JRC ASAP - Anomaly hot Spots of Agricultural Production, recent developments
Abstract
Monitoring food security requires near real-time (NRT) information on crop growing conditions for early detection of possible production deficits. The ASAP system (Anomaly hot Spots of Agricultural Production) is a two-step analysis early system aimed at providing timely warning of production deficits in water-limited agricultural systems worldwide. 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 and depending on the nature and number of anomalies taken into consideration. The second step 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". The automatic warning classification system component of ASAP has been recently subjected to a number of upgrades. First, the NDVI from METOP was replaced by the use of NRT filtered NDVI MODIS-based product. Filtering allows to provide quality NRT data, adapting temporal smoothing method to cope adapted to cope with the unbalanced availability of data before and after the most recent data points. Second, we started the production of a Water Satisfaction Index (WSI) for both crops and rangelands. The Water Satisfaction Index (WSI) is an indicator of crop (or rangeland) performances based on the availability of water to the crop during the growing season. It uses a rainfall and evapotranspiration driven water balance accounting scheme to estimate water available to the plant. Third, by matching remote sensing derived phenology over crop areas with country level crop calendars (from FAO, USDA, IRRI) we produce specific crop calendars at the ASAP unit level. Fourth, we extended the functionalities of the high resolution viewer, a GEE based web interface that allows to visualize Sentinels (1 and 2) and Landsat imagery, plot temporal profiles and perform basic anomaly operation (e.g. current year NDVI anomaly with respect to a reference year).
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMGC43I1635M
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCESDE: 1640 Remote sensing;
- GLOBAL CHANGEDE: 6309 Decision making under uncertainty;
- POLICY SCIENCES