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

2026-03-10

AI Summary

This study addresses the challenges posed by sparse and regionally biased ground-based carbon flux observations, which severely limit the generalization and increase the uncertainty of data-driven upscaling methods. To overcome these limitations, we propose a novel framework that integrates physical constraints with adaptive representation learning. Specifically, we develop a knowledge-guided encoder–decoder architecture grounded in the carbon balance equation, augmented with spatiotemporal representation learning and a task-aware modulation mechanism to enable high-accuracy upscaling of terrestrial carbon fluxes. Our approach represents the first successful integration of physical priors with deep representation learning, substantially enhancing model robustness and cross-regional transferability. Experiments across more than 150 flux tower sites demonstrate consistent improvements, with RMSE reduced by 8–9.6% and R² increasing from 19.4% to 43.8%.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 ($R^2$) 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.

Problem

• terrestrial carbon fluxes
• upscaling
• spatial generalization
• predictive uncertainty
• regional bias

Innovation

• Task-Aware Modulation
• Representation Learning
• Carbon Flux Upscaling
• Physics-Informed Learning
• Spatio-Temporal Modeling