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 ($R^2$) 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.

翻译：准确估算陆地碳通量的升尺度值是评估全球碳收支的核心环节，但由于地面观测数据稀疏且存在区域偏差，该任务仍具挑战性。现有的数据驱动升尺度产品往往难以泛化至观测域之外，导致系统性的区域偏差和较高的预测不确定性。我们提出了基于表征学习的任务感知调制框架，该框架将时空表征学习与知识引导的编码器-解码器架构相结合，并采用源自碳平衡方程的损失函数。在代表不同生物群落和气候区的150多个通量塔站点上，TAM-RL相较于现有最先进数据集提升了预测性能，将均方根误差降低了8-9.6%，并将解释方差从19.4%提升至43.8%（具体提升幅度因目标通量而异）。这些结果表明，将基于物理的约束条件与自适应表征学习相结合，能够显著提升全球碳通量估算的鲁棒性和可迁移性。