Dongchen Zhang

This study integrates GEDI LiDAR with an existing data assimilation system to improve terrestrial carbon monitoring, reducing woody biomass uncertainty by 63.7% and achieving RMSE reduction of 38.3 kg/m2.

This study uses a hybrid state data assimilation framework to generate accurate carbon budget maps of North America, reducing uncertainties in carbon pools and identifying regional trends.

This study reduces carbon cycle uncertainties using model-data fusion at NEON sites by integrating multiple data sources and identifying indirect constraints.