{"title":"VIIRS地表温度产品的地面验证与不确定度估算","authors":"Yuling Liu, Yunyue Yu, YU Peng, Heshun Wang","doi":"10.1109/IGARSS.2016.7730806","DOIUrl":null,"url":null,"abstract":"This study compares VIIRS LST with ground in-situ observations from Baseline Surface Radiation Network (BSRN) and Global Monitoring Division (GMD) baseline observatories. The validation results present a close agreement between satellite estimation and ground observations with the accuracy about -0.4 K and -0.7 K, precision of 2.1 and 1.8 over BSRN CAB site and GOB site, respectively. A precision of 2.2 is obtained over Summit station in Greenland. Cloud contamination and ground heterogeneity are found to have great impact on the product performance. In addition, a statistical method is applied to estimate LST uncertainty attributed to the sensor noise, surface type product imprecision and algorithm itself. The overall theoretical LST uncertainty is about 0.7 K.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Ground validation and uncertainty esitmation of VIIRS land surface temperature product\",\"authors\":\"Yuling Liu, Yunyue Yu, YU Peng, Heshun Wang\",\"doi\":\"10.1109/IGARSS.2016.7730806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study compares VIIRS LST with ground in-situ observations from Baseline Surface Radiation Network (BSRN) and Global Monitoring Division (GMD) baseline observatories. The validation results present a close agreement between satellite estimation and ground observations with the accuracy about -0.4 K and -0.7 K, precision of 2.1 and 1.8 over BSRN CAB site and GOB site, respectively. A precision of 2.2 is obtained over Summit station in Greenland. Cloud contamination and ground heterogeneity are found to have great impact on the product performance. In addition, a statistical method is applied to estimate LST uncertainty attributed to the sensor noise, surface type product imprecision and algorithm itself. The overall theoretical LST uncertainty is about 0.7 K.\",\"PeriodicalId\":179622,\"journal\":{\"name\":\"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2016.7730806\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2016.7730806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ground validation and uncertainty esitmation of VIIRS land surface temperature product
This study compares VIIRS LST with ground in-situ observations from Baseline Surface Radiation Network (BSRN) and Global Monitoring Division (GMD) baseline observatories. The validation results present a close agreement between satellite estimation and ground observations with the accuracy about -0.4 K and -0.7 K, precision of 2.1 and 1.8 over BSRN CAB site and GOB site, respectively. A precision of 2.2 is obtained over Summit station in Greenland. Cloud contamination and ground heterogeneity are found to have great impact on the product performance. In addition, a statistical method is applied to estimate LST uncertainty attributed to the sensor noise, surface type product imprecision and algorithm itself. The overall theoretical LST uncertainty is about 0.7 K.
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