Mapping drought severity impact on arboriculture systems over Tadla and lower Tassaout plains in Morocco using Sentinel-2 data and machine learning approaches
Bibliographic record
Abstract
Severe droughts have affected the irrigated regions of Tadla and Lower Tassaout, Morocco, since 2019, peaking in September 2021. This study integrates Sentinel-2 satellite imagery with machine learning algorithms (MLAs) to quantify drought impacts on fruit tree systems. Three predictor scenarios were tested: M1 (Sentinel-2 bands and indices), M2 (added historical vegetation indices), and M3 (incorporated phenological metrics). Tree-based MLAs performed best, with Random Forest (RF) and Gradient Tree Boost achieving 95.94% and 94.09% accuracy under M3. RF-based analysis identified significant crop losses: 2,121 ha of citrus orchards and 12,127 ha of olive groves, with 16,276 ha moderately affected. However, groundwater and spring irrigation preserved 5,298 ha of olive trees and 7,216 ha of citrus orchards but led to declining aquifer levels. These findings highlight remote sensing and MLAs’ role in assessing drought impacts and balancing agricultural resilience with water sustainability.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".