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

Source: [Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes](<https://www.sota2.com/research/paper/69b0f7192d603e6de3e45e6f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-13T05:00:12.498Z

Authors: Aleksei Rozanov, Arvind Renganathan, Vipin Kumar

Published: 2026-03-10T17:59:29.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Net Ecosystem Exchange (NEE) Upscaling on FLUXNET (Specific QC Sites)](<https://www.sota2.com/research/sota/net-ecosystem-exchange-nee-upscaling-on-fluxnet-specific-qc-sites.md>): Net Ecosystem Exchange (NEE) upscaling; FLUXNET EC tower sites, NEE\_VUT\_USTAR50\_QC=1 and NEE\_VUT\_REF\_QC=1 (All sites); RMSE: 1.62.

- [GPP prediction on FLUXNET](<https://www.sota2.com/research/sota/gpp-prediction-on-fluxnet.md>): GPP prediction; FLUXNET; RMSE: 1.97.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.09974>)

[PDF](<https://arxiv.org/pdf/2603.09974.pdf>)