Characterizing the spatial and temporal variability of biophysical variables of a wheat crop using hyper-spectral measurements. - Sophie Moulin; Raúl Zurita-Milla; Martine Guérif; Frédéric Baret (IGARSS, 2003)

Physically-based retrievals of Norway spruce canopy variables from very high spatial resolution hyperspectral data. - Zbynek Malenovský; Lucie Homolová; Pavel Cudlin; Raúl Zurita-Milla; Michael E. Schaepman; Jan G. P. W. Clevers; Emmanuel Martin; Jean-Philippe Gastellu-Etchegorry (IGARSS, 2007)

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. - Wouter Dorigo; Raúl Zurita-Milla; Allard J. W. de Wit; Jason Brazile; Ranvir Singh; Michael E. Schaepman (Int. J. Appl. Earth Obs. Geoinformation, 2007)

Unmixing-Based Landsat TM and MERIS FR Data Fusion. - Raúl Zurita-Milla; Jan G. P. W. Clevers; Michael E. Schaepman (IEEE Geosci. Remote. Sens. Lett., 2008)

J.G. Liu, P.J. Mason, , Essential Image Processing and GIS for Remote Sensing (2009) Wiley-Blackwell, Singapore 978-0-470-51032-2 443 pp., Price: £95.00. - Raúl Zurita-Milla (Int. J. Appl. Earth Obs. Geoinformation, 2011)

Development of an Open-Source Toolbox for the Analysis and Visualization of Remotely Sensed Time Series. - Connie A. Blok; Ulanbek D. Turdukulov; Raúl Zurita-Milla; Vasilios Retsios; Martin Schouwenburg; Mekonnen Metaferia (Cartogr. Int. J. Geogr. Inf. Geovisualization, 2011)

Gridding Artifacts on Medium-Resolution Satellite Image Time Series: MERIS Case Study. - Luis Gómez-Chova; Raúl Zurita-Milla; Luis Alonso 002; Julia Amorós-López; Luis Guanter; Gustavo Camps-Valls (IEEE Trans. Geosci. Remote. Sens., 2011)

Multitemporal Unmixing of Medium-Spatial-Resolution Satellite Images: A Case Study Using MERIS Images for Land-Cover Mapping. - Raúl Zurita-Milla; Luis Gómez-Chova; Luis Guanter; Jan G. P. W. Clevers; Gustavo Camps-Valls (IEEE Trans. Geosci. Remote. Sens., 2011)

Mapping specific crop- A multi sensor temporal approach. - Gourav Misra; Anil Kumar; N. R. Patel; Raúl Zurita-Milla; Alka Singh (IGARSS, 2012)

Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. - Julia Amorós-López; Luis Gómez-Chova; Luis Alonso 002; Luis Guanter; Raúl Zurita-Milla; José F. Moreno; Gustavo Camps-Valls (Int. J. Appl. Earth Obs. Geoinformation, 2013)

Exploring Spatiotemporal Phenological Patterns and Trajectories Using Self-Organizing Maps. - Raúl Zurita-Milla; J. A. E. van Gijsel; Nicholas A. S. Hamm; P. W. M. Augustijn; Anton Vrieling (IEEE Trans. Geosci. Remote. Sens., 2013)

Mining Frequent Spatio-Temporal Patterns in Wind Speed and Direction. - Norhakim Yusof; Raúl Zurita-Milla; Menno-Jan Kraak; Bas Retsios (AGILE Conf., 2014)

Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products. - Muhammad Imran 015; Alfred Stein; Raúl Zurita-Milla (Int. J. Appl. Earth Obs. Geoinformation, 2014)

Introducing co-clustering for hyperspectral image analysis. - Emma Izquierdo-Verdiguier; Raúl Zurita-Milla (IGARSS, 2015)

Visualizing the ill-posedness of the inversion of a canopy radiative transfer model: A case study for Sentinel-2. - Raúl Zurita-Milla; V. C. E. Laurent; J. A. E. van Gijsel (Int. J. Appl. Earth Obs. Geoinformation, 2015)

A Matlab© toolbox for calculating spring indices from daily meteorological data. - Toby R. Ault; Raúl Zurita-Milla; Mark D. Schwartz (Comput. Geosci., 2015)

Using geographically weighted regression kriging for crop yield mapping in West Africa. - Muhammad Imran 015; Alfred Stein; Raúl Zurita-Milla (Int. J. Geogr. Inf. Sci., 2015)

Co-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data. - Xiaojing Wu; Raúl Zurita-Milla; Menno-Jan Kraak (Int. J. Geogr. Inf. Sci., 2015)

Interactive discovery of sequential patterns in time series of wind data. - Norhakim Yusof; Raúl Zurita-Milla; Menno-Jan Kraak; Bas Retsios (Int. J. Geogr. Inf. Sci., 2016)

On the use of guided regularized random forests to identify crops in smallholder farm fields. - Emma Izquierdo-Verdiguier; Raúl Zurita-Milla; Rolf A. de By (MultiTemp, 2017)

Identifying crops in smallholder farms using time series of WorldView-2 images. - Raúl Zurita-Milla; Emma Izquierdo-Verdiguier; Rolf A. de By (MultiTemp, 2017)

Mapping frequent spatio-temporal wind profile patterns using multi-dimensional sequential pattern mining. - Norhakim Yusof; Raúl Zurita-Milla (Int. J. Digit. Earth, 2017)

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture. - Dimitris Stratoulias; Valentyn A. Tolpekin; Rolf A. de By; Raúl Zurita-Milla; Vasilios Retsios; Wietske Bijker; Mohammad Alfi Hasan; Eric F. Vermote (Remote. Sens., 2017)

Identifying Environmental and Human Factors Associated With Tick Bites using Volunteered Reports and Frequent Pattern Mining. - Irene Garcia Martí; Raúl Zurita-Milla; Arno Swart; Kees C. van den Wijngaard; Arnold J. H. van Vliet; Sita Bennema; Margriet Harms (Trans. GIS, 2017)

A Spark-Based Platform to Extract Phenological Information from Satellite Images. - Viktor Bakayov; Romulo Goncalves; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier (eScience, 2018)

Identifying Favorable Spatio-Temporal Conditions for West Nile Virus Outbreaks by Co-Clustering of Modis LST Indices Time Series. - Verónica Andreo; Emma Izquierdo-Verdiguier; Raúl Zurita-Milla; Roberto Rosà; Annapaola Rizzoli; Anna Papa (IGARSS, 2018)

Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval. - Emma Izquierdo-Verdiguier; Raúl Zurita-Milla (IGARSS, 2018)

Checking the Consistency of Volunteered Phenological Observations While Analysing Their Synchrony. - Hamed Mehdipoor; Raúl Zurita-Milla; Ellen-Wien Augustijn; Arnold J. H. van Vliet (ISPRS Int. J. Geo Inf., 2018)

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems. - Rosa Aguilar; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier; Rolf A. de By (Remote. Sens., 2018)

Dataset Reduction Techniques to Speed Up SVD Analyses on Big Geo-Datasets. - Laurens Bogaardt; Romulo Goncalves; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier (ISPRS Int. J. Geo Inf., 2019)

Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. - Azar Zafari; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier (Remote. Sens., 2019)

Correction: Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E. Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. Remote Sensing 2019, 11, 575. - Azar Zafari; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier (Remote. Sens., 2019)

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing. - Emma Izquierdo-Verdiguier; Raúl Zurita-Milla (Int. J. Appl. Earth Obs. Geoinformation, 2020)

An interactive web-based geovisual analytics platform for co-clustering spatio-temporal data. - Xiaojing Wu; Ate Poorthuis; Raúl Zurita-Milla; Menno-Jan Kraak (Comput. Geosci., 2020)

An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering. - Xiaojing Wu; Changxiu Cheng; Raúl Zurita-Milla; Changqing Song (Int. J. Geogr. Inf. Sci., 2020)

Land Cover Classification Using Extremely Randomized Trees: A Kernel Perspective. - Azar Zafari; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier (IEEE Geosci. Remote. Sens. Lett., 2020)

A Multiscale Random Forest Kernel for Land Cover Classification. - Azar Zafari; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2020)

Exploring Spring Onset at Continental Scales: Mapping Phenoregions and Correlating Temperature and Satellite-Based Phenometrics. - Raúl Zurita-Milla; Romulo Goncalves; Emma Izquierdo-Verdiguier; Frank O. Ostermann (IEEE Trans. Big Data, 2020)

Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. - Lorena A. Santos; Karine Reis Ferreira; Michelle Cristina Araújo Picoli; Gilberto Câmara; Raúl Zurita-Milla; Ellen-Wien Augustijn (Remote. Sens., 2021)

Area and Feature Guided Regularised Random Forest: a novel method for predictive modelling of binary phenomena. The case of illegal landfill in Canary Island. - Lorenzo Carlos Quesada-Ruiz; Victor Francisco Rodriguez-Galiano; Raúl Zurita-Milla; Emma Izquierdo-Verdiguier (Int. J. Geogr. Inf. Sci., 2022)

Incorporating Spatial Autocorrelation in Machine Learning Models Using Spatial Lag and Eigenvector Spatial Filtering Features. - Xiaojian Liu; Ourania Kounadi; Raúl Zurita-Milla (ISPRS Int. J. Geo Inf., 2022)

CGC: a Scalable Python Package for Co- and Tri-Clustering of Geodata Cubes. - Francesco Nattino; Ou Ku; Meiert W. Grootes; Emma Izquierdo-Verdiguier; Serkan Girgin; Raúl Zurita-Milla (J. Open Source Softw., 2022)

Spatiotemporal domestic wastewater variability: Assessing implications of population mobility in pollutants dynamics. - Néstor DelaPaz-Ruíz; Ellen-Wien Augustijn; Mahdi Farnaghi; Raúl Zurita-Milla (AGILE Conf., 2023)

Spatial+: A new cross-validation method to evaluate geospatial machine learning models. - Yanwen Wang 004; Mahdi Khodadadzadeh; Raúl Zurita-Milla (Int. J. Appl. Earth Obs. Geoinformation, 2023)

Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle. - Simone Borsci; Ville V. Lehtola; Francesco Nex; Michael Ying Yang; Ellen-Wien Augustijn; Leila Bagheriye; Christoph Brune; Ourania Kounadi; Jamy Li; João L. R. Moreira; Joanne Van Der Nagel; Bernard P. Veldkamp; et al. (AI Soc., 2023)

Harmonizing Machine Learning Based Phenological Modeling: A Unified Workflow for Comparative Analyses. - Mahdi Khodadadzadeh; Peter Kalverla; Raúl Zurita-Milla (IGARSS, 2024)

Using a Deployable Analysis Environment to Study Continental-Scale Phenology at High-Spatial Resolution. - Francesco Nattino; Emma Izquierdo-Verdiguier; Pranav Chandramouli; Meiert W. Grootes; Ou Ku; Fakhereh Alidoost; Raúl Zurita-Milla (IGARSS, 2024)

Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN. - Arun Venugopal; Mahdi Farnaghi; Raúl Zurita-Milla (IGARSS, 2024)

On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction. - Yanwen Wang 004; Mahdi Khodadadzadeh; Raúl Zurita-Milla (CoRR, 2024)

A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples. - Yanwen Wang 004; Mahdi Khodadadzadeh; Raúl Zurita-Milla (Ecol. Informatics, 2025)