Estimation of sugarcane biomass from Sentinel-2 leaf area index using an improved SAFY model (SAFY-Sugar)
Bibliographic record
Abstract
Assimilating crop biophysical traits (e.g., Leaf area index, LAI) derived from remote sensing data into a crop growth model provides an effective way for monitoring spatiotemporal variability of crop biomass and yield. However, traditional complex crop growth models generally require extensive input parameters and computation resources, limiting their applicability for large-area estimation using earth observation data. The Simple Algorithm for Yield Estimation model (SAFY), a semi-physical crop growth model grounded in light use efficiency theory has been widely adopted for satellite-based biomass estimation in major field crops. Despite its utility, SAFY cannot directly simulate sugarcane stalk biomass, a critical metric for sugarcane yield assessment. To address this, we developed SAFY-Sugar, a revised SAFY incorporating a temperature-driven stalk biomass module that partitions daily above-ground biomass into stalk biomass. Multi-temporal LAI (S2-LAI) was first inverted from vegetation indices of Sentinel-2 satellite using a semi-empirical model calibrated with the 250 m GLASS LAI product as reference. The estimated S2-LAI achieved an overall accuracy of 0.50 m 2 /m 2 in RMSE across selected vegetation indices. Two data assimilation strategies to assimilate the S2-LAI into the SAFY or SAFY-Sugar model for above-ground and stalk biomass estimation in fields were tested (1) independently optimizing SAFY and SAFY-Sugar parameters with S2-LAI alone, and (2) pre-optimizing the stalk module using independent farm measurements before assimilation. SAFY employed a fixed biomass allocation coefficient for stalk biomass estimation. Under the first Strategy, SAFY-Sugar demonstrated large improvements in stalk biomass estimation (R 2 = 0.94, nRMSE = 26.09 %) compared to SAFY (R 2 = 0.92, nRMSE = 32.73 %). The second strategy further enhanced SAFY-Sugar’s accuracy (R 2 = 0.98, nRMSE = 14.34 %). For regional application in Chongzuo City (2020 – 2021) using the second strategy, SAFY-Sugar captured spatial yield variability, consistent with the government statistics (nRMSE = 6.61 %). By integrating satellite data assimilation, SAFY-Sugar provides a robust framework for monitoring sugarcane productivity across scales, advancing precision agriculture in sugarcane systems.
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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 | 0.000 |
| 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) | 0.001 | 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".