GMIE-100: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method
Bibliographic record
Abstract
Abstract. Irrigation stands as the primary sector of human water consumption and plays a pivotal role in enhancing crop yields and mitigating drought effects. The precise distribution of irrigation is crucial for effective water resource management and the assessment of food security. However, the existing global irrigated cropland map is characterized by a coarse resolution, typically around 10 kilometers, and is often not regularly updated. In our study, we present a robust methodology that leverages irrigation performance during drought stress as an indicator of crop productivity and water consumption to identify global irrigated cropland. Within each irrigation mapping zone (IMZ), we identified the dry months occurring during the growing season from 2017 to 2019 or the driest months from 2010 to 2019. To delineate irrigated cropland, we utilized collected samples to calculate normalized difference vegetation index (NDVI) thresholds for the dry months of 2017 to 2019 and the NDVI deviation from the ten-year average for the driest month. By combining the results with the higher accuracy between these two methods, we generated the Global Maximum Irrigation Extent dataset at 100-meter resolution (GMIE-100), achieving an overall accuracy of 83.6 %. GMIE-100 reveals that the maximum extent of irrigated cropland encompasses 403.17 million hectares, accounting for 23.4 % of the global cropland. Concentrated in fertile plains and regions adjacent to major rivers, the largest irrigated cropland area is found in India, followed by China, the United States, and Pakistan, ranking 2nd to 4th, respectively. Importantly, the spatial resolution of GMIE-100, at 100 meters, surpasses that of the dominant irrigation map, offering more detailed information essential for supporting estimates of agricultural water use and regional food security assessments. Furthermore, with the help of the DL method, the global central pivot irrigation system (CPIS) was identified using Pivot-Net. We found that there are 11.5 million hectares of CPIS, accounting for about 2.9 % of total irrigated cropland. In Namibia, the US, Saudi Arabia, South Africa, Canada, and Zambia, the CPIS proportion was larger than 10 %. To our best knowledge, this is the first effort to identify irrigation methods globally. The GMIE-100 dataset containing both or irrigated extent and CPIS distribution is accessible on Harvard Dataverse at: https://doi.org/10.7910/DVN/HKBAQQ (Tian et al., 2023a).
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".