Abstract:
The second United Nations Sustainable Development Goal (SDG2), zero hunger, aims to
improve the productivity, food security, nutrition, and sustainability of small-scale farmers. The
fall armyworm (FAW, Spodoptera frugiperda) has been devasting to smallholder farmer food security
since it spread to sub-Saharan Africa in 2016, who have suffered massive crop losses, particularly
maize, an important staple for basic sustenance. Since the FAW mainly devours green leaf biomass
during the maize vegetative growth stage, the implementation of remote sensing technologies offers
opportunities for monitoring the FAW. Here, we developed and tested a Sentinel 2 a+b satellite-based
monitoring algorithm based on optimized first-derivative NDVI time series analysis using Google
Earth Engine. For validation, we first employed the FAO Fall Armyworm Monitoring and Early
Warning System (FAMEWS) mobile app data from Kenya, and then subsequently conducted field
validation campaigns in Zimbabwe, Kenya, and Tanzania. Additionally, we directly observed loss of
green biomass during maize vegetative growth stages caused by the FAW, confirming the observed
signals of loss of the leaf area index (LAI) and the total green biomass (via the NDVI). Preliminary
analyses suggested that satellite monitoring of small-scale farmer fields at the regional level may
be possible with an NDVI first-derivative time series anomaly analysis using ESA Sentinel 2 a+b
(R2 = 0.81). Commercial nanosatellite constellations, such as PlanetScope, were also explored, which
may offer benefits from greater spatial resolution and return interval frequency. Due to other
confounding factors, such as clouds, intercropping, weeds, abiotic stresses, or even other biotic pests
(e.g., locusts), validation results were mixed. Still, maize biomass anomaly detection for monitoring
the FAW using satellite data could help confirm the presence of the FAW with the help of expanded
field-based monitoring through the FAO FAMEWS app.
