Remote Sensing of Groundwater: Current Capabilities and Future Directions
Bibliographic record
Abstract
Abstract Globally, groundwater represents a critical natural resource that is affected by changes in natural supply and renewal, as well as by increasing human demand and consumption. However, despite its critical role, groundwater is difficult to accurately quantify as it is beneath the Earth surface. Here, we review several state‐of‐the‐art remote sensing techniques useful for local‐ to global‐scale groundwater monitoring and assessment, including proxies for groundwater extraction. These include inferring changes in subsurface water from mass changes using gravitational measurements, and analyzing changes in the Earth surface height using Interferometric Synthetic Aperture Radar, Light Detection and Ranging, Airborne Electromagnetic Systems, and satellite altimetry. Remote sensing information is often used in tandem with ground‐based observations such as hydraulic head in wells, Global Navigational Satellite System monitoring, and numerical modeling to complement the space‐based approaches. In the future, fusing different remote sensing techniques capable of operating in various environments will yield additional insight on the state and rate of use for groundwater across the globe.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".