Publications

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This article/publication is supported by the EU COST (European Cooperation in Science and Technology) Action CA22136 “Pan-European Network of Green Deal Agriculture and Forestry Earth Observation Science” (PANGEOS)

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2026

  1. Berger, K., Hostert, P., Schlerf, M., Immitzer, M., Szantoi, Z., Okujeni, A., Foerster, S., Colditz, R., Giardino, C., Machwitz, M., Weiss, M., Defourny, P., Kutser, T., Remeta, P., Foerster, M., Féret, J.-B., Asadzadeh, S., Chabrillat, S., Croft, H., … Herold, M. (2026). Advancing optical earth observation for EU policies: needs, opportunities, recommendations. Environmental Sciences Europe, 38(1), 52. https://doi.org/10.1186/s12302-026-01346-3
  2. Fernandez-Gallego, J. A., Segarra, G., Trillas, M. I., Barceló, M., Gjakoni, P., Kefauver, S. C., & Araus, J. L. (2026). Early detection of pre-symptomatic downy mildew (Pseudoperonospora cubensis) infection in cucumbers by using deep learning tools based on hyperspectral imagery. Smart Agricultural Technology, 13, 101726. https://doi.org/10.1016/j.atech.2025.101726
  3. Jauregui-Besó, J., Aparicio, N., Álvarez, S., Nieto-Taladriz, M. T., Araus, J. L., & Kefauver, S. C. (2026). Multi-sensor phenotyping of yield and yield stability for genotype selection in durum wheat. Plant Phenomics, 8(1), 100178. https://doi.org/10.1016/j.plaphe.2026.100178
  4. Petrovic, B., Candotti, A., Grande, C., Faerber, M. S., & Tomelleri, E. (2026). Potential of terrestrial laser scanning for characterizing forest regeneration: A review and an experimental case study. Open Research Europe, 5, 341. https://doi.org/10.12688/openreseurope.21434.2
  5. Procházka, J., Komárek, J., Moravec, D., Rous, J., & Klouček, T. (2026). Pansharpening largely preserves the normalized difference vegetation index: a multi-sensor comparison with PRISMA, Landsat 9, and field spectroscopy. Frontiers in Remote Sensing, 6. https://doi.org/10.3389/frsen.2025.1713895
  6. Werfeli, M., Ganeva, D., García-Soria, J. L., Mihai, L., Filchev, L., Roumenina, E., Verrelst, J., & Hueni, A. (2026). Quantifying Uncertainty in Spectral Leaf Chlorophyll Content Retrieval: A Propagation Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 19, 12606–12621. https://doi.org/10.1109/JSTARS.2026.3679990
  7. Yel, S. G., Özmen, H. B., Öney Birol, S., Tunç Görmüş, E., & Kaplan, G. (2026). Remote sensing applications for assessing climate change impacts on deciduous forests—A systematic review. Physics and Chemistry of the Earth, Parts A/B/C, 143, 104321. https://doi.org/10.1016/j.pce.2026.104321

2025

  1. Arok, M., Brkljač, B., Lugonja, P., Ivošević, B., Vukotić, M., & Nikolić Lugonja, T. (2025). High resolution descriptors for UAV mapping in biodiversity conservation – A case study of sandy steppe habitat renewal. PLOS ONE, 20(3), e0315399. https://doi.org/10.1371/journal.pone.0315399
  2. Gracia-Romero, A., Segarra, J., Rezzouk, F. Z., Aparicio, N., Kefauver, S. C., & Araus, J. L. (2025). In-Field Phenotyping Using the Low-Cost and Open Access Fluorescence PhotosynQ Multispeq Sensor Together with NDVI: A Case Study with Durum Wheat. Agriculture, 15(4), 385. https://doi.org/10.3390/agriculture15040385
  3. Kaplan, G., & Özbey, A. A. (2025). Identifying Environmental Constraints on Pinus brutia Regeneration Using Remote Sensing: Toward a Screening Framework for Sustainable Forest Management. Forests, 16(12), 1816. https://doi.org/10.3390/f16121816
  4. Narin, O. G., Ganeva, D., Abdikan, S., Sekertekin, A., Bayik, C., Chanev, M., Dimitrov, Z., Esetlili, M. T., Alpat, M., Bektas, M., Filchev, L., Ustuner, M., Balik Sanli, F., & Kurucu, Y. (2025). Integration of multi-temporal sentinel-1 and sentinel-2 data for paddy rice crop height estimation and uncertainty assessment using quantile regression forests. Precision Agriculture, 26(6), 94. https://doi.org/10.1007/s11119-025-10287-5
  5. Prikaziuk, E., Silva, C. F., Koren, G., Cai, Z., Berger, K., Belda, S., Graf, L. V., Tomelleri, E., Verrelst, J., Segarra, J., & Ganeva, D. (2025). Evaluation and improvement of Copernicus HR-VPP product for crop phenology monitoring. Computers and Electronics in Agriculture, 233, 110136. https://doi.org/10.1016/j.compag.2025.110136
  6. Riba, A., Garcia, M., Tarquís, A. M., Domingo, F., Antala, M., Feng, S., Liu, J., Johnson, M. S., Kim, Y., & Wang, S. (2025). Integrating Remotely Sensed Thermal Observations for Calibration of Process-Based Land-Surface Models: Accuracy, Revisit Windows, and Implications in a Dryland Ecosystem. Remote Sensing, 17(21), 3630. https://doi.org/10.3390/rs17213630
  7. Weinman, A., Linker, R., & Rozenstein, O. (2025). Uncertainty propagation analysis of remote sensing data in a coupled crop-radiative transfer model using particle filter and winding stairs. Environmental Modelling & Software, 193, 106645. https://doi.org/10.1016/j.envsoft.2025.106645
  8. Weinman, A., Malachy, N., Linker, R., & Rozenstein, O. (2025). Assimilation of UAV multispectral imagery into a coupled DSSAT-CROPGRO − SCOPE model for processing tomatoes. Computers and Electronics in Agriculture, 236, 110460. https://doi.org/10.1016/j.compag.2025.110460

2024

  1. Ganeva, D., Filchev, L., Roumenina, E., Dragov, R., Nedyalkova, S., & Bozhanova, V. (2024). Winter Durum Wheat Disease Severity Detection with Field Spectroscopy in Phenotyping Experiment at Leaf and Canopy Level. Remote Sensing, 16(10), 1762. https://doi.org/10.3390/rs16101762