Drone-measured canopy spectra can be a complementary predictor of belowground arbuscular mycorrhizal fungi variations in urban community gardens

Abstract

Arbuscular mycorrhizal fungi (AMF) form essential symbioses with plant roots, improving nutrient uptake, plant health, and soil function. However, assessing AMF abundance in urban ecosystems remains challenging. Drone-based multispectral imaging can estimate canopy functional traits, raising the question of whether variation in aboveground canopy metrics can serve as an ecological indicator of belowground AMF abundance. We sampled five community gardens over two growing seasons, combining quadrat-level plant surveys, soil sampling for AMF abundance, Unmanned Aerial Vehicle (UAV) multispectral imagery, and point clouds. From the UAV, we derived vegetation indices, spectral diversity and texture metrics, and from the point clouds we extracted canopy height metrics. We related these traits to log AMF abundance using LASSO cross-validated regression and piecewise structural equation modelling (SEM). Our analyses showed that drone-derived canopy traits captured part of the aboveground variation associated with belowground AMF abundance, particularly through canopy greenness and image texture patterns. Higher AMF abundance was associated with greener and more vertically heterogeneous canopies. UAV-derived canopy texture features explained up to ∼38% of the variance in AMF abundance. Piecewise SEM indicated that AMF abundance was associated with a non-linear climate response, represented by the quadratic climate term (β = 0.25) and a positive vegetation-index pathway (β = 0.21). The AMF abundance SEM model had a marginal R 2 of 0.62, suggesting that seasonal climatic conditions and canopy greenness jointly structured AMF variation. Overall, UAV-derived canopy traits represent a scalable ecological indicator of relative AMF abundance in urban gardens, supporting non-destructive assessment of relative AMF variation and spatial targeting of field sampling. Establishing this link provides a baseline for developing stronger urban ecological indicators by integrating UAV data with soil measurements, garden management, and higher-resolution remote-sensing approaches.