A novel feature space monitoring index of salinisation in the Yellow River Delta based on SENTINEL‐2B MSI images
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Bibliographic record
Abstract
Abstract Most of previous studies utilized the surface parameters from LANDSAT images to construct the feature space monitoring index model of salinisation (salinization), and a few studies that have combined the feature space model with SENTINEL‐2B MSI images have been reported. In addition, the red edge index derived from SENTINEL‐2B MSI images can provide more detailed information to indicate the vegetation condition when monitoring salinized land ecosystems. Based on SENTINEL‐2B MSI images, this paper introduces seven typical parameters, namely NDVI, MSAVI, SI, Albedo, NDre1, NDre2, and NDre3 (red edge index) to construct two category features space models (point‐to‐line type and point‐to‐point type), and then, a novel salinisation monitoring index for use in the Yellow River Delta (YRD). Our main conclusions showed that: (1) the monitoring index model based on SENTINEL‐2B MSI images and a feature space model has high applicability for the salinisation monitoring in the YRD, with an average precision of R 2 = 0.8499; (2) the point‐to‐point monitoring index of soil salinisation based on the NDre1‐SI feature space model has the best inversion accuracy of R 2 = 0.9305 and RMSE = 0.9926; (3) the red edge index can better indicate the state and evolution process of soil salinisation. The salinisation monitoring models that included the red edge indexes have higher inversion accuracy with an average value of R 2 = 0.8650; (4) the soil salinisation in the YRD was more serious in its eastern and northeastern regions than other parts. The results provide a new technical and methodological approach for the prevention and treatment of regional salinisation.
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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 it