Operational Remote Sensing System for Real-Time Agricultural Drought Monitoring and Early Warning in the Nigerian Sahel

Timely agricultural drought early warning is essential for enabling adaptive responses among smallholder farmers and government agencies in Nigeria's Sahel zone, where recurrent droughts cause food insecurity affecting millions. This study designs and validates an operational remote sensing system for real-time agricultural drought monitoring and early warning across Sokoto, Zamfara, Katsina, and Jigawa states. The system architecture integrates automated acquisition and processing of Sentinel-3 OLCI vegetation products, MODIS land surface temperature data, CHIRPS rainfall estimates, and Sentinel-1 SAR soil moisture products within a cloud computing environment. A machine learning ensemble model fusing the four data streams produces daily agricultural drought severity classifications at 1-kilometre resolution. The system is validated against a 10-year record of crop failure reports and agricultural drought events, achieving a drought detection probability of 0.89 and a false alarm rate of 0.12. Alert dissemination protocols are developed in partnership with state agricultural development programmes, extension services, and the National Emergency Management Agency. A cost-benefit analysis demonstrates that timely early warning reduces drought-related economic losses by an estimated 18 percent in validated case scenarios. Keywords: drought early warning, operational remote sensing, Nigerian Sahel, agricultural drought, ensemble model