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

作者：Aolin Jia, Helge Aasen, Lukas Hörtnagl, Iris Feigenwinter, Sélène Ledain, T. Bruce Lauber, Kukka‐Maaria Kohonen, Flavian Tschurr, Lorenz Allemann, Fabio Turco, Nina Buchmann · 年份：2026 · DOI：10.5194/egusphere-2026-3522 · 研究领域：Plant Water Relations and Carbon Dynamics、Remote Sensing in Agriculture、Atmospheric and Environmental Gas Dynamics

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-FMI...