinel-2 satellites. Three deep learning models, called UNET, FAPNET, and PLANET, were implemented to perform segmentation of rice fields within a region located in southern Brazil. Specifically, the FAPNET model outperformed others when using vegetation-related spectral bands (NIR, RED), while the PLANET model demonstrated greater efficacy with water-related bands (SWIR, VH, VV). However, PLANET struggled with multi-band configurations, and its slower convergence indicated the need for refined training strategies. The integration of optical and SAR data did not lead to significant performance improvements for these models, suggesting that their architectures are limited in processing more than three input channels. In contrast, the UNET model exhibited greater robustness when handling diverse data combinations, achieving balanced performance even with the integration of optical and SAR data. This suggests that while FAPNET and PLANET specialize in extracting features from specific spectral bands, UNET is more adaptable to multi-source inputs. These findings highlight the role of thoughtful model and data choice, illustrating that special- ized structures perform well with particular data setups, whereas more generalized models are superior at synthesizing various data sources. Future research should focus on enhancing PLANET’s multi-band capabilities and improving FAPNET’s sensitivity to SWIR, advancing segmentation precision across a broader range of spectral profiles. This study contributes to the field of crop mapping through remote sensing by providing evidence that indiscriminate data fusion is not always the optimal approach, advocating for model and spectral band choices tailored to the specific application requirements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".