Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world
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Title |
Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world
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Creator |
Defourny, Pierre
Bontemps, Sophie Bellemans, Nicolas Cara, Cosmin Dedieu, Gérard Guzzonato, Eric Hagolle, Olivier Inglada, Jordi Nicola, Laurentiu Rabaute, Thierry Savinaud, Mickael Udroiu, Cosmin Valero, Silvia Bégué, Agnès Dejoux, Jean-François El Harti, Abderrazak Ezzahar, Jamal Kussul, Nataliia Labbassi, Kamal Lebourgeois, Valentine Miao, Zhang Newby, Terrence Nyamugama, Adolph Salh, Norakhan Shelestov, Andrii Simonneaux, Vincent Sibiry Traoré, Pierre C. Traoré, Souleymane S Koet, Benjamin |
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Subject |
agriculture
monitoring learning crop management food security climate change geology |
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Description |
The convergence of new EO data flows, new methodological developments and cloud computing infrastructure calls for a paradigm shift in operational agriculture monitoring. The Copernicus Sentinel-2 mission providing a systematic 5-day revisit cycle and free data access opens a completely new avenue for near real-time crop specific monitoring at parcel level over large countries. This research investigated the feasibility to propose methods and to develop an open source system able to generate, at national scale, cloud-free composites, dynamic cropland masks, crop type maps and vegetation status indicators suitable for most cropping systems. The so-called Sen2-Agri system automatically ingests and processes Sentinel-2 and Landsat 8 time series in a seamless way to derive these four products, thanks to streamlined processes based on machine learning algorithms and quality controlled in situ data. It embeds a set of key principles proposed to address the new challenges arising from countrywide 10m resolution agriculture monitoring. The full-scale demonstration of this system for three entire countries (Ukraine, Mali, South Africa) and five local sites distributed across the world was a major challenge met successfully despite the availability of only one Sentinel-2 satellite in orbit. In situ data were collected for calibration and validation in a timely manner allowing the production of the four Sen2-Agri products over all the demonstration sites. The independent validation of the monthly cropland masks provided for most sites overall accuracy values higher than 90%, and already higher than 80% as early as the mid-season. The crop type maps depicting the 5 main crops for the considered study sites were also successfully validated: overall
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Date |
2019-02
2020-03-13T20:06:33Z 2020-03-13T20:06:33Z |
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Type |
Journal Article
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Identifier |
Defourny P, Bontemps S, Bellemans N, Cara C, Dedieu G, Guzzonato E, Hagolle O, Inglada J, Nicola L, Rabaute T, Savinaud M, Udroiu C, Valero S, Bégué A, Dejoux JF, El Harti A, Ezzahar J, Kussul N, Labbassi K, Lebourgeois V, Miao Z, Newby T, Nyamugama A, Salh N, Shelestov A, Simonneaux V, Sibiry Traore P, Traore S, Koet B. 2019. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote Sensing of Environment 221: 551-568.
0034-4257 https://hdl.handle.net/10568/107771 https://doi.org/10.1016/j.rse.2018.11.007 |
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Language |
en
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Rights |
CC-BY-4.0
Open Access |
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Format |
551-568
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Publisher |
Elsevier
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Source |
Remote Sensing of Environment
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