EASE-DGGS:a hybrid discrete global grid system for Earth sciences

作     者：Jeffery A.Thompson Mary J.Brodzik Kevin A.T.Silverstein Mason A.Hurley Nathan L.Carlson 

作者机构：Minnesota Supercomputing InstituteUniversity of MinnesotaMinneapolisMNUSA GEMS Informatics InitiativeUniversity of MinnesotaMinneapolisMNUSA National Snow and Ice Data CenterCooperative Institute for Research in Environmental SciencesUniversity of ColoradoBoulderCOUSA 

出 版 物：《Big Earth Data》 (地球大数据（英文）)

年 卷 期：2022年第6卷第3期

页      面：340-357页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：CFANS InnoVation Economy University of Minnesota, UMN College of Agriculture Science and Natural Resources, Institute of Agriculture and Natural Resources, CASNR Minnesota Supercomputing Institute, University of Minnesota, MSI 

主　　题：Discrete global grid systems DGGS EASE Grid coordinate reference systems 

摘      要：Although we live in an era of unprecedented quantities and access to data,deriving actionable information from raw data is a hard *** observation systems(EOS)have experienced rapid growth and uptake in recent decades,and the rate at which we obtain remotely sensed images is *** significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster,less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative *** global grid systems(DGGS)have been proposed as one potential solution that addresses the challenge of geospatial data integration and ***,we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data *** describe the extensions as well as present implementation as an application programming interface(API),which forms part of the University of Minnesota’s GEMS(Genetic x Environment x Management x Socioeconomic)Informatics Center’s API portfolio.