An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models

Abstract

In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 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 enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.

Keywords

• MODIS
• Fractional snow cover
• NDSI threshold Optimization
• Mediterranean
• Machine Learning
• Random Forest