Agriculture 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 set 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 in the Warning Explorer available at http://mars.jrc.ec.europa.eu/asap
The second step, not described in this manuscript, involves the verification of the automatic warnings by agricultural analysts to identify the countries with potentially critical conditions at the national level that are marked as “hot spots”.
This report focuses on the technical description of the automatic warning classification scheme version 1.1.