{"id":3855,"date":"2024-03-11T16:24:23","date_gmt":"2024-03-11T16:24:23","guid":{"rendered":"https:\/\/hygeos.com\/?p=3855"},"modified":"2024-03-11T16:24:23","modified_gmt":"2024-03-11T16:24:23","slug":"2023-best-paper-award","status":"publish","type":"post","link":"https:\/\/hygeos.com\/en\/2023-best-paper-award\/","title":{"rendered":"2023 Best Paper Award"},"content":{"rendered":"
<\/span><\/div><\/div><\/div><\/div><\/div><\/div>

2023 Best Paper Award<\/h1><\/div>

In the Copernicus Land Monitoring Service<\/a> (CLMS), HYGEOS coordinates the scientific activities of the consortium in charge of the operations of the Global Land Component for the “Vegetation and Energy” products. The Leaf Area Index<\/a> (LAI), the Fraction of Absorbed Photosynthetically Active Radiation<\/a> (FAPAR) by the vegetation and the Fraction of green vegetation Cover<\/a> (FCover) are three biophysical variables that were generated over the whole globe at 1km resolution from 1999 to June 2020 from the SPOT\/VEGETATION and the PROBA-V data. The version 2 of the products, so-called GEOV2, is presented in Verger et al. 2023<\/strong><\/em> which has been awarded as 2023 Best Paper<\/strong> by the Editor-in-Chief and the Publisher of the International Journal of Applied Earth Observation and Geoinformation.<\/p>\n<\/div><\/div><\/div>

\"Certificate<\/span><\/div><\/div><\/div>

The retrieval methodology, defined by CREAF<\/a>, is based on neural networks first trained with a combination of existing CYCLOPES and MODIS products to estimate LAI, FAPAR and FCover from daily top of Canopy reflectance. In a second step, temporal techniques are then applied to filter, smooth, fill gaps and get a composited value every 10 days. The quality of the GEOV2 products was assessed by EOLAB<\/a>. The results show that GEOV2 products keep a high consistency with the previous version 1 (GEOV1, Baret et al., 2013<\/strong><\/em>) with 90% of residuals within \u00b1 max(0.5, 20%) LAI and 80% within \u00b1 max(0.05, 10%) FAPAR \/ FCover. Furthermore, GEOV2 improves in terms of product completeness (<1% of missing data), temporal consistency, consistency across variables and accuracy.<\/p>\n

The details of the algorithm can be found in the Algorithm Theoretical Basis Document while the validation procedure and complete results are available in\u00a0 two quality assessment reports, one for the SPOT\/VEGETATION GEOV2 products and one for the PROBA-V GEOV2 products. The three documents are available in the Technical Library<\/a> while the GEOV2 products are accessible via the Portfolio<\/a> of the CLMS.<\/p>\n

From July 2020 onwards, the production of global LAI, FAPAR, FCover is ensured in the CLMS using the Sentinel-3 OLCI data at 300m resolution. The products are available in near-real-time every 10 days. The retrieval methodology is similar to GEOV2 approach and the products are consistent with the historical GEOV2 products, as demonstrated in Fuster et al., 2020<\/strong><\/em>.<\/p>\n

Verger, A., J. Sanchez-Zapero, M. Weiss, A. Descals, F. Camacho, R. Lacaze<\/a> and F. Baret. GEOV2: improved smoothed and gap filled time series of LAI, FAPAR and FCover 1km Copernicus Global Land products, International Journal of Applied Earth Observation and Geoinformation<\/em>, vol. 123, Sept. 2023, 103479. https:\/\/doi.org\/10.1016\/j.jag.2023.103479<\/a><\/p>\n

Baret, F., M. Weiss, R. Lacaze<\/a>, F. Camacho, H. Makmarra, P. Pacholcyzk and B. Smets. GEOV1: LAI and FAPAR essential climate varaibles and FCover global time series capitalizing over existing products. Part1: principles of development and production, Remote Senging of Environment<\/em>, vol. 137, October 2013, pages 299-309. https:\/\/doi.org\/10.1016\/j.rse.2012.12.027<\/a><\/p>\n

Fuster, B., J. S\u00e1nchez-Zapero, F. Camacho, V. Garc\u00eda-Santos, A. Verger, R. Lacaze<\/a>, M. Weiss, F. Baret and B. Smets. Quality Assessment of PROBA-V LAI, FAPAR and FCover Collection 300 m Products of Copernicus Global Land Service.\u00a0Remote Sensing<\/i>\u00a02020,\u00a012137<\/i>, 1017. https:\/\/doi.org\/10.3390\/rs12061017<\/a><\/p>\n<\/div><\/div><\/div><\/div>

<\/i>Go back<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

Work coordinated by HYGEOS awarded as 2023 Best Paper published in the International Journal of Applied Earth Observation and Geoinformation<\/p>\n","protected":false},"author":4,"featured_media":3491,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[],"class_list":["post-3855","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"yoast_head":"\n2023 Best Paper Award - Hygeos<\/title>\n<meta name=\"description\" content=\"Work coordinated by HYGEOS awarded as 2023 Best Paper published in the International Journal of Applied Earth Observation and Geoinformation\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/hygeos.com\/en\/2023-best-paper-award\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"2023 Best Paper Award - 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