Automatic detection of Cyanobacterial blooms using multi-source optical satellite imagery: method development and application
Bibliographic record
Abstract
Cyanobacterial blooms (CyanoBloom) are a widespread environmental concern in lakes, and optical satellite imagery has been widely used to monitor their spatiotemporal dynamics. Many spectral-based CyanoBloom detection methods rely on Rayleigh- or atmosphere-corrected surface reflectance. However, these methods require ancillary atmospheric data and preprocessing procedures, thereby limiting their operational efficiency and scalability. The potential of multi-source satellite imagery remains underexploited due to the lake of robust methods for automatic CyanoBloom detection across various satellite platforms. To address this challenge, we proposed a novel automatic CyanoBloom detection (ACD) method that directly utilizes satellite top-of-atmosphere reflectance ( R TOA ) data. Cross-index and cross-sensor analyses confirmed the high detection accuracy and robustness of the ACD method against atmospheric and observational variations. The method was successfully applied to various inland lakes, including Lake Taihu, Lake Chaohu, Lake Dianchi, and Lake Xingyun in China, Lake Okeechobee in the United States, and Lough Neagh in Northern Ireland. Requiring only for four spectral bands (blue, green, red, and near-infrared), the ACD method was compatible with various optical sensors, including HY1C/D-CZI, GF-WFV, GF4-PMS, HJ-CCD, Sentinel2-MSI, Landsat9-OLI, and Terra-MODIS. This study presents a novel automatic and rapid approach for detecting CyanoBloom in lakes, providing valuable technical support for water quality monitoring and bloom management.
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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 | 0.000 |
| 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".