The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis
Bibliographic record
Abstract
AbstractVegetation fractional cover, which defines the amount of vegetation on the surface of the land, is a key parameter in land surface models. Based on a geometric-optical model in combination with a linear spectral mixture analysis, the retrieval of shrub fractional cover in Wushen Banner of Inner Mongolia in the Mu Us Sandland using HJ-1B multispectral images is discussed. We acquired the surface reflectance based on geometric correction and atmospheric correction of the HJ-1B image. Then we assumed that the reflectance of a mixed pixel is a simple linear combination of two components, namely illuminated background and illuminated canopy, and further calculated the areal proportion of the illuminated background within each pixel based on the linear spectral mixture analysis. Then, combined with the measured shrub structural parameters, the shrub fractional cover was estimated using an inverted geometric-optical model. Finally, the result was validated through the measured shrub cover of 13 sample plots and a comparison study was done with the NDVI regression method and simple linear spectral mixture analysis. The R 2 of the three methods are 0.898, 0.614, and 0.659, with corresponding root-mean-squared errors of 0.136, 0.154, and 0.175, which indicate the reliability of the combined method.Le couvert végétal fractionnaire qui définit la proportion de végétation sur la surface de la terre, est un paramètre important de modèles pour la surface terrestre. Basé sur un modèle géométrique et optique en combinaison avec une analyse du mélange spectral linéaire; dans le présent document nous discutons sur les recherches du couvert fractionnaire en arbuste dans Wushen Banner en Mongolie intérieure à Mu Us Sandland en utilisant les images multi spectrales HJ-1B. Premièrement, nous acquérons la réflectance de la surface basée sur la correction géométrique et la correction atmosphérique de l'image HJ-1B. Ensuite, nous supposons que la réflectance d'un pixel mixte est une simple combinaison linéaire de deux composantes à savoir de l'arrière-plan illuminé et la voûte illuminé, et de plus calculons la proportion de la superficie de l'arrière-plan illuminé intérieur de chaque pixel basé sur l'analyse du mélange spectrale linéaire. Après cela, en combinaison avec les paramètres structurels d'arbustes mesurés, le couvert fractionnaire en arbuste sera estimé en utilisant un modèle géométrique et optique inversé. Enfin, le résultat est validé à partir des mesures du couvert en arbuste effectuées sur 13 échantillons de terrain et une étude de comparaison est effectuée avec la méthode de régression NDVI et l'analyse simple du mélange spectral linéaire. Le R 2 de ces trois méthodes est de 0,898, 0,614 et 0,659 avec la racine de l'erreur quadratique moyenne correspondant de 0,136, 0,154 et 0,175 indiquant la fiabilité de la méthode combinée.[Traduit par la Rédaction] AcknowledgementsThis paper was supported by the Natural Science Foundation of China (Grant Nos. 41171330 and 40871173). Thanks to the China Centre for Resources Satellite Data and Application (CRESDA) for providing the HJ-1B data. We are grateful to Professor Arthur Cracknell and his assistant Ms. Pauline Lovell for their fruitful work in revising the paper. The authors also thank all people that have given help for the paper.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".