Measuring and Modelling Gross Primary Productivity in Alpine and Arctic Tundra: from Point to Landscape Scale Using Satellite Data

Measuring and Modelling Gross Primary Productivity in Alpine and Arctic Tundra: from Point to Landscape Scale Using Satellite Data Authors: Mariasilvia Giamberini (1), Sebastian Aleksandrowicz (2), Francesca Avogadro (1), Ilaria Baneschi (1), Alice Baronetti (1), Arnon Karnieli (3), Marta Magnani (1), Silvio Marta (1), Antonio Monteiro (4), Natalya Panov (3), Angelica Parisi (1), Manuel Salvoldi (3), Saverio Vicario (5), Gianna Vivaldo (1), Edyta Woźniak (3), Antonello Provenzale (1). Istituto di Geoscienze e Georisorse, Consiglio Nazionale delle Ricerche, Italy Centrum Badań Kosmicznych Polskiej Akademii Nauk, Bartycka 18A 00-716 Warsaw, Poland The Remote Sensing Laboratory, Ben Gurion University of the Negev, Israel Institute of Geography and Spatial Planning (IGOT-CEG), University of Lisbon, Portugal Istituto sull’Inquinamento Atmosferico, Consiglio Nazionale delle Ricerche, Italy Gross primary productivity (GPP) is an Essential Variable needed for evaluating the status and changes in terrestrial ecosystems. Measuring GPP in the Alpine and Arctic tundra is especially important. It is also challenging, due to the remoteness of the sites and the harsh weather conditions. Nevertheless, an effort is necessary to understand the behaviour of such natural ecosystems in front of climate change. For this aim, the Institute of Geoscience and Earth Resources of the National Research Council of Italy established two Critical Zone Observatories, respectively in the Western Italian Alps (Nivolet Plain, Gran Paradiso National Park, since 2017) and in the High Arctic (Ny Ålesund, Svalbard, since 2019) both equipped with portable flux chambers and an Eddy Covariance tower. Since then, Gross Primary Productivity and Ecosystem Respiration (ER) have been measured during summer and empirical models have been implemented, correlating GPP and ER to climate variables and other environmental parameters meant to represent vegetation biomass. While flux chambers allow a detailed mapping of an area by capturing the spatial heterogeneity and a direct measure of daily respiration, the measuring campaigns are demanding and thus can only be discontinuous. Eddy Covariance helps in broadening the size of the study area and provides continuous data, still daily ER is calculated using partitioning models that need to be verified with direct measurements. In this framework, Flux chambers and Eddy Covariance complement each other and enforce the validity of GPP estimates over a larger area. A further step in widening the investigation area is reached by modelling GPP using vegetation indices extracted from remote sensing (satellite) data. At Nivolet Plain, in the Alps, Sentinel-2 data provides good coverage during the vegetative season, while at Svalbard the combined limited numbers of cloud-free days and satellite passages is a strong limitation for satellites such as Landsat and Sentinel-2. Usually, their revisit period is too long to guarantee an adequate number of cloud-free scenes allowing them to capture the development of the vegetative season. For this, the availability of the VENμS satellite data, with a 1-day revisit time and 4 metres resolution, are essential to extract a consistent time-series of vegetation index values (https://venus.cnes.fr/en/VENUS/index.htm). We aim to elaborate on a consistent and robust model representing the correlation of GPP with climate and vegetation parameters that allows the widening of the spatial extent of the in-situ measurements used for its validation, addressing some of the cross-scale challenges that characterise field studies. Submitted at: https://egw2023.eurac.edu/