Remote Sensing of Mountain Snow from Space: Developing Accurate Snow Products for Efficient Water Resource Management in Morocco’s Atlas Mountains
Bibliographic record
Abstract
In semi-arid regions of the Mediterranean, snowmelt and precipitation are vital water sources for downstream communities. Here, snow-covered mountain peaks serve as natural water reservoirs, playing a crucial role in regulating river flow and replenishing groundwater. This research leverages remote sensing to compensate for the lack of ground-based hydroclimatic data, focusing on the latest version of the MODIS snow cover product (version 6, V6). The study aims to refine the Normalized Difference Snow Index (NDSI) threshold and develop localized models for fractional snow cover (FSC) estimation tailored to the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 scenes across six different regions in the Atlas Mountains were used to adjust the NDSI threshold and develop FSC models. Moreover, 8419 MOD10A1 and 7561 MYD10A1 images covering the period from March 2000 to June 2023 were processed to improve cloud filtering and generate a high-precision daily snow cover product for the region. Significant improvements were achieved in reducing cloud-covered pixels from 25.7% to 0.4%. Two NDSI MODIS threshold selection schemes were tested: the standard global threshold of 0.4 and a locally optimized threshold of 0.2. The local threshold demonstrated superior accuracy, significantly reducing snow cover estimation errors compared with the global threshold (0.4) for both Terra and Aqua MODIS images. The newly developed FSC models demonstrate high accuracy, displaying high correlation coefficients (average of 0.84) and low error measures when comparing MODIS-derived FSCs with high-resolution Sentinel-2 data. The improved daily snow cover product was compared with high-resolution snow maps obtained from Sentinel-2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of -0.17%. This research concludes that the improved daily snow cover product offers a robust understanding of the spatio-temporal dynamics of snow extent. These advancements offer considerable potential improvements to modelling snowmelt contribution to the water balance, supporting efficient water resource management in the southern Mediterranean region.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".