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

TL;DR

TAM-RL is introduced, a framework that couples spatio-temporal representation learning with knowledge-guided encoder-decoder architecture and loss function derived from the carbon balance equation that demonstrates that integrating physically grounded constraints with adaptive representation learning can substantially enhance the robustness and transferability of global carbon flux estimates.

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.

Figures (5)

• Figure 1: Spatial distribution of the sites used in the current study where color represents climate type according to Köppen–Geiger climate classification essd-13-5087-2021. The sites are derived from the FLUXCOM, AmeriFlux, ICOS, and JapanFlux networks.
• Figure 2: TAM-RL Architecture
• Figure 3: Barplots illustrating mean RMSE of CT-LSTM, TALMSTL, TAM-RL, XGBoost and FLUXCOM-X-BASE aggregated by IGBP and Köppen climate types. The error bars are computed for classes with more than one site presenting them. NEE_VUT_USTAR50_QC=1.
• Figure 4: Relative RMSE heatmap of XGBoost, CT-LSTM, TAMLSTM, and TAM-RL derived in comparsion to FLUXCOM-X-BASE with NEE_VUT_USTAR50_QC=1.
• Figure 5: Scatter plots of RMSE and $R^2$ of TAM-RL and FLUXCOM-X-BASE computed for every site and colored by the Koppen climate type.