Lopez Lozano Raul


Chargé de Recherche INRAE : High-resolution remote sensing applications for agriculture, development of field plant phenotyping methods, crop modelling, radiative transfer modelling, crop yield forecasting, machine learning.


2022: HDR: Avignon Université (France), title: “Close-range and remote sensing methods to characterize the spatial heterogeneity of crops at different scales : applications to field phenotyping, precision agriculture and crop yield forecasting”

2008 : PhD thesis: Department of Geography and Land Management. University of Zaragoza (Spain). Dissertation title: “Use of spatial information technologies to map biophysical parameters on maize and orchards plots for precision farming”.

2003 : Master: Spatial Information Technologies (GIS and remote sensing) for land management. Department of Geography and Land Management. University of Zaragoza (Spain).

2001 : Degree in Geography. Department of Geography and Land Management. University of Zaragoza (Spain).


Depuis 2018 : Chargé de Recherche Classe Normale (CRCN). INRAE-UMR 1114-EMMAH, UMT CAPTE

2013 – 2018 : Officier Scientifique. Monitoring Agricultural Resources Unit (MARS). Centre Commun de Recherche. Commission Européenne, Ispra (Italie)

2010 – 2012 : Grantholder Cat. 30. Monitoring Agricultural Resources Unit (MARS). Centre Commun de Recherche. Commission Européenne, Ispra (Italie).

2010 – 2010 : Ingénieur de Recherche – Chef du projet. INRAE-UMR 1114-EMMAH

2009 – 2010: Ingénieur de Recherche. UMR ITAP. Montpellier SupAgro.

2008 – 2009: Ingénieur de Recherche. INRAE-UMR 1114-EMMAH

2007 – 2007 : Développeur de logiciel. Centre de Recherche et Technologie Agro-alimentaire d’Aragón (CITA). Gouvernement d’Aragón (Espagne).

2003 – 2007 : Doctorat effectué au Centre de Recherche et Technologie Agro-alimentaire d’Aragón (CITA). Gouvernement d’Aragón (Espagne).


2004 - 2006 : Master “Spatial Information Technologies (GIS and remote sensing) for land management. Department of Geography and Land Management”. University of Zaragoza (Spain). Modules : Field radiometry ; Information technologies for agriculture. (16 hours/year)


VELUMANI KaaviyaDec.2018- Jul. 2021Deep Learning Algorithms for high-throughput cereal plant and organ identification.
SEROUART MarioMay 2021-May 2024Intra-specific competition of maize: characterisation of architectural plasticity by  field phenotyping and structural models


TitleYearsRoleFunding Body
PHENET : Tools and methods for extended plant PHENotyping and EnviroTyping services of European Research Infrastructures.2023-2027PartnerHorizon Europe
FFAST ; Functioning from the assimilation of structural traits. Understanding wheat functioning from the assimilation of high throughput observations into plant simulation models2022-2026PIANR
Télétypage : Télédétection appliqué au phénotypage du blé. Utilisation des données satellitaires à haute résolution avec des observations acquises par les plateformes de phénotypage à haut débit pour caractériser le fonctionnement des génotypes de blé2022-2024PIPNTS (Plan National de Télédétection Spatiale)
VerSEau: Suivi des pratiques agricoles et de la phénologie des Vergers à partir d’images Sentinel pour mieux gérer les ressources en Eau sur le territoire Nord-Vaucluse.2020-2023PartnerRégion PACA


Rouault, P., Courault, D., Flamain, F., Pouget, G., Doussan, C., Lopez-Lozano, R., McCabe, M., Debolini, M. (2024): High-resolution satellite imagery to assess orchard characteristics impacting water use. Submitted to Agricultural Water management, 295,108763, https://doi.org/10.1016/j.agwat.2024.108763 OA

Li, W, Weiss, M., Jay, S., Wei, S., Zhao, N., Comar , A., Lopez-Lozano, R., De Solan, B., Yu, Q., Wu, W., Baret, F.(2024)  Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat. Remote Sensing on Environment, 300, 113883 https://doi.org/10.1016/j.rse.2023.113883

Serouart, M., Lopez-Lozano, R., Daubige, G., Baumont, M., Escale, B., De Solan, B., Baret, F. (2023-01): Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images.  Plant Phenomics, 2023, Article ID 0046, https://doi.org/10.34133/plantphenomics.0046, OA

Serouart, M., Madec, S., David, E., Velumani, K., Lopez-Lozano, R., Weiss, M., Baret, F. (2022-11-12). SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods. Plant Phenomics, 2022, Article ID 9803570, https://doi.org/10.34133/2022/9803570, OA

Wang. J., Lopez-Lozano, R., Weiss, M., Buis, S., Li, W., Liu, S., Baret, F., Zhang, J. (2022-09-01). Crop specific inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework. Remote Sensing of Environment, 278, 113085. https://doi.org/10.1016/j.rse.2022.113085

Toda, Y., Sasaki, G., Ohmori, Y., Takahashi, H., Takanashi, H., Tsuda, M., Kajiya-Kanegae, H., Lopez-Lozano, R. et al. (2022-03-16). Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations. Frontiers in Plant Science, 13, 828864,  https://doi.org/10.3389/fpls.2022.828864, OA

Velumani K., Lopez-Lozano R., Madec S., Guo W., Gillet J., Comar A., Baret F. (2021-08-21). Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution. Plant Phenomics, 2021, 1-16, https://dx.doi.org/10.34133/2021/9824843, https://hal.inrae.fr/hal-03769738, OA

Li W., Comar A., Weiss M., Jay S., Colombeau G., Lopez-Lozano R., Madec S., Baret F. (2021-12-06). A Double Swath Configuration for Improving Throughput and Accuracy of Trait Estimate from UAV Images. Plant Phenomics, 2021, 1-11, https://dx.doi.org/10.34133/2021/9892647, https://hal.inrae.fr/hal-03583959, OA

Toreti A., Deryng D., Tubiello F., Müller C., Kimball B., Moser G., Boote K., Asseng S., Pugh T., Vanuytrecht E. ... Lopez-Lozano R. et al. (2020-12). Narrowing uncertainties in the effects of elevated CO2 on crops. Nature Food, 1 (12), 775-782, https://dx.doi.org/10.1038/s43016-020-00195-4, https://hal.inrae.fr/hal-03093673, OA

Velumani K., Madec S., de Solan B., Lopez-Lozano R., Gillet J., Labrosse J., Jezequel S., Comar A., Baret F. (2020-07). An automatic method based on daily in situ images and deep learning to date wheat heading stage. Field Crops Research, 252, 107793, https://dx.doi.org/10.1016/j.fcr.2020.107793, https://hal.inrae.fr/hal-03162912, OA

García-León D., Lopez-Lozano R., Toreti A., Zampieri M. (2020-06). Local-Scale Cereal Yield Forecasting in Italy: Lessons from Different Statistical Models and Spatial Aggregations. Agronomy, 10 (6), 809, https://dx.doi.org/10.3390/agronomy10060809, https://hal.inrae.fr/hal-03163254, OA

García-Condado S., Lopez Lozano R., Panarello L., Cerrani I., Nisini L., Zucchini A., van Der Velde M., Baruth B. (2019). Assessing lignocellulosic biomass production from crop residues in the European Union: modelling, analysis of the current scenario, and drivers of inter-annual variability. Global Change Biology - Bioenergy, 11 (6), 809-831, https://dx.doi.org/10.1111/gcbb.12604, https://hal.inrae.fr/hal-02624640, OA

Toreti A., Belward A., Perez‐dominguez I., Naumann G., Luterbacher J., Cronie O., Seguini L., Manfron G., Lopez-Lozano R., Baruth B. et al. (2019). The Exceptional 2018 European Water Seesaw Calls for Action on Adaptation. Earth's Future, , 12 p., https://dx.doi.org/10.1029/2019EF001170, https://hal.inrae.fr/hal-02629352, OA

Weissteiner C., López-Lozano R., Manfron G., Duveiller G., Hooker J., van Der Velde M., Baruth B. (2019-11). A Crop Group-Specific Pure Pixel Time Series for Europe. Remote Sensing, 11 (22), 2668, https://dx.doi.org/10.3390/rs11222668, https://hal.inrae.fr/hal-02972915, OA

Meroni M., Fasbender D., Lopez-Lozano R., Migliavacca M. (2019). Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model. Remote Sensing, 11 (7), 21 p., https://dx.doi.org/10.3390/rs11070749, https://hal.inrae.fr/hal-02619256, OA

Lopez-Lozano R., Baruth B. (2019). An evaluation framework to build a cost-efficient crop monitoring system. Experiences from the extension of the European crop monitoring system. Agricultural Systems, 168, 231-246, https://dx.doi.org/10.1016/j.agsy.2018.04.002, https://hal.inrae.fr/hal-02619128, OA

Lecerf R., Ceglar A., Lopez-Lozano R., van Der Velde M., Baruth B. (2019). Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe. Agricultural Systems, 168, 191-202, https://dx.doi.org/10.1016/j.agsy.2018.03.002, https://hal.inrae.fr/hal-02628126, OA

Voir aussi

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Date de modification : 06 juin 2024 | Date de création : 12 février 2019 | Rédaction : R. Lopez-Lozano