How Well Do EO-Based Food Security Warning Systems for Food Security Agree? Comparison of NDVI-Based Vegetation Anomaly Maps in West Africa

The GEOGLAM crop monitor for early warning is based on the integration of the crop conditions assessments produced by regional systems. Discrepancies between these assessments can occur and are generally attributed to the interpretation of the vegetation and climate data. The premise of this article is that other sources of discrepancy related to the data themselves must also be considered. We conducted a comparative experiment of the growth vegetation anomalies routinely produced by four operational crop monitoring systems in West Africa [FEWSNET, GIEWS, ASAP, VAM] for the 2010-2020 period. We collected a set of normalized differences vegetation index-based indicators (% mean, % median, and Z-score) and proposed original methods to analyze and compare the spatio-temporal variations of these indices using Hovmoller representation, statistics, and spatial analysis. To facilitate systems comparison, a classification scheme based on the percentile rank values of anomaly indicators was applied to produce 3-class alarm maps (negative, absence, and positive anomalies). Results show that, on an annual basis, the per-pixel similarity is relatively low between the four systems [24.5%-34.1%], and that VAM and ASAP are the most similar (70%). The reasons of the products discrepancies come mainly from different preprocessing methods, especially the choice of the reference period used to calculate the anomaly. The negative alarm agreement classes show no eco-climatic zoning influence, but negative alarms hot-spots were locally observed. The negative alarm agreement maps can be a useful tool for early warning as they synthesize the information provided by the different systems, with a confidence level.

Automated summary

The GEOGLAM crop monitor for early warning is based on the integration of regional systems' crop condition assessments. Discrepancies between these assessments can be attributed to interpretation of vegetation and climate data, and this article argues that discrepancies related to the data must also be considered. An experiment comparing four operational crop monitoring systems in West Africa was conducted, revealing relatively low per-pixel similarity and indicating that preprocessing methods, especially the choice of reference period, are the main reasons for discrepancies. Negative alarm agreement maps can serve as a useful tool for early warning with synthesized information and confidence levels.