Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery

Regular measurement of carbon stock in the world's forests is critical for carbon accounting and reporting under national and international climate initiatives and for scientific research but has been largely limited in scalability and temporal resolution due to a lack of ground-based assessments. Increasing efforts have been made to address these challenges by incorporating remotely sensed data. We present a new methodology that uses multi-sensor, multispectral imagery at a resolution of 10 m and a deep learning-based model that unifies the prediction of aboveground biomass density (AGBD), canopy height (CH), and canopy cover (CC), as well as uncertainty estimations for all three quantities. The model architecture is a custom Feature Pyramid Network consisting of an encoder, decoder, and multiple prediction heads, all based on convolutional neural networks. It is trained on millions of globally sampled GEDI-L2/L4 measurements. We validate the capability of the model by deploying it over the entire globe for the year 2023 as well as annually from 2016 to 2023 over selected areas. The model achieves a mean absolute error for AGBD (CH, CC) of 26.1 Mg/ha (3.7 m, 9.9%) and a root mean squared error of 50.6 Mg/ha (5.4 m, 15.8%) on a globally sampled test dataset, demonstrating a significant improvement over previously published results. We also report the model performance against independently collected ground measurements published in the literature, which show a high degree of correlation across varying conditions. We further show that our pre-trained model facilitates seamless transferability to other GEDI variables due to its multi-head architecture.