Integrating eddy covariance and machine learning for the spatial estimation ofcarbon exchanges in natural grasslands of the Pampa biome

Quantifying carbon exchanges in natural grasslands is crucial for improving management practices, estimating carbon budgets, and supporting climate mitigation policies. However, direct measurements of net ecosystem CO₂ exchange (NEE) using flux towers are spatially limited, particularly in heterogeneous biomes such as the Brazilian Pampa. This study presents a machine learning framework to upscale carbon exchange observations based on flux towers in natural grasslands used for extensive cattle production in southern Brazil. Continuous CO₂ flux measurements were obtained from multiple flux towers installed across four ecological regions representative of the Brazilian Pampa biome, encompassing different combinations of soil types, vegetation structure, climatic conditions, and grassland management. These long-term observations capture pronounced seasonal and interannual variability in NEE, driven primarily by climate variability and grazing management. Artificial neural networks (ANNs) were trained using eddy covariance flux data, meteorological variables (solar radiation, precipitation, air temperature, and humidity) derived from reanalysis products, and vegetation indicators obtained from satellite remote sensing. The trained models were applied to estimate daily NEE in other regions of the Pampa with different edaphoclimatic and vegetation characteristics where flux towers were installed. Model performance was evaluated using independent subsets of eddy covariance observations, with accuracy assessed using standard statistical metrics for this type of model. The results demonstrate that the machine learning approach successfully reproduces observed seasonal patterns and interannual variability of carbon exchanges, enabling spatially explicit estimation of carbon uptake and emissions in natural grasslands. This framework provides a scalable tool for regional carbon accounting in natural grasslands and for deriving regional emission and uptake factors. The approach contributes to improving monitoring, reporting, and verification (MRV) of nature-based climate solutions and supports policies aimed at low-carbon livestock production and conservation of the Pampa biome.