Monitoring small reservoirs storage from satellite remote sensing in inaccessible areas
Bibliographic record
Abstract
Abstract. In river basins with water storage facilities, the availability of regularly-updated information on reservoir level and capacity is of paramount importance for the effective management of those systems. Yet, for the vast majority of reservoirs around the world, storage levels are either not measured or not readily available due to financial, political or legal considerations. This paper proposes a novel approach using Landsat imagery and Digital Elevation Models (DEM) to retrieve information on storage variations in inaccessible regions. Unlike existing approaches, the method does not require any in situ measurement and is appropriate to monitor small, and often undocumented, irrigation reservoirs. It consists of three recovery steps: (i) a 2D dynamic classification of Landsat spectral bands information to quantify the surface area of water, (ii) a statistical correction of DEM data to characterize the topography of each reservoir and (iii) a 3D reconstruction algorithm to correct for clouds and Landsat 7 Scan Line Corrector failure. The method is applied to quantify reservoir storage in the Yarmouk basin in Southern Syria, where ground monitoring is impeded by the ongoing civil war. It is validated against available in situ measurements in neighboring Jordanian reservoirs. Coefficients of determination range from 0.69 to 0.84, and the average relative error from 3 % to 35 % for storage estimations on six Jordanian reservoirs with maximal water surface areas ranging from 0.59 km2 to 3.79 km2.
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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 | 0.000 |
| 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 | 0.001 |
| 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".