Crop Yield Estimation Using NDVI: A Comparison of Various NDVI Metrics
Bibliographic record
Abstract
The objective of this study is to examine and compare multiple Normalized Difference Vegetation Index (NDVI) metrics for estimating crop yield. There are several terms used to describe NDVI metrics including: process methods, aggregation techniques, phenological indices, and aggregation metrics. The various NDVI metrics included in this study are maximum NDVI (MaxNDVI), integrated-NDVI (INDVI), Minimum NDVI (MinNDVI), relative annual range of NDVI (RREL), days to maximum NDVI (DTM), and days from maximum NDVI (DFM). NDVI data was accessed from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) over a 13-year period (2006-2018). County level corn yield data was from the United States Department of Agriculture (USDA) National Agriculture Statistics Service (NASS) database. Temperature data was gathered from the Puget Sound Regional Synthesis Model (PRISM). Regression analysis was conducted to examine the performance of various NDVI metrics for estimating crop yield. The results indicate that MaxNDVI is best able to estimate county level corn yields. This research aids in understanding the ability of the various NDVI metrics to estimate crop yield. This information will assist those designing satellite-based crop yield forecasting and index-based crop insurance models.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".