Quantifying forest biomass is an important part of determining the regional carbon balance, but currently there is little knowledge regarding forest carbon storage at a high spatial resolution. Here, we combined a deep learning (DL) algorithm with field measurements, light detection and ranging (LiDAR) observations, Landsat and ALOS/PALSAR images to develop a spatially explicit estimate of forest aboveground carbon density at a 30 m spatial resolution for northeast China, home to nearly one-third of China’s forested area. We also conducted an uncertainty analysis using the bootstrap method. The DL method performed well, with a high coefficient of determination (R2) of 0.84 and a relatively low root mean squared error of 6.28 MgC ha-1, and is superior to traditional machine learning methods such as random forest, support vector machine and artificial neural network. The forest carbon storage is estimated to be 2.43 ± 0.10 PgC, and increases along the latitude gradient. Among climatic factors, the wettest month precipitation and annual mean temperature stand out in explaining the spatial variation of forest carbon density, with contributions reaching 15.8% and 10.8%, respectively. A model-data comparison shows that current ecosystem models generally capture the spatial pattern of forest carbon density but underestimate the forest carbon storage by 22.2%, partially due to the overestimation of high-temperature inhibition, highlighting the need to re-parameterize such temperature effects in forest carbon simulations.