Synergetic inversion of leaf chlorophyll content and leaf area index from Sentinel-2 data using artificial neural networks trained with a radiative transfer model
Bibliographic record
Abstract
Leaf area index (LAI) and leaf chlorophyll content (LCC) are two key vegetation traits affecting vegetation growth and function. They have often been retrieved individually from multi-spectral remote sensing data by ignoring their mutual influence on spectral signals. In this study, we developed a machine learning algorithm for synergetically retrieving both traits at the same time through training the algorithm using simulations of a radiative transfer model PROSAIL. The algorithm determines LAI based on visible, red-edge, near-infrared and shortwave infrared bands, while it infers LCC mostly from visible and red-edge bands. In this way, the mutual influences of LAI and LCC on visible and red-edge bands are considered in the algorithm. The algorithm is applied to a rice field over multiple growing seasons (2017-2018, 2024) using Sentinel-2 data. It is found that synergetically retrieved LAI and LCC are more accurate (R2=0.82 and 0.85, RMSE=0.83 and 5.00 μg/cm², respectively) than individually retrieved LAI (R2=0.54, RMSE=1.49) and LCC (R2=0.85, RMSE=9.35 μg/cm²). Our results suggest that this synergetic retrieval principle and methodology may be utilized to improve regional and global LAI and LCC products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".