Dr. Alicia Palacios-Orueta | Spatial Data Analysis | Research Excellence Award
Universidad Politécnica de Madrid | Spain
Dr. Alicia Palacios-Orueta is a Full Professor (Catedrática de Universidad) in the Department of Agroforestry Engineering at the Technical University of Madrid (Universidad Politécnica de Madrid, UPM), with an internationally recognized career in environmental science and remote sensing. She holds a degree in Agricultural Engineering from UPM and completed her MSc and PhD in Soil Science at the University of California, Davis. Her academic trajectory spans more than two decades, including appointments as Assistant Professor, Associate Professor, and Full Professor, as well as research positions in leading international institutions in the United States, Italy, Israel, and Sweden. Her research focuses on soil science, climatology, meteorology, geology, plant ecology, remote sensing, hyperspectral analysis, and spatial–temporal time series analysis. She is especially known for pioneering work on hyperspectral indices for soil and vegetation assessment and for innovative statistical approaches to remote sensing time series with spatial dimensions. Her research has received international visibility, including coverage by Nature Geoscience and New Scientist, and she has contributed to advisory bodies such as the Seosat/Ingenio Mission Advisory Group. She has coordinated numerous national and international research projects, supervised doctoral theses, and taught extensively at undergraduate, master’s, and doctoral levels. Her scholarly impact is reflected in 44 documents, an h-index of 21, and approximately 1,743 citations. Overall, Dr. Palacios-Orueta is a leading scholar whose work has significantly advanced the understanding of land–atmosphere interactions and environmental monitoring through remote sensing.
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Featured Publications
Available and missing data to model impact of climate change on European forests
– Ecological Modelling, 2020
Improving aboveground forest biomass maps: From high-resolution to national scale
– Remote Sensing, 2019