[2603.03531] Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction

Computer Science > Machine Learning arXiv:2603.03531 (cs) [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 (experimental) 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 (RACI), 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 geog...