RETRIEVAL OF LEAF AREA INDEX AND LEAF CHLOROPHYLL CONTENT FROM HYPERSPECTRAL DATA USING DEEP LEARNING NETWORKS
Bibliographic record
Abstract
Abstract. This study aimed to exploit the use of deep learning networks in the retrieval of the biophysical and biochemical parameters of vegetation canopies. Convolutional Neural Network (CNN), network with only fully connected layers, referred as dense network (DNN), and Autoencoder (AE) were investigated to retrieve leaf area index (LAI) and leaf chlorophyll content. Hyperspectral data simulated by the coupled PROSPECT and SAIL model were used for training and validation. The real CASI hyperspectral data in 50 spectral channels ranging from 522.4 nm to 894.2 nm collected over three agricultural crop fields during the growing season of 2001 were used, together with the in-situ measured LAI and leaf chlorophyll content, as independent test set. Occlusion analysis was also employed to determine the important spectral bands at which reflectance made more contributions to the retrieval with a CNN and interpret the latent variables of the AE. Satisfactory results from these deep learning networks were obtained, compared with ground truth. The DNN with the input of the vegetation indices sensitive to LAI and leaf chlorophyll content (MTVI2 and TCARI/OSAVI) generated the best results with R2 of 0.86 for LAI and 0.55 for leaf chlorophyll content.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".