标题： GreenHyperSpectra：用于全球植被特征预测的多源高光谱数据集 显示英文标题

标题： GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction

摘要： 植物性状如叶片碳含量和叶片质量是研究生物多样性和气候变化的关键变量。 然而，传统的实地采样无法在生态上有意义的空间尺度上可行地覆盖性状变化。 机器学习为跨生态系统预测植物性状提供了一个有价值的解决方案，利用遥感的高光谱数据。 然而，从高光谱数据中进行性状预测面临标签稀缺性和显著的领域转移（\eg across sensors, ecological distributions）的挑战，需要稳健的跨领域方法。 在此，我们提出了GreenHyperSpectra，这是一个包含现实世界跨传感器和跨生态系统样本的预训练数据集，旨在使用半监督和自监督方法对性状预测进行基准测试。 我们采用了一个涵盖分布内和分布外场景的评估框架。 我们成功利用GreenHyperSpectra对标签高效的多输出回归模型进行预训练，其性能优于最先进的监督基线。 我们的实证分析表明，在性状预测的光谱表示学习方面有显著改进，建立了一个全面的方法论框架，以推动表示学习和植物功能性状评估交叉领域的研究。 所有代码和数据均可在以下网址获取：https://github.com/echerif18/HyspectraSSL. 显示英文摘要

摘要： 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.