Ground photography verification of remote sensing-derived vegetation phenology in the Xilinguole grassland
Bibliographic record
Abstract
Using photographic observation data of grassland phenology over the entire growing season and different satellite remote sensing data in Xilinguole of Inner Mongolia,we analyzed statistical relationships between the two datasets.The results showed that MODIS reflectance in visible light band positively correlated(P0.05)with the ground photographic digital number,in which the most significant correlation appeared between MODIS reflectance in 500 mspatial resolution and the ground photographic digital number.Nevertheless,TM/ETM+reflectance did not significantly correlate(P0.05)with the ground photographic digital number.The positive correlation between MODIS Normalized Difference Vegetation Index(NDVI)and relative greenness index from ground photography(G%)was obviously higher than those between other vegetation indices and greenness indices.Errors between phenological occurrence dates derived from remote sensing and ground photography data were mostly within 7days.In conclusion,the reliability of remote sensing phenology monitoring by means of ground photography was of crucial for selecting appropriate remote sensing data source and phenological monitoring index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".