Identifying Food Insecurity in Africa Using Remote Sensing Datasets

The Famine Early Warning Systems Network (FEWS NET) monitors critical environmental variables that impact food production in developing countries, including over 30 in Africa. However, there is a notable lack of consistent quantitative data accurately capturing crop yields or the number of people facing food insecurity. The recently implemented Integrated Food Security Phase Classification (IPC) protocol seeks to address this issue through a set of protocols that define the severity of food security ranging from "None/Minimal" to "Humanitarian Catastrophe/Famine". The IPC framework considers both the severity of the hazard and the vulnerability of the population, as well as the four dimensions of food security (availability, access, utilization and stability). This framework is applied at a fairly fine sub-national level and its consistent application across national borders provides a large dataset to work with. This presentation reports on an ongoing project to examine correlations between a number of geophysical variables and IPC condition. These variables are rainfall, reference evapotranspiration (ETo) and soil moisture (SM), along with combinations of these variables, such as the Standardized Precipitation Evapotranspiration Index (SPEI). We use the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset as the rainfall product and an experimental ETo dataset generated using NASA's MERRA-2 atmospheric forcings. Measures of SM use simulations coming from the FEWSNET Land Data Assimilation System (FLDAS). The variables will be compared based on predicative accuracy of IPC and how that accuracy varies across regions and calendar year. The goal is to identify the optimal geophysical predictor of agricultural drought and food insecurity. The results of this research could prioritize the datasets used in identifying and quantifying food insecurity in Africa, and may allow for accurate and frequent updates of the food security conditions.