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New paper: Estimating vegetation indices and biophysical parameters using SAR data and machine learning
Published: Monday 7.4.2025
Can we estimate vegetation indices and biophysical parameters for forests using SAR & ancillary data and machine learning? Researchers from the EO4Landscape research team (Daniel Paluba and Přemysl Štych), in collaboration with Eurpean Space Agency (Bertrand Le Saux from the Φ-lab and Francesco Sarti), tried to answer this question in a recently published paper in Big Earth Data journal by Taylor & Francis.
Our team comprises leading researchers, young innovators and students from the Department of Applied Geoinformatics and Cartography Faculty of Science at Charles University. This diverse group brings together expertise in EO, geoinformation science, digital and 3D cartography, web map services, ecology, environmental monitoring, programming and data analysis. Our team's strength lies in our ability to approach problems from interdisciplinary perspectives, ensuring comprehensive and innovative solutions. The members of the team have rich experiences with end-users or user uptake activities and international projects. This advantage brings a wide and deep overview of the needs and requirements of the end-users in geoinformatics and EO disciplines. On top of our research activities we are conducting training and capacity building acitivities at all levels of education from primary to terciary education and for public or governmental bodies as well.