CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling

TL;DR

CarbonSense is the first comprehensive, machine learning-ready dataset combining flux measurements, meteorological data, and satellite imagery to advance data-driven carbon flux modeling with multimodal deep learning.

Contribution

We introduce CarbonSense, a standardized multimodal dataset for carbon flux modeling and establish baseline models including a novel transformer-based approach.

Findings

Multimodal deep learning improves carbon flux prediction accuracy.

Baseline models demonstrate the dataset's utility for model development.

The dataset covers 385 global locations, enabling diverse model training.

Abstract

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.