Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction
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arXiv:2603.03531v1 Announce Type: cross
Abstract: Accurate prediction of terrestrial ecosystem carbon fluxes (e.g., CO$_2$, GPP, and CH$_4$) is essential for understanding the global carbon cycle and managing its impacts. However, prediction remains challenging due to strong spatiotemporal heterogeneity: ecosystem flux responses are constrained by slowly varying regime conditions, while short-term fluctuations are driven by high-frequency dynamic forcings. Most existing learning-based approache
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--> Computer Science > Machine Learning arXiv:2603.03531 [Submitted on 3 Mar 2026] Title: Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction Authors: Yiming Sun , Runlong Yu , Rongchao Dong , Shuo Chen , Licheng Liu , Youmi Oh , Qianlai Zhuang , Yiqun Xie , Xiaowei Jia View a PDF of the paper titled Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction, by Yiming Sun and 8 other authors View PDF HTML Abstract: Accurate prediction of terrestrial ecosystem carbon fluxes (e.g., CO$_2$, GPP, and CH$_4$) is essential for understanding the global carbon cycle and managing its impacts. However, prediction remains challenging due to strong spatiotemporal heterogeneity: ecosystem flux responses are constrained by slowly varying regime conditions, while short-term fluctuations are driven by high-frequency dynamic forcings. Most existing learning-based approaches treat environmental covariates as a homogeneous input space, implicitly assuming a global response function, which leads to brittle generalization across heterogeneous ecosystems. In this work, we propose Role-Aware Conditional Inference , a process-informed learning framework that formulates ecosystem flux prediction as a conditional inference problem. RACI employs hierarchical temporal encoding to disentangle slow regime conditioners from fast dynamic drivers, and incorporates role-aware spatial retrieval that supplies functionally similar and geographically local context for each role. By explicitly modeling these distinct functional roles, RACI enables a model to adapt its predictions across diverse environmental regimes without training separate local models or relying on fixed spatial structures. We evaluate RACI across multiple ecosystem types (wetlands and agricultural systems), carbon fluxes (CO$_2$, GPP, CH$_4$), and data sources, including both process-based simulations and observational measurements. Across all settings, RACI consistently out...
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