Optimizing hybrid models for forest leaf and canopy trait mapping from EnMAP hyperspectral data with limited field samples

Accurate estimation of functional vegetation traits is essential for understanding ecosystem dynamics and supporting environmental monitoring. This study investigates the potential of spaceborne hyperspectral data from the Environmental Mapping and Analysis Program (EnMAP) for mapping key forest vegetation traits such as leaf area index (LAI), leaf chlorophyll content (C ab ), leaf mass per area (LMA), specific leaf area (SLA), and leaf water content (C w ) as well as the rarely addressed leaf anthocyanin content (C anth ) using a hybrid approach combining radiative transfer calculations and machine learning. While machine learning-based studies on vegetation traits retrieval often have relied on intensive field data collection, the present studies explicitly focus on situations with limited field samples which is a often the case in ecosystem research. Here, we use a combination of the PROSAIL-D leaf and canopy radiative transfer models (RTMs) and Gaussian Process Regression (GPR) optimized with Active Learning (AL) sampling to improve the retrieval accuracy while minimizing reliance on extensive in situ data. Validation results demonstrated that the hybrid approach successfully retrieved LAI (R 2 = 0.69) and C ab (R 2 = 0.68) with relatively low errors (RMSE of 0.172 m 2 /m 2 for LAI and 1.502 μg/cm 2 for C ab ), while more complex traits, such as C anth , LMA, and C w exhibited moderate retrieval performance only (R 2 of 0.45, 0.51, and 0.31, respectively). The application of optimized hybrid GPR models to the study area enabled area-wide landscape forest trait mapping with minimal computational effort.