Integrated Remote Sensing Analysis of snow cover and Topographic influences in Mountainous Regions: A Case study of Marivan County, Iran

Snow plays a vital role in the hydrological cycle, especially in mountainous regions, significantly affecting water resources, agriculture, and natural hazard mitigation. This study employs remote sensing data to analyze the spatiotemporal distribution of snow cover and its relationship with precipitation and topographic variables, including slope, aspect, and elevation in Marivan County, western Iran. Sentinel-2 and Landsat satellite imagery from snow cover seasons between 2021 to 2024, alongside several snow indices (NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS), were used to generate snow cover maps. Satellite-based precipitation datasets (CHIRPS and PERSIANN) were utilized to explore correlation between snow cover and precipitation patterns. Results indicated that the S3 and SWI indices provided the highest accuracy in snow detection, with Sentinel-2 imagery outperforming Landsat due to its finer spatial resolution. Topographic analysis revealed that regions with higher elevation, northern aspect and gentle slopes had the densest snow cover. Overall, this study highlights that effectiveness of intergrating optical remote sensing, precipitation data, and topographic information for accurate monitoring of snow cover in mountainous areas.Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.

Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.

Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.

Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.

Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.

Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.

Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.

Snow is an important element in the hydrological cycle, especially in mountainous areas, and it dramatically impacts water resources, agriculture, and natural hazard management. This study uses remote sensing data to investigate the spatiotemporal distribution of snow cover and analyze its relationship with precipitation and topographic parameters, including slope, slope direction, and elevation. For this reason, Sentinel-2 and Landsat satellite images from 2021 to 2024 and seasons with snow cover, as well as NDSI, NDSII, NDSInw, S3, SWI, and NBSI-MS indices were used to extract snow cover maps. CHIRPS and PERSIANN satellite precipitation data were also used to analyze precipitation patterns and their relationship with snow cover. The results showed that S3 and SWI indices had the highest accuracy in snow detection, and Sentinel-2 images performed more accurately than Landsat due to their better spatial resolution. The topographic data also showed that areas with high altitudes, gentle slopes, and northern directions have the highest snow cover density. Overall, this study demonstrated that integrating optical satellite data, precipitation, and topography can effectively monitor and analyse snow cover in mountainous areas.