用物理约束提升碳通量估算精度，跨区域表现更稳定。
Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes
- 结合时空表征学习与物理方程指导的编码解码结构
- 在150多个站点上降低8-9.6%均方根误差，R²从19.4%升至43.8%
- 适合需要高可靠性碳通量估计的研究者使用
准确放大陆地碳通量对全球碳预算估算至关重要，但受限于地面观测点稀疏且区域分布不均。现有数据驱动的放大产品常无法泛化到未观测区域，导致系统性区域偏差和高预测不确定性。本文提出任务感知调制与表征学习框架（TAM-RL），将时空表征学习与基于碳平衡方程构建的知识引导编码解码架构及损失函数相结合。在超过150个代表不同生物群落和气候区的通量塔站点上，TAM-RL相比现有最先进数据集，预测性能显著提升，均方根误差降低8%-9.6%，解释方差（R²）从19.4%提高至43.8%，具体取决于目标通量。结果表明，融合物理约束与自适应表征学习可显著增强全球碳通量估计的鲁棒性与可迁移性。
原文摘要 · Abstract (English)
Accurately upscaling terrestrial carbon fluxes is central to estimating the global carbon budget, yet remains challenging due to the sparse and regionally biased distribution of ground measurements. Existing data-driven upscaling products often fail to generalize beyond observed domains, leading to systematic regional biases and high predictive uncertainty. We introduce Task-Aware Modulation with Representation Learning (TAM-RL), a framework that couples spatio-temporal representation learning with knowledge-guided encoder-decoder architecture and loss function derived from the carbon balance equation. Across 150+ flux tower sites representing diverse biomes and climate regimes, TAM-RL improves predictive performance relative to existing state-of-the-art datasets, reducing RMSE by 8-9.6% and increasing explained variance (R2) from 19.4% to 43.8%, depending on the target flux. These results demonstrate that integrating physically grounded constraints with adaptive representation learning can substantially enhance the robustness and transferability of global carbon flux estimates.
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