{"title":"全球尺度下不同算法的融雪检测","authors":"M. Takala, J. Pulliainen","doi":"10.1109/MICRAD.2008.4579496","DOIUrl":null,"url":null,"abstract":"Knowing the snow melt date is important for hydrology, weather forecasting and climatological modes. Carbon dioxide balance in the atmosphere is related to the growing season and thus snow melt. The snow melt date is also important for other ecological processes as well. Spaceborne radiometers are well suited for global monitoring of environmental parameters because they can cover a large area in a day and the measurements do not depend on sunlight or weather. Four different algorithms to estimate the snow melt date have been tested and a nearly 30 year dataset of snow melt date in Eurasia has been derived using brightness temperatures from SMMR and SSM/I instruments. The results are validated using INTAS SSCONE snow depth data. The results show that the algorithm estimates the snow melt date accurately. Maps of the snow melt area derived.","PeriodicalId":193521,"journal":{"name":"2008 Microwave Radiometry and Remote Sensing of the Environment","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Detection of snow melt using different algorithms in global scale\",\"authors\":\"M. Takala, J. Pulliainen\",\"doi\":\"10.1109/MICRAD.2008.4579496\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Knowing the snow melt date is important for hydrology, weather forecasting and climatological modes. Carbon dioxide balance in the atmosphere is related to the growing season and thus snow melt. The snow melt date is also important for other ecological processes as well. Spaceborne radiometers are well suited for global monitoring of environmental parameters because they can cover a large area in a day and the measurements do not depend on sunlight or weather. Four different algorithms to estimate the snow melt date have been tested and a nearly 30 year dataset of snow melt date in Eurasia has been derived using brightness temperatures from SMMR and SSM/I instruments. The results are validated using INTAS SSCONE snow depth data. The results show that the algorithm estimates the snow melt date accurately. Maps of the snow melt area derived.\",\"PeriodicalId\":193521,\"journal\":{\"name\":\"2008 Microwave Radiometry and Remote Sensing of the Environment\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Microwave Radiometry and Remote Sensing of the Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MICRAD.2008.4579496\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Microwave Radiometry and Remote Sensing of the Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MICRAD.2008.4579496","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of snow melt using different algorithms in global scale
Knowing the snow melt date is important for hydrology, weather forecasting and climatological modes. Carbon dioxide balance in the atmosphere is related to the growing season and thus snow melt. The snow melt date is also important for other ecological processes as well. Spaceborne radiometers are well suited for global monitoring of environmental parameters because they can cover a large area in a day and the measurements do not depend on sunlight or weather. Four different algorithms to estimate the snow melt date have been tested and a nearly 30 year dataset of snow melt date in Eurasia has been derived using brightness temperatures from SMMR and SSM/I instruments. The results are validated using INTAS SSCONE snow depth data. The results show that the algorithm estimates the snow melt date accurately. Maps of the snow melt area derived.