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Exploiting big earth data from space – first experiences with the timescan processing chain

机译：利用来自太空的大地球数据–时间扫描处理链的初步经验

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The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring. They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data ( 250 m). However, images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions. Hence, this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of (private or public) computing clusters. The TimeScan framework covers solutions for data access to arbitrary mission archives (with different data provisioning policies) and data ingestion into a processing environment (EO2Data module), mission specific pre-processing of multi-temporal data collections (Data2TimeS module), and the generation of a final TimeScan baseline product (TimeS2Stats module) providing a spectrally and temporally harmonized representation of the observed surfaces. Technically, a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest (i.e. up to hundreds of TBs or even PBs of data) into a higher level product with significantly reduced volume. In first test, the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat–8 scenes acquired from 2013 to 2015, a global data-set of 25,550 Envisat ASAR radar images collected 2010–2012, and regional Sentinel–1 and Sentinel–2 collections of 1500 images acquired from 2014 to 2016. The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics.
机译：欧洲哨兵任务和最新一代的美国Landsat卫星为全球环境监测提供了新的机会。他们在时空覆盖范围内以10至60 m的空间分辨率获取图像，而以前只能基于较低分辨率的地球观测数据（250 m）才能实现。但是，这些现代化任务收集的图像迅速增加了数据量，而标准工作站和软件解决方案将无法再处理这些数据。因此，此贡献引入了TimeScan概念，该概念将预先存在的工具组合到示例性模块化管道中，以灵活，可扩展地处理各种（私有或公共）计算集群上的海量图像数据集合。 TimeScan框架涵盖了以下方面的解决方案：对任意任务档案的数据访问（具有不同的数据供应策略）以及将数据提取到处理环境中（EO2Data模块），多时间数据集合的任务特定预处理（Data2TimeS模块）以及生成最终TimeScan基线产品（TimeS2Stats模块）的示意图，提供了观察到的表面的光谱和时间上的协调表示。从技术上讲，TimeScan层将可用于感兴趣的区域和时间段的数百或数千个单个图像的信息内容（即多达数百个TB甚至PB的数据）聚集到一个更高级别的产品中，并且体积明显减小。在第一次测试中，TimeScan管道已用于处理从2013年到2015年采集的452,799张多光谱Landsat-8场景的全球覆盖，2010-2012年收集的25,550张Envisat ASAR雷达图像的全球数据集以及Sentinel-1和从2014年到2016年采集了Sentinel–2的1500张图像。所得的TimeScan产品已成功用于与大规模监测环境过程及其时间动态有关的各种研究中。

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