Terrestrial carbon fluxes provide vital information about our biosphere's
health and its capacity to absorb anthropogenic CO$_2$ emissions. The
importance of predicting carbon fluxes has led to the emerging field of
data-driven carbon flux modelling (DDCFM), which uses statistical techniques to
predict carbon fluxes from biophysical data. However, the field lacks a
standardized dataset to promote comparisons between models. To address this
gap, we present CarbonSense, the first machine learning-ready dataset for
DDCFM. CarbonSense integrates measured carbon fluxes, meteorological
predictors, and satellite imagery from 385 locations across the globe, offering
comprehensive coverage and facilitating robust model training. Additionally, we
provide a baseline model using a current state-of-the-art DDCFM approach and a
novel transformer based model. Our experiments illustrate the potential gains
that multimodal deep learning techniques can bring to this domain. By providing
these resources, we aim to lower the barrier to entry for other deep learning
researchers to develop new models and drive new advances in carbon flux
modelling.

通过使用统计技术从生物物理数据中预测碳通量，世界上第一个机器学习准备就绪的数据集 CarbonSense 整合了全球 385 个地点的测量碳通量、气象预测因子和卫星图像，提供了全面的覆盖并促进了模型训练，同时通过提供基准模型和新的基于 Transformer 的模型，展示了多模态深度学习技术在该领域带来的潜在收益，旨在降低其他深度学习研究人员进入和推动碳通量建模领域的门槛。