Exploitation of prisma spaceborne hyperspectral observations for improved functional trait retrievals in mid-latitude forest ecosystems

Forest ecosystems cover about one third of the Earth's land surface and provide invaluable ecosystem services, but their extension and health is threatened by the effects of climate change. Remote sensing has the potential to map the condition and functioning of global forests, however methodological and technical challenges still hamper the quantitative estimation of forest traits from spaceborne observations. The advent of new generation satellites and more advanced retrieval schemes may provide the chance to overcome these limitations, but the potential of both the data and models still needs to be assessed. In this study, we addressed the retrieval of forest traits from PRISMA hyperspectral space-borne images collected over a temperate forest in Italy using hybrid retrieval schemes. The results obtained evidenced the potential of PRISMA images and hybrid models for the accurate quantification of Leaf Area Index (LAI) (R-2=0.79, nRMSE=12.5%) and Canopy Chlorophyll Content (CCC) (R-2=0.74, nRMSE=24.2%) in forest ecosystems. The analysis of the retrievals revealed a sharp decrease of both LAI and CCC from June to early September 2022 due to a severe drought that hit Europe in the summer, providing indication of the usefulness of hyperspectral spaceborne imagery for forest monitoring.