Improving the PROSPECT Model to Consider Anisotropic Scattering of Leaf Internal Materials and Its Use for Retrieving Leaf Biomass in Fresh Leaves
Bibliographic record
Abstract
The PROSPECT model has been widely used to estimate leaf biochemical constituents, but retrieval of leaf mass per area (LMA) in fresh leaves has proved to be difficult due to the predominant water absorption in the infrared spectral region. At wavelengths where water absorption is low, both LMA absorption and light scattering are relatively high. Therefore, the uncertainty in scattering simulation at these wavelengths will lead to a relatively large error in LMA estimation. In this paper, we introduce a wavelength-independent factor to represent the first-order effect of anisotropic scattering in the elementary layer in the modified model PROSPECT-g, aiming at appropriately simulating leaf optical properties in spectral regions with high scattering and thus reducing the uncertainty in LMA estimation. In order to avoid introducing a new variable to be retrieved in model inversion, this factor is an intermediate variable derived from measured near infrared region spectral data and other existing model parameters. Results show that about 30%-40% of the tested samples are well simulated using PROSPECT-5, while for the rest of the samples simulation is greatly improved with PROSPECT-g. Leaf reflectance and transmittance reconstructions using PROSPECT-g are improved, especially at wavelengths with high scattering such as 750-1400 and 1500-1850 nm. LMA retrieval is significantly improved, with the average root-mean-square error decreasing from 38.7 (PROSPECT-5) to 16.6 g/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (PROSPECT-g) for 628 leaves after considering anisotropic scattering in the elementary layer. Improvements are particularly noticeable for leaves with extremely high LMA contents.
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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 | 0.000 | 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 | 0.001 |
| 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 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".