Mapping irrigated agriculture in fragmented landscapes of sub-Saharan Africa: An examination of algorithm and composite length effectiveness
Bibliographic record
Abstract
Accurately identifying irrigated areas is crucial for sustainable development, food security, and effective land and water resource management. However, incomplete or outdated national estimates of irrigated areas underestimate the extent of it, particularly among smallholders. This study aimed to address this issue by investigating the impact of different algorithms and composite lengths on predicting irrigated agriculture in four study areas in Mozambique. The study found that the choice of algorithm and composite length notably impacted the accuracy of identifying irrigation. Shorter composite lengths, such as 2-monthly or 3-monthly composites, were more effective in identifying irrigation in fragmented and dynamic landscapes, while longer composite lengths were better suited to stable classes and homogeneous landscapes. Artificial neural networks, support vector machines, and random forests were all effective algorithms for classifying irrigation. However, the study emphasised the importance of considering hotspots and agreement maps when identifying irrigation. Agreement maps combine the classification results of multiple models, providing better insights into the core areas of irrigated agriculture and allowing for a better understanding of irrigation dynamics and policy decision-making, particularly among smallholder systems. This research provides valuable insights for those working on remote sensing-based irrigation mapping and monitoring in sub-Saharan Africa, focusing on identifying smallholder irrigation with greater certainty.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".