Satellite-derived management indicators improve modeling of water and greenhouse gas fluxes in Swiss agroecosystems

Abstract. Agroecosystems regulate carbon, water, and nitrogen cycles, yet robust modeling of water and greenhouse gas (GHG) fluxes remains limited by incomplete or inaccessible information on field management practices. Although high-resolution remote sensing (RS) observations can detect management events such as mowing or harvest, their use for representing management intensity and associated impacts on ecosystem flux dynamics remains limited in existing models. Here, we developed an RS-assisted modeling framework to estimate daily latent heat flux (LE), net ecosystem CO₂ exchange (NEE), nitrous oxide (N₂O), and methane (CH₄) fluxes across six Swiss FluxNet sites (two croplands and four grasslands) between 2016 and 2025. Sentinel-2 time series were used to derive leaf area index and RS-based field management indices (RS-FMIs), detecting mowing events, quantifying defoliation intensity, and identifying crop rotation and bare soil periods. These indicators were combined with meteorological drivers to train XGBoost models for each ecosystem type and target variable separately, and driver contributions were evaluated using SHapley Additive exPlanations (SHAP) analysis.

The RS-FMIs effectively captured in situ recorded management events and enabled improved reconstruction of daily flux variability. Model performances were strong for LE (R² ≈ 0.89–0.90) and NEE (R² ≈ 0.59–0.71), whereas N₂O and CH₄ fluxes were reproduced with moderate accuracy (R² ≈ 0.37–0.55). Models using RS-FMIs performed similarly to those using well-compiled in situ management records, supporting the ability of RS-derived vegetation and management indicators to represent management effects. LE variability was primarily energy-driven and dominated by meteorological conditions, whereas vegetation dynamics and RS-FMIs played stronger roles in shaping NEE, N₂O, and CH₄ variability. These results demonstrate that RS-FMIs offer new opportunities to reconstruct management information and improve the representation of management effects in agroecosystem flux modeling.