Coupled mechanistic ecohydrological modelling and machine learning can provide robust estimates of global ET and GPP


Quentin Mailhos, Imperial College London, London, SW7, United Kingdom, Athanasios Paschalis, Imperial College London, Department of Civil and Environmental Engineering, London, United Kingdom, Charles Zogheib, Imperial College London, Civil and Environmental Engineering, London, United Kingdom and Simone Fatichi, ETH Zurich, Institute of Environmental Engineering, Zurich, Switzerland
Back to: Applications of Machine Learning and Data Cubes in Remote Sensing III Posters