Using Satellite‐Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas
Bibliographic record
Abstract
Abstract Normalized Difference Vegetation Index (NDVI) is widely used as an efficient indicator of vegetation cover. Here we assess the possibility of using NDVI as an indicator of groundwater storage. We used groundwater level (GWL) obtained from in situ groundwater observation wells (n > 15,000) in India in 2005–2013. Good correlation (r > 0.6) is observed between NDVI and GWL in natural vegetation‐covered areas, that is, forest lands, shrubs, and grasslands. We apply artificial neural network and support vector machine approaches to investigate the relationship between GWL and NDVI using both of the parameters as input. Artificial neural network‐ and support vector machine‐simulated GWL matches very well with observed GWL, particularly in naturally vegetated areas. Thus, we interpret that NDVI may be used as a suitable indicator of groundwater storage conditions in certain areas where the water table is shallow and the vegetation is natural and where in situ groundwater observations are not available.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".