Watching from Space Agricultural Drought Worldwide – using the FAO-ASIS (Agricultural Stress Index System)

The Agricultural Stress Index System (ASIS) is based on 10-day (dekadal) satellite data, of vegetation and land surface temperatures, from the METOP-AVHRR sensor at 1 km resolution. Data for Country-level ASIS is freely available for download from FAO FTP. Time Series Database from 1984.


Country-Level ASIS is been implemented at the regional level in Central America and at Country-level in Mexico, Nicaragua, Panama, Bolivia, Chile, Paraguay, Vietnam, Pakistan and Philippines

FAO has developed ASIS, to support its global food security monitoring work by detecting agricultural areas with a high likelihood of water stress (dry-spells and drought) based on Earth Observation data and information. The Flemish Institute for Technological Research (VITO) is supporting the scientific and technical development of ASIS while the Joint Research Centre of European Commission (JRC) and the University of Twente in the Netherlands are members of the steering committee of the development of ASIS.

ASIS is based on the Vegetation Health Index (VHI), derived from NDVI and developed by Kogan (1994, 1995 and 1997). This index was successfully applied in many different environmental conditions around the globe, including Asia, Africa, Europe, North America and South America. VHI can detect drought conditions at any time of the year. For agriculture, however, we are only interested in the period most sensitive for crop growth (temporal integration), so the analysis is performed only between the start (SOS) and end (EOS) of the crop season and restricted to crop areas. ASIS assess the severity (intensity, duration and spatial extent) of the agricultural drought and express the final results at the administrative level given the possibility to compare it with the agricultural statistics of the country.

After the successful completion of the global system, the team is concentrating on the development of a standalone ASIS to support regional and national early warning systems. In the standalone version, adapting analysis parameters to each region or country´s specific agricultural conditions will allow for more accurate results. The final index could be used as a trigger for activating drought mitigation activities in countries, or for the implementation of index-based crop insurance. 
Innovative impact: Availability, simplicity, free of charge data, good research literature and citation, minimum requirements of inputs are main criteria. Sustainability guaranteed by the automatization of the analysis.  

Availability, simplicity, free of charge data, good research literature and citation, and minimum requirements of inputs, are deemed to be the main criteria. Sustainability (will be) guaranteed by the automatization of the analysis.

ROJAS, O. VRIELING, A. and REMBOLD, F. 2011. Assessing drought probability for agricultural areas in Africa with remote sensing.    Remote Sensing of Environment 115 (2011) 343-352 pp.

REMBOLD, F., ATZBERGER, C., SAVIN, I. and ROJAS, O. 2013. Using Low Resolution Satellite   Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing ISSN 2072-4292 Remote Sensing 2013, 5, 1704-1733; doi: 10.3390/rs5041704.

ROJAS, O. and AHMED, S. 2013.  Feasibility of using the FAO Agricultural stress index system (ASIS) as a remote sensing-based index for crop insurance.  In: The challenges of index-based insurance for food security in developing countries.  Ed. Rene Gommes and Francois Kayitakire, European Commission, Joint Research Centre. 246-253 pp.

ROJAS, O., LI, Y. and CUMANI, R. 2014. Understanding the drought impact of El Niño on the global agricultural areas: An assessment using FAO’s Agricultural Stress Index (ASI).  Environmental and Natural Resources Management Series No. 22, FAO. 42 p.

VAN HOOLST, R., EERENS, H., HAESEN, D., ROYER, A., BYDEKERKE, L., ROJAS, O., LI, Y. & RACIONZER, P.   (2016) FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring, International Journal of Remote Sensing, 37:2, 418-439, DOI: 10.1080/01431161.2015.1126378