Monitoring growth and ground cover of turfgrass germplasm using a UAV-mounted multispectral sensor in a ground-based system

Efficient selection of turfgrass germplasm with persistent green coverage requires evaluation across multiple observation periods rather than at a single time point. This study developed an integrated framework for temporal evaluation and multi-criteria selection of 526 turfgrass accessions monitored monthly from August to December 2021 during one growing season in a single experimental environment. Green cover percentage (GCP) quantified visible canopy coverage, whereas the normalized difference vegetation index (NDVI) provided complementary spectral information on canopy greenness. Four multi-month summary traits GCP area under the curve (AUC), late-season GCP, NDVI AUC, and late-season NDVI formed a common trait space examined through complementary analytical steps: K-means clustering characterized broad temporal-performance groups, Pareto screening retained non-dominated trade-offs, and an a priori equal-weight composite score ranked the resulting candidates. The three-cluster solution separated a high-performance group of 97 accessions, and the global Pareto set comprised 14 accessions. The ranking identified five promising candidates, sensitivity analysis indicated a stable core of four, whereas the fifth position depended on moderate changes in trait weights. Within the limits of a single season and environment, the results show that integrating multi-month traits, clustering, Pareto screening, composite ranking, and sensitivity analysis provides a transparent framework for prioritizing turfgrass germplasm for further evaluation.