Remote Sensing of Mountain Snow from Space: Developing Accurate Snow Products for Efficient Water Resource Management in Morocco’s Atlas Mountains

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.