Full metadata record
DC FieldValueLanguage
dc.contributor.authorMERONI MICHELEen_GB
dc.contributor.authorREMBOLD Felixen_GB
dc.contributor.authorURBANO FERDINANDOen_GB
dc.contributor.authorCSAK Gaboren_GB
dc.contributor.authorLEMOINE Guidoen_GB
dc.contributor.authorKERDILES Herveen_GB
dc.contributor.authorPEREZ HOYOS ANAen_GB
dc.identifier.otherEUR 28313 ENen_GB
dc.identifier.otherOP LB-NA-28313-EN-Nen_GB
dc.description.abstractAgriculture monitoring, and in particular food security, requires near real time information on crop growing conditions for early detection of possible production deficits. Anomaly maps and time profiles of remote sensing derived indicators related to crop and vegetation conditions can be accessed online thanks to a rapidly growing number of web based portals. However, timely and systematic global analysis and coherent interpretation of such information, as it is needed for example for the United Nation Sustainable Development Goal 2 related monitoring, remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide timely warning of production deficits in water-limited agricultural systems worldwide every month. The first step is fully automated and aims at classifying each sub-national administrative unit (Gaul 1 level, i.e. first sub-national level) into a number of possible warning levels, ranging from “none” to level 4++. Warnings are triggered only during the crop growing season, as derived from a remote sensing based phenology. The classification system takes into consideration the fraction of the agricultural area for each Gaul 1 unit that is affected by a severe anomaly of two rainfall-based indicators (the Standardized Precipitation Index computed at 1 and 3-month scale), one biophysical indicator (the anomaly of the cumulative Normalized Difference Vegetation Index from the start of the growing season), and the timing during the growing cycle at which the anomaly occurs. The level (i.e. severity) of the warning thus depends on: the timing, the nature and number of indicators for which an anomaly is detected, and the agricultural area affected. Maps and summary information are published on a web GIS. The second step, not described in detail in this manuscript, involves the verification of the automatic warnings by agricultural analysts to identify the countries (national level) with potentially critical conditions that are marked as “hot spots”. This report focusses on the technical description of the automatic warning classification scheme version 1.0.en_GB
dc.description.sponsorshipJRC.D.5-Food Securityen_GB
dc.publisherPublications Office of the European Unionen_GB
dc.titleThe warning classification scheme of ASAP – Anomaly hot Spots of Agricultural Productionen_GB
dc.typeEUR - Scientific and Technical Research Reportsen_GB
JRC Directorate:Sustainable Resources

Files in This Item:
File Description SizeFormat 
lb-na-28313-en-n .pdf1.59 MBAdobe PDFView/Open

Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.