{"title":"Sentinel-2-MSI光学数据在保加利亚山区积雪监测中的应用","authors":"Andrey Stoyanov, Daniela Avetisyan","doi":"10.1117/12.2679774","DOIUrl":null,"url":null,"abstract":"The present study aims to monitor the Snow Cover Extent (SCE) of the mountainous region of Bulgaria (13905 km2), located 1000m above sea level, for eight years. Information is important for calculation of Snow Water Equivalent (SWE), hydrological runoff modeling, forecasting, and assessing flood events. Global Warming and Climate Change and their impacts, such as a constant increase in recorded high-temperature levels, frequent droughts, water scarcity in the summers, and less-snow winters, have a significant effect on agriculture, hydrology, forests, and ecology in Bulgaria. The present research uses the available cloudless optical data of Sentinel-2-MSI for snow cover monitoring concerning the decrease in snow distribution during the last decade. Sentinel-2 satellite imagery, from October to May, for the period between 2016 and 2023, was generated and exported from Google Earth Engine (GEE). Normalized Differential Snow Index (NDSI) and Snow Water Index (SWI) were calculated, and the resulting output indices rasters were post-processed and inspected additionally to obtain thresholding classifications, masking out the areas covered by shadows (topographic), water bodies, forests, etc., and snow cover area distribution. The results obtained in the study can be used and integrated for climate change observations and research at the local and regional levels.","PeriodicalId":222517,"journal":{"name":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV","volume":"163 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of optical data from Sentinel-2-MSI for snow cover monitoring on the territory of the mountainous region of Bulgaria\",\"authors\":\"Andrey Stoyanov, Daniela Avetisyan\",\"doi\":\"10.1117/12.2679774\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present study aims to monitor the Snow Cover Extent (SCE) of the mountainous region of Bulgaria (13905 km2), located 1000m above sea level, for eight years. Information is important for calculation of Snow Water Equivalent (SWE), hydrological runoff modeling, forecasting, and assessing flood events. Global Warming and Climate Change and their impacts, such as a constant increase in recorded high-temperature levels, frequent droughts, water scarcity in the summers, and less-snow winters, have a significant effect on agriculture, hydrology, forests, and ecology in Bulgaria. The present research uses the available cloudless optical data of Sentinel-2-MSI for snow cover monitoring concerning the decrease in snow distribution during the last decade. Sentinel-2 satellite imagery, from October to May, for the period between 2016 and 2023, was generated and exported from Google Earth Engine (GEE). Normalized Differential Snow Index (NDSI) and Snow Water Index (SWI) were calculated, and the resulting output indices rasters were post-processed and inspected additionally to obtain thresholding classifications, masking out the areas covered by shadows (topographic), water bodies, forests, etc., and snow cover area distribution. The results obtained in the study can be used and integrated for climate change observations and research at the local and regional levels.\",\"PeriodicalId\":222517,\"journal\":{\"name\":\"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV\",\"volume\":\"163 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/10.1117/12.2679774\",\"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/10.1117/12.2679774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of optical data from Sentinel-2-MSI for snow cover monitoring on the territory of the mountainous region of Bulgaria
The present study aims to monitor the Snow Cover Extent (SCE) of the mountainous region of Bulgaria (13905 km2), located 1000m above sea level, for eight years. Information is important for calculation of Snow Water Equivalent (SWE), hydrological runoff modeling, forecasting, and assessing flood events. Global Warming and Climate Change and their impacts, such as a constant increase in recorded high-temperature levels, frequent droughts, water scarcity in the summers, and less-snow winters, have a significant effect on agriculture, hydrology, forests, and ecology in Bulgaria. The present research uses the available cloudless optical data of Sentinel-2-MSI for snow cover monitoring concerning the decrease in snow distribution during the last decade. Sentinel-2 satellite imagery, from October to May, for the period between 2016 and 2023, was generated and exported from Google Earth Engine (GEE). Normalized Differential Snow Index (NDSI) and Snow Water Index (SWI) were calculated, and the resulting output indices rasters were post-processed and inspected additionally to obtain thresholding classifications, masking out the areas covered by shadows (topographic), water bodies, forests, etc., and snow cover area distribution. The results obtained in the study can be used and integrated for climate change observations and research at the local and regional levels.