Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes

• URL: http://arxiv.org/abs/2603.09974v2
• Date: Wed, 11 Mar 2026 16:27:50 GMT
• Title: Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes
• Abstract summary: Existing data-driven upscaling products often fail to generalize beyond observed domains.<n>We introduce Task-Aware Modulation with Representation Learning (TAM-RL)<n>TAM-RL improves predictive performance relative to existing state-of-the-art datasets.
• Score: 1.3663949573259215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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