Author

Shawn Paul Serbin

Goddard Space Flight CenterORCID 0000-0003-4136-8971Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:ecology & biodiversity· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

Dongchen ZhangiD· 3 shared Michael C. DietzeiD· 3 shared Alexis HelgesoniD· 1 shared Cameron WebbiD· 1 shared Jonathan H. HugginsiD· 1 shared Li QianyuiD· 1 shared Qianyu LiiD· 1 shared Qianyu LiiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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3 shared papersEcology

Dongchen ZhangORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Harmonizing Repeated Multi-Platform Optical and LiDAR Observations for Continental-Scale Terrestrial Carbon Cycle Data Assimilation.

Shawn P Serbin, Dongchen Zhang, Michael C. Dietze, Qianyu Li

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.

bioRxiv · Nov 2, 2026 · PreprintEcology

Mapping the North American Terrestrial Carbon Cycle: A Process-based Reanalysis Using State Data Assimilation (SDA)

Dongchen Zhang, Jonathan H. Huggins, Qianyu Li, Shashank Ramachandran, Shawn Paul Serbin, Cameron Webb, Zhenpeng Zuo, Michael C. Dietze

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.

bioRxiv · Feb 26, 2026 · PreprintEcology

The uncertainty partitioning of the terrestrial C cycling over CONUS NEON sites using model-data fusion

Dongchen Zhang, Li Qianyu, Alexis Helgeson, Shawn Paul Serbin, Michael C. Dietze

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

bioRxiv · Jun 11, 2025 · PreprintEcology