A high-resolution satellite-based solar-induced chlorophyll fluorescence dataset for China from 2000 to 2022
Bibliographic record
Abstract
Solar-induced chlorophyll fluorescence (SIF) serves as a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5P mission offers nearly global coverage with a fine spectral resolution for reliable SIF retrieval. However, the present satellite-derived SIF datasets are accessible only at coarse spatial resolutions, constraining its applications at fine scales. Here, we utilized a weighted stacking algorithm to generate a high spatial resolution SIF dataset (500 m, 8-day) in China (HCSIF) from 2000 to 2022 from the TROPOMI with a spatial resolution at a nadir of 3.5 km by 5.6–7 km. Our algorithm demonstrated high accuracy on validation datasets (R 2 = 0.87, RMSE = 0.057 mW/m 2 /nm/sr). The HCSIF dataset was evaluated against OCO-2 SIF, GOME-2 SIF tower-based measurements of SIF, and gross primary productivity (GPP) from flux towers. We expect this dataset can potentially advance the understanding of fine-scale terrestrial ecological processes, allowing for monitoring of ecosystem biodiversity and precise assessment of crop health, productivity, and stress levels in the long term.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".