Enhanced winter wheat LAI retrieval from Sentinel-2: asoil-informed radiative transfer based approach
Bibliographic record
Abstract
Leaf Area Index (LAI) is a key trait related to several agronomic issues such as soil cover, plant health, crop productivity, biomass and yield estimation. Availability of high-resolution LAI information at large scale is crucial for monitoring and managing agricultural landscapes effectively [1], as it can help monitor growth conditions and adapt practices. However, its satellite-based assessment is confounded by several factors such as soil background, vegetation type and noise. Today, the retrieval of LAI through the inversion of a radiative transfer model (RTM) is state-of-the-art. Still, research investigating the performance of crop-type specific models compared to across-biome models such as the ESA’s Sentinel Application Platform (SNAP) and in-situ data is rare. In this research we propose to improve the combined leaf and canopy PROSAIL [2] RTM crop-specific reflectance simulations by integrating soil spectra into this model. We specifically sample Sentinel-2 spectra from fields over which we perform LAI retrieval. A neural network is trained to invert the RTM. To scale this strategy to larger areas (i.e. country scale) we exploit Sentinel-2 observations of bare soil and use clustering methods to generate a condensed soil dataset representing varying background conditions across space.We use Switzerland to test the approach, with in-situ measurements of winter wheat from 2022 and 2023 available for validation. We focus on Sentinel-2 imagery for its high temporal and spatial resoltuions. Preliminary results show that a model trained on a data generated with a Switzerland-wide soil dataset and constrained for winter wheat (CH-LAI-WW model) outperformed predictions (nRMSE: 0.180) obtained from a classic setup without the soil inclusion (nRMSE: 0.201). Furthermore, prediction errors were improved compared to the across-biome SNAP LAI processor (nRMSE: 0.268). The proposed methodology demonstrates a way to improve the crop- and biome-specific prediction of key traits and consequently to improve the reliability for agricultural monitoring and management applications.[1] B. Brisco, R. Brown, T. Hirose, H. McNairn, and K. Staenz, “Precision agriculture and the roleof remote sensing: A review,” Canadian Journal of Remote Sensing, vol. 24:3, pp. 315–327, 1998[2] S. Jacquemoud, W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-Tejada, G. P. Asner, C. François, and S. L. Ustin, “PROSPECT+SAIL models: A review of use for vegetation characterization,” Remote Sensing of Environment, vol. 113, pp. S56–S66, Sept. 2009.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".