Python version of Simplified Level 2 Prototype Processor for Retrieving Canopy Biophysical Variables from Sentinel 2 Multispectral Instrument Data
Bibliographic record
Abstract
The Copernicus Sentinel-2 mission is designed to provide data that can be used to globally map widely used vegetation biophysical variables. Currently, estimates of vegetation biophysical variables are not produced operationally by the Sentinel-2 ground segment. Instead, a retrieval algorithm called Simplified Level 2 Prototype Processor (SL2P) has been defined by the European Space Agency. SL2P is a backpropagation neural network trained using a database of globally representative canopy conditions populated using canopy radiative transfer model simulations. SL2P had been implemented within the Canada Centre for Remote Sensing LEAF-Toolbox that relies on Google Earth Engine. This document describes a PYTHON implementation of SL2P (SL2P-PYTHON) that provides identical outputs as the LEFA-Toolbox implementation given the same input Sentiel-2 image.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.021 |
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".