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Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with sentinel-2

机译:在全球12个分布站点上建立数据集,以支持使用sentinel-2开发农业监控应用程序

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摘要

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel¬2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel¬2 for Agriculture” system. (Résumé d'auteur)
机译:根据地球观测数据发展更好的农业监测能力对于加强粮食生产信息和市场透明度至关重要。从分辨率(10–20米),重访频率(五天)和覆盖范围(全球)的角度看,前哨2号任务具有最佳的区域到全球农业监测能力。在这种背景下,欧洲航天局于2014年启动了“农业Sentinel 2”项目,该项目旨在通过开发相关产品的开放源代码处理链,准备开发用于农业监测的Sentinel-2数据。该项目生成了前所未有的数据集,该数据集由“类似于Sentinel-2”的时间序列和2013年在12个全球分布站点上获得的现场数据组成。地球观测时间序列主要建立在SPOT4(Take 5)数据集上,该数据集专门用于模拟Sentinel-2。他们还包括Landsat 8和RapidEye影像作为补充数据源。将图像预处理到2A级,并评估所得时间序列的质量。现场管理人员共享有关农田,作物类型和生物物理变量的原地数据,其中大多数属于“作物评估和监测联合试验”网络。该数据集允许跨站点测试和比较将成为未来“农业前哨2”系统核心的方法。 (Résuméd'auteur)

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