{"title":"利用Sentinel-2卫星数据评估保加利亚山区高坡暴露对积雪分布的影响","authors":"Daniela Avetisyan, Andrey Stoyanov","doi":"10.1117/12.2679770","DOIUrl":null,"url":null,"abstract":"Snow cover is among the most important features of the Earth's surface and a crucial element of the cryosphere that affects the global energy balance, water, and carbon cycles. Accurate monitoring of this land surface component is of particular significance as snowmelt provides between 50%–80% of the annual runoff in the temperate (boreal) regions and significantly impacts the hydrological balance during the warm season. Limited reserves of soil moisture during the winter period can lead to all types of droughts, including green-water drought, which is expressed by reduced water storage in soil and vegetation. Green-water drought causes a variable effect across landscape components, on the functions and ecosystem services (ES) they provide. The present study aims to track the snow cover dynamics in the transitional seasons of the year when the snow cover is most unstable and to differentiate its territorial distribution depending on elevation and slope exposure. The study area covers the mountainous territories of Bulgaria and the seasons from 2016 to 2022. To achieve the aim of the study, we used Sentinel-2 images and calculated the Snow Water Index (SWI). SWI uses spectral characteristics of the visible, shortwave infrared (SWIR), and near-infrared (NIR) bands to distinguish snow and ice pixels from other pixels, including water bodies which is crucial for the accurate monitoring of snow cover dynamics. The obtained results were validated using VHR images for pre-selected test areas.","PeriodicalId":222517,"journal":{"name":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of elevation and slope exposure impact on snow cover distribution in the mountainous region in Bulgaria using Sentinel-2 satellite data\",\"authors\":\"Daniela Avetisyan, Andrey Stoyanov\",\"doi\":\"10.1117/12.2679770\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Snow cover is among the most important features of the Earth's surface and a crucial element of the cryosphere that affects the global energy balance, water, and carbon cycles. Accurate monitoring of this land surface component is of particular significance as snowmelt provides between 50%–80% of the annual runoff in the temperate (boreal) regions and significantly impacts the hydrological balance during the warm season. Limited reserves of soil moisture during the winter period can lead to all types of droughts, including green-water drought, which is expressed by reduced water storage in soil and vegetation. Green-water drought causes a variable effect across landscape components, on the functions and ecosystem services (ES) they provide. The present study aims to track the snow cover dynamics in the transitional seasons of the year when the snow cover is most unstable and to differentiate its territorial distribution depending on elevation and slope exposure. The study area covers the mountainous territories of Bulgaria and the seasons from 2016 to 2022. To achieve the aim of the study, we used Sentinel-2 images and calculated the Snow Water Index (SWI). SWI uses spectral characteristics of the visible, shortwave infrared (SWIR), and near-infrared (NIR) bands to distinguish snow and ice pixels from other pixels, including water bodies which is crucial for the accurate monitoring of snow cover dynamics. The obtained results were validated using VHR images for pre-selected test areas.\",\"PeriodicalId\":222517,\"journal\":{\"name\":\"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2679770\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2679770","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessment of elevation and slope exposure impact on snow cover distribution in the mountainous region in Bulgaria using Sentinel-2 satellite data
Snow cover is among the most important features of the Earth's surface and a crucial element of the cryosphere that affects the global energy balance, water, and carbon cycles. Accurate monitoring of this land surface component is of particular significance as snowmelt provides between 50%–80% of the annual runoff in the temperate (boreal) regions and significantly impacts the hydrological balance during the warm season. Limited reserves of soil moisture during the winter period can lead to all types of droughts, including green-water drought, which is expressed by reduced water storage in soil and vegetation. Green-water drought causes a variable effect across landscape components, on the functions and ecosystem services (ES) they provide. The present study aims to track the snow cover dynamics in the transitional seasons of the year when the snow cover is most unstable and to differentiate its territorial distribution depending on elevation and slope exposure. The study area covers the mountainous territories of Bulgaria and the seasons from 2016 to 2022. To achieve the aim of the study, we used Sentinel-2 images and calculated the Snow Water Index (SWI). SWI uses spectral characteristics of the visible, shortwave infrared (SWIR), and near-infrared (NIR) bands to distinguish snow and ice pixels from other pixels, including water bodies which is crucial for the accurate monitoring of snow cover dynamics. The obtained results were validated using VHR images for pre-selected test areas.