Comparison and evaluation of the suitability of existing irrigation area products in the Yellow River Basin
Bibliographic record
Abstract
Accurate monitoring of irrigated farmland is crucial for water resource management and food production forecasting. While remote sensing has enhanced global irrigation monitoring, a systematic comparison of existing products is still needed. This study evaluates six irrigation area products—GFSAD, GMIA-Meier, GMIE, IAAA, IrriMap-CN, and Xiang—using the Yellow River Basin as a case study. Four types of in-situ measurement data— TPD-FIWEC, FSD, and both ASMD and MSMD—were analyzed, resulting in a validation dataset of 5382 points. Results based on area and spatial accuracy showed that GMIE performed best, with a relative area deviation (RAD) of 17.33 %, overall accuracy (OA) of 0.72, a consistency index (CI) of 0.65, and both omission and commission errors (OE and CE) below 0.40. Cross-comparison showed that only 22.62 % of irrigated pixels were identified by more than three products. The Comprehensive Evaluation Index (CEI) analysis revealed regional performance variations, particularly in areas such as LZ to TDG, the Mid-altitude region, and the Northern arid/semi-arid and Humid Climate regions, where several products performed well. This study provides a solid foundation for applying irrigation area products in water resource management and offers recommendations for future optimization.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".