The Validation of Snow Cover Product Over High Mountain Asia

Xu Su, Lingmei Jiang, Gongxue Wang, Jian Wang
{"title":"The Validation of Snow Cover Product Over High Mountain Asia","authors":"Xu Su, Lingmei Jiang, Gongxue Wang, Jian Wang","doi":"10.1109/IGARSS39084.2020.9324316","DOIUrl":null,"url":null,"abstract":"Many algorithms and products for snow cover have been developed. Then a unified set of “ground truth” data is important to validate snow cover products. In this study, Landsat-8/OLI data processed by linear unmixing algorithm was determined as “ground truth” to validate the moderate resolution snow products. In order to evaluate the cloud removing effect of the daily fractional snow cover (FSC) dataset of MODIS over High Asia, we use the MOD10A1 FSC product which is calculated by recommended equations as the before cloud removing data, then the Landsat-8/OLI FSC was used to validate both of the MODIS data. The results show that when the percentage of cloud pixels is less than 10%, the binary accuracy can reach 0.85 or more, the mean absolute error is less than 0.25, and the root mean square error is less than 0.35. These results suggest that the product has high credibility, despite there is still a small amount of cloud in the product.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"201 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS39084.2020.9324316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Many algorithms and products for snow cover have been developed. Then a unified set of “ground truth” data is important to validate snow cover products. In this study, Landsat-8/OLI data processed by linear unmixing algorithm was determined as “ground truth” to validate the moderate resolution snow products. In order to evaluate the cloud removing effect of the daily fractional snow cover (FSC) dataset of MODIS over High Asia, we use the MOD10A1 FSC product which is calculated by recommended equations as the before cloud removing data, then the Landsat-8/OLI FSC was used to validate both of the MODIS data. The results show that when the percentage of cloud pixels is less than 10%, the binary accuracy can reach 0.85 or more, the mean absolute error is less than 0.25, and the root mean square error is less than 0.35. These results suggest that the product has high credibility, despite there is still a small amount of cloud in the product.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
目前已经开发了许多积雪的算法和产品。因此,一套统一的“地面真值”数据对于验证积雪产品非常重要。本研究采用线性解混算法处理的Landsat-8/OLI数据作为“地面真值”,对中分辨率雪产品进行验证。为了评价高亚洲地区MODIS日分数积雪(FSC)数据集的去云效果,我们使用推荐公式计算的MOD10A1 FSC产品作为去云前数据,然后使用Landsat-8/OLI FSC对这两个MODIS数据进行验证。结果表明,当云像素百分比小于10%时,二值精度可达到0.85以上,平均绝对误差小于0.25,均方根误差小于0.35。这些结果表明,尽管产品中仍有少量的云,但产品具有较高的可信度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Retrieval of Solar-Induced Chlorophyll Fluorescence at Red Spectral Peak with Tropomi on Sentinel-5 Precursor Mapping the Rate of Carbon Mineralization in Oman Ophiolites Using Sentinel-1 InSAR Time Series Characterization of Biomass Burning Aerosols During the 2019 Fire Event: Singapore and Kuching Cities Exploitation of Earth Observations: OGC Contributions to GRSS Earth Science Informatics A Pseudospectral Time-Domain Simulator for Large-Scale Half-Space Electromagnetic Scattering and Radar Sounding Applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1