Dr. Julia Amorós López obtained her degree in Telecommunications Engineering, as well as her Master's and Doctorate degrees in Electronic Engineering from the University of Valencia in 2000, 2002, and 2012, respectively. Since 2003, she has been part of the Department of Electronic Engineering at the University of Valencia, where she is currently a tenured professor. She teaches courses on industrial automation, time series analysis, image processing, machine learning, deep learning, and classification and change detection in remote sensing data. She is also a researcher at the Image Processing Laboratory at the University of Valencia, which is made up of a multidisciplinary team of physicists, mathematicians, and engineers. Her work is mainly related to pattern recognition and machine learning applied to multispectral remote sensing images. Specifically, her doctoral thesis focused on image fusion, developing methods to improve spatial resolution by applying multitemporal image fusion techniques and change detection with improved spatio-temporal resolutions.

In addition, she has participated in the development of ESA's Cal/Val database and in various field campaigns (SPARC, SEN2FLEX, CEFLES2), helping to organize and manage different types of vegetation parameter measurements from in situ, aerial, and satellite data. Her research in machine learning and pattern recognition has also been carried out within the framework of several national and international projects. She has been involved in several national transfer projects (Valencian Cartographic Agency - ICV; National Geographic Institute - IGN), and in numerous international projects funded by the European Space Agency (ESA), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the European Union (Sentinels Synergy Framework SenSyF project of the FP7-Space program).

She has mainly developed algorithms in classification, change detection, and image fusion applications using different optical sensors for Earth observation, including aerial and satellite data (Proba/CHRIS, Landsat, SPOT, MERIS, MODIS, Sentinel-2, among others). She has also participated in the development of algorithms for cloud removal in ESA projects and in multitemporal analysis for crop monitoring. Currently, his research focuses on obtaining biophysical parameters and vegetation properties using satellite data, multitemporal image fusion, and change detection. She also works with the Department of Geography and History at the University of Valencia on estimating ocean water parameters using hyperspectral remote sensing images, as well as classifying different optical water types (OWT) using Sentinel-3 images, UAVs, and in-situ measurements.

Dr. J. Amorós López has a Hirsch h-index of h=21 according to Google Scholar.

Javier Cervera Montesinos, graduated in physical sciences (1998) and PhD in physics (2003) at the University of Valencia. Nowadays he works as Associate Professor at the Earth Physics and Thermodynamics. He is member of the consolidated group in teaching innovation in thermodynamics, having participated in teaching innovation projects for more than 10 years. His research interests involve the ionic transport in nanopores, with applications in detectors, actuators, and neuromorphic computation, and in biolectricity models in non-excitable cells.

Head of the Environmental Remote Sensing Group (UV-ERS) at the University of Valencia (www.uv.es/uvers), whose main lines of research are linked to the study of vegetation using remote sensing (development of algorithms for its detection and quantification). With my latest national projects (ÁRTEMIS, RESET CLIMATE, ESCENARIOS, and ECCE EO), we have initiated the study of carbon fluxes using satellite imagery. Throughout my career, I have supervised 5 doctoral theses and numerous research projects (12 credits), master's theses (12 credits), and final degree projects.

Prof. Dr. Luis Gómez-Chova is Full Professor at the Department of Electronics Engineering, University of Valencia, Spain, and Senior Researcher with the Image and Signal Processing Group at the Imaging Processing Laboratory (IPL). He received the B.Sc. (with first-class honors), M.Sc., and Ph.D. (summa cum laude) degrees in electronic engineering from the University of Valencia, Spain, in 2000, 2002 and 2008, respectively. He was the recipient of the National Award for Electronic Engineering by the Spanish Ministry of Education. He has completed different doctoral and postdoctoral stays at prestigious research centers (ESA, DLR, DTU).

He is a senior researcher of the Image and Signal Processing (ISP, isp.uv.es) group at the Image Processing Laboratory (IPL), where his research focuses on machine learning and image processing, particularly in the application domains of remote sensing data analysis and multispectral image processing. He conducts and supervises research on these topics within the framework of many national and international projects. He has been involved in many national projects with public funds, many international projects funded by the European Space Agency (ESA), and by the EU. In particular, he has leaded the development of the cloud detection algorithms for PROBA/CHRIS, ENVISAT MERIS & AATSR, Proba-V, SPOT-VGT, Landsat-8/9, and Sentinel-2, instruments in the frame of several projects. He was the recipient of the Google Earth Engine Award 2015, which was used to fund the 'Cloud detection in the Cloud' project. He is member of the ESA Proba-V Quality Working Group and he has developed the operational cloud detection algorithms implemented in the SPOT-VGT archive reprocessing and Proba-V ground segment.

He is the author (or coauthor) of more than 75 international journal papers (JCR), more than 160 international conference papers, and several international book chapters. He holds a Hirsch's h index h=50 (Google Scholar). He is referee of many international journals and conferences such as IEEE Trans. on Geoscience and Remote Sensing, International Journal of Remote Sensing, Pattern Analysis & Applications, Computers & Geo-sciences, etc.

Visit http://www.uv.es/chovago and http://isp.uv.es for more information.

She holds a Degree in Physics from the University of Valencia (1994–1999) and obtained her PhD in Physics in July 2006 from the University of Valencia (UV). Since 2000, she has carried out teaching and research activities in the Department of Earth Physics and Thermodynamics at UV, except for the period from June 2001 to June 2002, during which she completed a one-year stay at the European Commission’s Joint Research Centre (JRC) in Ispra (Italy), specifically in the Forest Fires Unit. Her continued participation in the Remote Sensing Research Unit (UIT), currently the Environmental Remote Sensing Group of the University of Valencia (UV-ERS), and in the department has included roles as a Research Fellow (2000–2004), Researcher since 2004, and Adjunct Professor in both the Faculty of Physics and the Faculty of Economics over several academic years. She served as Assistant Professor (Ayudante Doctor) in the Department of Earth Physics and Thermodynamics in the Faculty of Physics at the University of Valencia from 2021 to 2025 and is currently a Tenured Professor in the same department. In addition, she has been part of the founding team of the spin-off company EOLAB, SL (https://eolab.es/) since 2007.

Her main research areas have focused on the estimation and validation of biophysical products (fAPAR, LAI, and FVC) derived from satellite imagery, the development of methodologies to detect vegetation changes by combining long satellite time series with wavelet and embedding techniques, and more recently, the development of carbon flux products derived from low-spatial-resolution satellites. She has participated in operational European initiatives for land surface monitoring within the LSA SAF project (EUMETSAT), as well as in various Spanish projects such as ÁRTEMIS (CGL2008-00381/CLI), RESET CLIMATE (CGL2012-35831), ESCENARIOS (CGL2016-75239-R), and ECCE EO (PID2020-118036RB-I00), all of which are aimed at monitoring carbon fluxes in Spain.

My research focuses on the retrieval of land biophysical parameters and on improving the global estimation of carbon and water fluxes using remote sensing and machine learning. I have contributed to the development of global datasets and operational geospatial products, with experience in workflows related to NASA products and the Google Earth Engine platform.

Currently, at the Image Processing Laboratory (ISP), University of Valencia, I combine remote sensing expertise with advanced machine learning to enhance the monitoring and understanding of ecosystem processes. I am also strongly committed to teaching and academic training: I lecture in the Physics degree and in the Data Science bachelor’s program, and I particularly enjoy teaching and mentoring students in remote sensing and machine learning.

Dr. Belén Franch Gras received her PhD in Physics (Cum Laude) from the University of Valencia. Between 2013 and 2019, she carried out her research activity at the University of Maryland and at NASA, where she served as an Associate Research Professor. From 2019 to 2022, she was a Distinguished Researcher at the University of Valencia, and since 2022 she has been a Tenured Associate Professor at the same institution. In parallel, she has been an Adjunct Associate Professor at the University of Maryland since 2019.

Her research focuses on the use of remote sensing for the analysis of the Earth’s surface, with particular emphasis on two main research lines. On the one hand, she works on the estimation of surface albedo and directional effects in the measured radiation, developing during her doctoral thesis models for daily albedo estimation with reduced uncertainty. These models are currently being adapted to medium- and high-spatial-resolution sensors to improve the characterization of the surface energy balance and to correct directional effects. On the other hand, she investigates the application of remote sensing to agricultural monitoring, with a focus on pest monitoring and early crop yield estimation, particularly for strategically important cereals, with the aim of providing objective and quantitative information to support the stability of global food markets.

Dr. Franch leads the Crop Condition Monitoring and Yield Estimation Working Group within NASA Harvest, NASA’s agriculture program, and is a member of the Executive Committee of the GEOGLAM initiative, where she coordinates international agricultural monitoring activities based on remote sensing, leading the in situ measurement working group. She has authored numerous scientific articles in SCI journals, book chapters, technical guides for NASA, and has contributed to high-impact international conferences. She serves as Principal Investigator on competitive projects funded by NASA, ESA, FAO, the Gates Foundation, and national agencies, and leads a research group that includes postdoctoral researchers and PhD students.

Her career has been recognized with several awards, including the NASA Early Career Achievement Medal (2018), the NASA Goddard Exceptional Achievement for Science Award (2019), the EVAP Young Talent Award (2019), the Forinvest Award for Professional Trajectory in Innovation and Entrepreneurship (2020), and the Onda Cero Valencia Award (2022).

Her citation metrics are available on Google Scholar:
https://scholar.google.es/citations?user=OTH_6lcAAAAJ&hl=es