WEAK RESPONSE OF VEGETATION PHOTOSYNTHESIS TO METEOROLOGICAL DROUGHTS IN SOUTHWEST CHINA: INSIGHTS FROM GOME-2 SOLAR-INDUCED FLUORESCENCE
Bibliographic record
Abstract
Drought stress threatens vegetation dynamics across diverse ecosystems. Monitoring how vegetation responds to water stress is vital for ecological conservation. The response of vegetation photosynthesis to water availability variations in Southwest China from 2008 to 2018 is investigated in this study. The solar-induced fluorescence (SIF) derived from GOME-2 is used to characterize photosynthetic changes. We examined the sensitivity of SIF anomaly to standardized precipitation-evapotranspiration index (SPEI) at multiple time scales to evaluate the drought impacts on different ecosystems (i.e. forests, croplands, grasslands, and shrublands). We find that (1) SIF has significant yet weak correlations to SPEI across major ecosystems in Southwest China; (2) Forests are more sensitive to short-term droughts in comparison with other ecosystems. (3) Cropland, grassland, and shrubland are more subjected to long-term droughts compared to forests. Our findings indicate that, in Southwest China, satellite SIF may not be effective in monitoring the impact of drought on vegetation due to its weak response to SPEI. The robustness of using satellite-observed SIF to assess drought’s effects still needs to be further tested with high-resolution SIF data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".