PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data
PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data
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PyTroll：一个开源、社区驱动的 Python 框架，用于处理地球观测卫星数据
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
H. Thorsteinsson
中科院分区：
文献类型：
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作者：
Martin Raspaud;D. Hoese;A. Dybbroe;Panu Lahtinen;A. Devasthale;Mikhail Itkin;Ulrich Hamann;Lars Ørum Rasmussen;Esben Stigård Nielsen;Thomas Leppelt;Alexander Maul;Christian Kliche;H. Thorsteinsson
PyTroll（http：pytroll.org）是一套开源的易于使用的Python包，用于促进地球观测（EO）卫星数据的处理和有效共享。PyTroll软件旨在用于24/7实时操作以及研究和开发。PyTroll源于提供一个能够快速响应新用户需求和新数据源的弹性和敏捷平台的需求。PyTroll是开源的，它促进了国际合作，这对于卫星信息可用性的快速增长至关重要。PyTroll软件的开发是由用户驱动的，在过去的八年里，它从丹麦和瑞典国家气象部门之间的合作努力发展成为一个拥有活跃贡献者的全球社区。PyTroll至少在丹麦、挪威、瑞典、芬兰、德国、瑞士、意大利、爱沙尼亚和拉脱维亚的国家气象服务中得到了实际应用。然而，鉴于其简单性，对用户资源的需求最小，以及社区驱动的方法，它也鼓励和促进了EO数据用于单个应用程序。虽然PyTroll最初是为了满足大气遥感社区的需求而开发的，但它对陆地和海洋应用以及水文学同样有用。本文提供了PyTroll的概述，并通过示例展示了一些核心包的功能。
PyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages.
影响因子：
11.4
作者：
M. Stengel;Stefan Stapelberg;O. Sus;C. Schlundt;C. Poulsen;G. Thomas;M. Christensen;C. C. Henken-C.;R. Preusker;J. Fischer;A. Devasthale;U. Willén;K. Karlsson;G. Mcgarragh;S. Proud;A. Povey;R. Grainger;J. F. Meirink;A. Feofilov;R. Bennartz;J. Bojanowski;R. Hollmann
通讯作者：
M. Stengel;Stefan Stapelberg;O. Sus;C. Schlundt;C. Poulsen;G. Thomas;M. Christensen;C. C. Henken-C.;R. Preusker;J. Fischer;A. Devasthale;U. Willén;K. Karlsson;G. Mcgarragh;S. Proud;A. Povey;R. Grainger;J. F. Meirink;A. Feofilov;R. Bennartz;J. Bojanowski;R. Hollmann