Title: Testing VGT data continuity between SPOT and PROBA-V missions for operational yield forecasting in North African countries
Authors: MERONI MICHELEFASBENDER DOMINIQUEBALAGHI RaidDALI MustaphaHAFANI MiriamHAYTHEM IsmaelHOOKER JOSEPH DOMINICLAHLOU MouanisLOPEZ LOZANO RAULMAHYOU HamidMONCEF Ben MoussaSGHAIER NabilWAFA TalhaouiLEO Olivier
Publisher: Publications Office of the European Union
Publication Year: 2015
JRC N°: JRC96277
ISBN: 978-92-79-49237-2
ISSN: 1831-9424
Other Identifiers: EUR 27327
OP LB-NA-27327-EN-N
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC96277
DOI: 10.2788/920806
Type: EUR - Scientific and Technical Research Reports
Abstract: The SPOT-VEGETATION mission operationally provided 15 years of remote sensing indicators of vegetation status. The mission reached its end-of-life in May 2014 and was timely replaced by the PROBA-V mission, aiming to ensure, among other objectives, the seamless continuity of provision of VGT-like products, including Normalized Difference Vegetation Index (NDVI). Exploiting the period of overlap when both instruments were functioning (November 2013 –May 2014), this study compared NDVI data provided by the SPOT-VGT and the PROBA-V instruments from the point of view of the user interested in operational crop monitoring and yield forecasting. The comparison is performed for the three North Africa country of Morocco, Algeria and Tunisia. All such countries, through different agencies and institutional arrangements, had in place an operational crop monitoring system that was based on 10-day composites of SPOT-VGT NDVI. In view of the operational crop monitoring season of 2015, when they will have to decide whether to continue their business-as-usual activities with the PROBA-V data (instead of SPOT-VGT), this study analysed the impact of the use of this new data source on the information being operationally derived and analysed for crop monitoring and yield forecasting: anomaly maps, temporal profiles and cereal yield figures (for barley, durum and soft wheat) estimated using semi-empirical regression models.
JRC Directorate:Sustainable Resources

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