Use of unmanned aerial vehicle-derived multi-spectral data for the early detection of multi-temporal maize leaf equivalent water thickness and fuel moisture content for the improved resilience of smallholder maize farming

Abstract

Maize water stress from rainfall variability is a key challenge in producing rain-fed maize farming, especially in water-scarce regions, such as southern Africa. Hence, quantifying maize foliar water content variations throughout the phenological stages is valuable in detecting smallholder maize moisture stress and supporting agricultural decision-making. The emergence of unmanned aerial vehicles (UAVs) equipped with multispectral sensors offers a unique opportunity for robust and rapid monitoring of maize foliar water content and stress. The combination of nearreal-time spatially explicit information acquired using UAV imagery with physiological indicators, such as equivalent water thickness (EWT) and fuel moisture content (FMC), provides viable options for detecting and quantifying maize foliar water content and moisture stress in smallholder farming systems. Therefore, we evaluated the utility of UAV-based multispectral datasets and random forest regression in quantifying maize EWT and FMC throughout the maize phenological growth cycle. Results showed that EWT and FMC could be determined using the near-infrared and red-edge wavelengths to a relative root mean square error of 2.27% and 1%, respectively. Specifically, the spectra acquired during the early reproductive growth stages between silking and milk stages demonstrated a high sensitivity to the variation in maize moisture content. These findings serve as a fundamental step toward creating an early maize moisture stress detection and warning system and contribute to climate change adaptation and resilience of smallholder maize farming.