Plant trait retrieval from hyperspectral data: Collective efforts in scientific data curation outperform simulated data derived from the PROSAIL model

Plant traits play a pivotal role in steering ecosystem dynamics. As plant canopies have evolved to interact with light, spectral data convey information on a variety of plant traits. Machine learning techniques have been used successfully to retrieve diverse traits from hyperspectral data. Nonetheless, the efficacy of machine learning is restricted by limited access to high-quality reference data for training. Previous studies showed that aggregating data across domains, sensors, or growth forms provided by collaborative efforts of the scientific community enables the creation of transferable models. However, even such curated databases are still sparse for several traits. To address these challenges, we investigated the potential of filling such data gaps with simulated hyperspectral data generated through the most widely-used radiative transfer model (RTM) PROSAIL. We coupled trait information from the TRY plant trait database with information on plant communities from the sPlot database, to build a realistic input trait dataset for the RTM-based simulation of canopy spectra. Our findings indicate that simulated data can alleviate the effects of data scarcity for highly underrepresented traits. In most other cases, however, the effects of including simulated data from RTMs are negligible or even negative. While more complex RTM models promise further improvements, their parameterization remains challenging. This highlights two key observations: firstly, RTM models, such as PROSAIL, exhibit limitations in producing realistic spectra across diverse ecosystems; secondly, real-world data repurposed from various sources exhibit superior retrieval success compared to simulated data. As a result, we advocate to emphasize the importance of active data sharing over secrecy and overreliance on modeling to address data limitations. •Robust biophysical trait retrieval using machine-learning requires ample training data.•Can we use radiative transfer model simulations to augment real-world spectral data?•We compare the retrieval performances gained with real-world and hybrid data sets.•Results show that real-world data alone in most cases enable more accurate results.•Only in case of real-world data shortage, simulated spectra lead to improvements.