Integrating Gravimetry Data With Thermal Infra‐Red Data From Satellites to Improve Efficiency of Operational Irrigation Advisory in South Asia
Bibliographic record
Abstract
Abstract The rapid decline of groundwater resources in South Asia due to excessive irrigation during dry season requires awareness of optimal on‐field water requirements. Such information is currently provided on farmer cellphones through an operational Irrigation Advisory System (IAS). To minimize the cost of sending such irrigation advisory texts to farmers while maximizing impact of IAS on groundwater sustainability, we integrated Gravity Recovery and Climate Experiment (GRACE) data with Landsat Thermal Infrared (TIR) Imagery to target regions in greater need of the IAS service. We demonstrated the concept of an improved IAS over eight irrigation districts of the Ganges and Indus basins. The Surface Energy Balance Algorithm for Land (SEBAL) was used to monitor on‐field water consumption (evapotranspiration‐ET) over cropped areas using Landsat TIR data at plot‐scale spatial resolution. Comparison of SEBAL ET with crop water demand from Penman‐Monteith (FAO56) technique quantified the extent of overirrigation at the plot scale and provided a tangible pathway to microtarget the IAS service only to farmers with the largest groundwater use footprint, thereby improving the impact of the IAS service further. Our results suggested that an operational IAS that integrates GRACE and Landsat TIR data on average can save about 85% (80 million m 3 ) of groundwater per dry season for irrigation districts of Northern India and 87% (or 150 million m 3 ) per year for irrigation districts of Eastern Pakistan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".