Empowering water utilities: Crafting an end-user-friendly reliability ranking for evaluating satellite remote sensing advances through literature insights
Bibliographic record
Abstract
ABSTRACT Satellite remote sensing provides extensive data for water management, enabling the measurement of hydro-meteorological and environmental variables. It aids in assessing trends, and hydrological conditions and guiding appropriate management actions. Satellite remote sensing serves as a cost-effective supplement to ground-based monitoring infrastructure. Over the past decade, satellite Earth observation technologies have advanced significantly, offering new opportunities for water utilities and agencies. These developments include improved satellite capabilities, enhanced data access, private sector involvement, and advancements in data processing and analytics. However, an end-user-friendly reliability ranking tool for evaluating numerous satellite remote sensing options is needed for operational decision-making purposes. Here, we summarise recent trends and the literature on satellite remote sensing for water management, evaluating its capabilities and available tools, focusing on the routine but essential operation of utilities. A novel assessment of satellite potential implementation to water-related applications using an end-user-friendly reliability ranking process is presented. The study focuses on selected application areas, including catchment monitoring, water demand estimation, flood monitoring, water quality monitoring, farm dam monitoring, urbanization trends, drought forecasting, fire spotting, and post-fire water quality impacts. This paper outlines the operational advantages/limitations of satellite remote sensing and provides recommendations for its adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".