Abstract

<p>Quantitative understanding and monitoring of gross primary productivity (GPP) and its response to environmental variables is critical for understanding the feedbacks of ecosystems to the changing climate and projecting the future climate state.</p><p>Due to limitations of the eddy covariance (EC) method related to the restricted spatial coverage obtained with the method, as well as drawbacks of the so-called CO<sub>2</sub> flux partitioning approaches, adding scale-appropriate extra-information on canopy physiological status and flux partitioning is crucial for constraining gross photosynthesis (GPP), also beyond the ecosystem scale.</p><p>Here, we present the outcome of the H2020-MSCA-IF COSIF project aiming at investigating the potential of two novel GPP traces, i.e. carbonyl sulfide (COS) and sun-induced fluorescence (SIF) for inferring GPP.</p><p>The major result of the presented study are three independent GPP data sets obtained with different methods of contrasting theoretical backgrounds (CO<sub>2</sub> flux partitioning, COS and SIF) in a temperate mountain grassland in Neustift, Austria (AT-Neu). Moreover, the study compares empirical approaches with a process-based estimates obtained using the “Soil-Canopy Observation Photosynthesis and Energy fluxes” (SCOPE) model, updated with a soil and leaf COS exchange module. The obtained results foster the use of repeated hyperspectral remote sensing observations together with radiative transfer and biochemical models for carbon assimilation monitoring.</p>

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