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

作者：Daniel Mederer, Hannes Feilhauer, Eya Cherif, Katja Berger, Tobias Hank, Kyle R. Kovach, Phuong D. Dao, Bing Lu, Philip A. Townsend, Teja Kattenborn · 发表于：ISPRS Open Journal of Photogrammetry and Remote Sensing · 年份：2024 · DOI：10.1016/j.ophoto.2024.100080 · 被引用次数：13 · 研究领域：Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses、Species Distribution and Climate Change

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 ...