  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.
