Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data
Bibliographic record
Abstract
Abstract The interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah‐MP‐Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) observations to predict sensible ( H ) and latent (LE) heat fluxes, gross primary productivity (GPP), etc. Such joint assimilation is demonstrated to be effective in constraining the model state variables (i.e., leaf biomass and SM) and optimizing key crop‐model parameters (i.e., specific leaf area [SLA], and maximum rate of carboxylation, Vcmax). The performance of the MLDAS is evaluated against observations at two AmeriFlux cropland sites, revealing good an agreement with the observed H , LE, and GPP. When using optimized model parameters (SLA and Vcmax) and jointly assimilating LAI, SM, and SIF observations, the MLDAS produces 34.28%, 26.90%, and 51.82% lower root mean square deviations for daily H , LE, and GPP estimates compared with the Noah‐MP‐Crop open loop simulation. Our findings also indicate that the H and LE predictions are more sensitive to SM measurements, while the GPP simulations are more affected by LAI and SIF observations. The results indicate that performances of physical models can be greatly improved by assimilating multi‐source observations within MLDAS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".