GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction
This work addresses the challenge of scaling plant trait prediction for biodiversity and climate change studies by providing a dataset and framework to improve machine learning methods in remote sensing, though it is incremental as it builds on existing representation learning approaches.
The authors tackled the problem of predicting plant traits like leaf carbon content from hyperspectral data, which is hindered by label scarcity and domain shifts, by introducing GreenHyperSpectra, a multi-source dataset for pretraining models that outperformed state-of-the-art supervised baselines in cross-domain scenarios.
Plant traits such as leaf carbon content and leaf mass are essential variables in the study of biodiversity and climate change. However, conventional field sampling cannot feasibly cover trait variation at ecologically meaningful spatial scales. Machine learning represents a valuable solution for plant trait prediction across ecosystems, leveraging hyperspectral data from remote sensing. Nevertheless, trait prediction from hyperspectral data is challenged by label scarcity and substantial domain shifts (\eg across sensors, ecological distributions), requiring robust cross-domain methods. Here, we present GreenHyperSpectra, a pretraining dataset encompassing real-world cross-sensor and cross-ecosystem samples designed to benchmark trait prediction with semi- and self-supervised methods. We adopt an evaluation framework encompassing in-distribution and out-of-distribution scenarios. We successfully leverage GreenHyperSpectra to pretrain label-efficient multi-output regression models that outperform the state-of-the-art supervised baseline. Our empirical analyses demonstrate substantial improvements in learning spectral representations for trait prediction, establishing a comprehensive methodological framework to catalyze research at the intersection of representation learning and plant functional traits assessment. All code and data are available at: https://github.com/echerif18/HyspectraSSL.