Quantifying plant biomass in ecosystems is an essential basis for many ecological questions. A direct estimation of macrophyte biomass proves to be difficult for the large number of kettle holes in Pleistocene landscapes, due to their strong heterogeneities. This study compared a classical non-destructive method for biomass estimation based on allometric relationships built from a larger selection of plant trait variables with regressions only based on plant height and cover of four macrophyte species typical for kettle holes in northeast Germany (i.e. Carex riparia, Phalaris arundinacea, Persicaria amphibia, Rorippa amphibia). Their predictive power and potential applicability for remotely sensed biomass estimation using unmanned aerial systems (UAS) was evaluated. The usage of several in-situ measured plant traits of individual plants revealed best macrophyte biomass predictions (R² = 0.84 to 0.95). Yet, using only plant height and cover to predict biomass still showed a moderate to good correlation (R² = 0.52 to 0.81). Using P. arundinacea as an example, we demonstrated for one kettle hole the potential of calculating plant patch height from digital surface models (DSM) derived from UAS RGB images processed with structure-from-motion (SfM) photogrammetry. After applying a site-specific correction factor for discrepancies between reference field measurements of plant heights and DSM derived plant heights, we were able to calculate P. arundinacea biomass of the entire kettle hole based on allometric relationships using plant height and cover. Finally, we briefly discuss how further methodological development can improve UAS-derived plant height as predictor variable for biomass estimation.