Integrating Eddy-Covariance and Satellite Data to Upscale ET and GHG Fluxes across Swiss Agricultural Systems

Reliable quantification of agricultural water use and greenhouse gas (GHG) emissions is essential for understanding and mitigating the environmental footprint of food production. However, it remains challenging due to the limited spatial representativeness of in-situ measurements and the strong influence of vegetation dynamics, management practices, and weather variability. Eddy-covariance (EC) observations provide direct and high-frequency measurements of evapotranspiration (ET) and GHG fluxes, but their footprint is inherently local, constraining their applicability for regional and national assessments. Satellite remote sensing (RS) offers spatially continuous information on vegetation status and land cover, yet its effective integration with flux observations for process-relevant upscaling remains limited.

In this contribution, we provide first insights from a synthesis of recent field-scale literature, comprising over 300 ET studies and more than 400 GHG-focused studies, to assess how remote sensing information has been incorporated into ET and GHG flux modelling. Our review indicates a clear divergence in modelling development trajectories across flux types. Earlier ET studies were largely dominated by physically based formulations, such as Penman–Monteith and surface energy balance models. Over the past five years, ET modelling has shifted toward data-driven and machine-learning approaches, enabling the integration of a broader range of satellite-derived predictors, including vegetation indices and shortwave infrared (SWIR)-based indicators related to soil moisture conditions. Net ecosystem exchange (NEE) exhibits a similar transition from process-based to data-driven modelling frameworks, reflecting improved data availability and methodological flexibility.

In contrast, modelling of other GHG fluxes, particularly CH₄ and N₂O, remains largely confined to process-based approaches, with DNDC and DayCent being the most widely applied models. This persistence primarily reflects the limited availability of long-term, high-quality ground-based GHG flux measurements. Moreover, RS-based information on soil moisture and temperature, vegetation status, and land-use or management practices offers potential to better inform and constrain GHG flux estimates in agricultural systems. These findings highlight a persistent gap between the availability of spatially explicit satellite information and its current use in GHG flux modelling, pointing to substantial opportunities for improved integration of remote sensing and in-situ flux observations in future upscaling efforts.