Fate and transport of nutrients in karst streams remains a pressing research need. The objective of this study was to couple high -frequency and remote imagery data to quantify spatial and temporal variability of aquatic vegetation and determine the associated impacts on in -stream nitrate removal in karst headwater streams. The study was conducted in a spring -fed karst stream in the Inner -Bluegrass region of Central Kentucky, USA. Ten Unmanned Aircraft System (UAS) campaigns were coupled with three -years of high frequency in situ data of water quality. Automated segmentation and image classification analysis was performed on UAS imagery, and spatial variability in floating aquatic macrophytes was quantified. Results demonstrated the utility of UAS images to capture spatiotemporal variability of floating aquatic macrophytes (duckweed) with high accuracy, but the UAS analysis poorly predicted algal biomass dynamics due to spectral interferences in the water column and shading by duckweed. Further, in situ water quality data was used to estimate stream metabolism using the Bayesian Single Station Estimator (BASE) model, with results demonstrating primary production and ecosystem respiration was driven by algal biomass. Stream metabolism displayed a shorter season (March -August) relative to other agricultural streams in the region (March -October), likely reflecting the hydraulic structures in the channel which reduce flow velocities in the stream reach and promote transition to duckweed cover during the summer. The impact of aquatic vegetation dynamics on nitrate was assessed using diurnal analysis and regression with dissolved oxygen. Results for March through November showed an average daily diurnal variation of 0.25 mgN/L, which was more than two -fold greater than winter months. During the growing season, maxima generally occurred between 6 a.m.-2 p.m., and minima occurred between 2 p.m. -12 a.m., but varied depending on prominent aquatic vegetation and dissolved oxygen dynamics. These results suggest shifting N removal mechanisms as aquatic vegetation changes throughout the year. Implications for numerical modeling of fluvial nitrate fluxes are illustrated through the evaluation of a previously developed AI model simulating nitrate concentrations at the watershed outlet. The findings highlight the importance of integrating novel datasets, such as those presented in this study, to evaluate and improve predictions of numerical models.
