Method comparison of indirect assessments of understory leaf area index (LAIu): A case study across the extended network of ICOS forest ecosystem sites in Europe

Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and modelling energy exchange between atmosphere biosphere. While LAI forest overstory can be accurately assessed over large spatial scales via remote sensing, understory (LAIu) still largely ignored in studies ecosystem due to fact that it often too complex destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made retrieve because dense with high are hindering retrieval algorithms produce meaningful estimates LAI. Consequently, constitutes poorly investigated research realm impeding properly account its contribution absorption capacity stands. This study aims compare three conceptually different indirect methodologies LAIu diverse panel types distributed across Europe. For this we carried out near-to-surface measurements reflectance spectra as well digital surface photography extended network Integrated Carbon Observation System (ICOS) sites. was exploiting empirical relationship vegetation cover light (Beer-Lambert- Bouguer law) utilizing proposed relationships two prominent indices: normalized difference (NDVI) simple ratio (SR). Retrievals from methods were significantly correlated each (r = 0.63–0.99, RMSE 0.53–0.72), but exhibited also significant bias depending on scale. The NDVI based approach most likely overestimates at productive sites when > 2, while algorithm sparse presence litter bare soil. purely method Beer-Lambert law seems offer good compromise, since provides reasonable values both low higher ranges. Surprisingly, variation among decoupled differences climate permeability overstory, increased diversity (expressed species richness) hence proposes new applications modelling.