Evaluating the effectiveness of remote sensing-based vegetation indices in estimating the spatio-temporal distribution of Nitrogen rates
Bibliographic record
Abstract
Evaluating the effectiveness of remote sensing-based vegetation indices in estimating the spatio-temporal distribution of Nitrogen ratesOne of the main objectives of precision agriculture is to optimize nitrogen fertilizer application management. This study aimed to assess the efficacy of different vegetation indices derived from satellite data in estimating soil ammonium and nitrate values during the corn growing season. To achieve this, multi-temporal Sentinel-2 images and ground data including ammonium and soil nitrate values measured at specific ground stations throughout the corn growing season for Hunter field, Canada, were utilized. Firstly, various vegetation indices including NDVI, EVI, MSAVI, ARVI, GNDVI, and OSAVI were calculated for different dates throughout the crop growing season. Subsequently, the Pearson correlation between these vegetation indices and temporal variations in soil ammonium and nitrate values during the growing season was examined. Moreover, the relationship between vegetation indices at the crop growth peak and the amount of fertilizer applied to the soil during planting was investigated. The findings indicated that the average correlation coefficients between total soil nitrate and ammonium values throughout the growing season and the NDVI, EVI, MSAVI, ARVI, GNDVI, and OSAVI indices were -0.67, -0.72, -0.69, -0.68, -0.73, and -0.70, respectively. Furthermore, the average correlation coefficients between these indices at the growth peak and the cumulative ammonium and nitrate applied at planting were 0.60, 0.60, 0.64, 0.60, 0.68, and 0.64, respectively. The correlation coefficient and root mean square error (RMSE) between the measured and modeled sum of ammonium and nitrate, based on the six vegetation indices in a multivariate form, were 0.89 and 17.3 mg, respectively.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".