Predicting the productivity of Alpine grasslands using remote sensing information

• DOI: 10.5194/egusphere-2023-2824
• Publication Date: 2024-01-23T12:59:25Z
• Authors: Saverio Vicario; Marta Magnani; Maria Adamo; Gianna Vivaldo; Chiara Richiardi; Mariasilvia Giamb...; Antonello Provenzale
• Keywords: Empirical modelling

Abstract. Gross primary productivity (GPP) is a crucial variable for ecosystem dynamics, and it can significantly vary on the small spatial scales of vegetation environmental heterogeneity. This especially true mountain ecosystems, which pose severe difficulties to field monitoring. In addition, specificity such ecosystems extreme abiotic conditions that they experience often make global regional models unsuited predictions. this case, remote sensing products offer opportunity explore communities in areas as Alpine grasslands all year round, empirical help challenge modelling GPP. Along these lines, we took hybrid approach, blending several data sources (such high-definition digital terrain model moderate- high- resolution satellite MODIS Sentinel 2) gridded datasets ERA5 with situ measurements implement specific model. The resulting remote-sensing-based developed here was suited represent measured different within high-altitude grassland at Nivolet plain, north-western Italian Alps 2700–2500 m amsl. A cross-validation approach allowed us evaluate what extent single could diverse factors found areas. We finally identified ratio between MCARI2 MSAVI2 good predictor light use efficiency, key factor model, probably due its correlation leaves phenological status, inasmuch estimates chlorophyll ensemble leaf pigments.