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23 results on '"Jean-François Dejoux"'

1. Impact of the Russian invasion on wheat biomass in Ukraine

Author

Veronika Antonenko, Ahmad Al Bitar, Iuliia Danylenko, Taeken Wijmer, Jérôme Colin, Jean-François Dejoux, Antoine Lefebvre, Maxime Knibbe, Eric Ceschia, and Simon Gascoin

Subjects
wheat, biomass, Ukraine, agriculture, remote sensing, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract

Ukraine was the seventh-biggest worldwide producer of wheat in 2021 before the Russian invasion. The war caused a disruption of the global wheat market but the impact on the different levels of the production chain remains unclear. Here we leverage high resolution satellite imagery and crop modeling to analyze specifically the impact of the war on the evolution of the dry aboveground biomass in wheat fields. We compare the Kherson region, a battlefield occupied by the Russian army in 2022 with the Poltava region which was not invaded. We also compare the wheat biomass in both regions in 2022 with the two previous years before the war. We find that the wheat biomass sharply declined in the Kherson region in 2022 only. We interpret this result as a consequence of the abandonment of invaded land by farmers. The lack of agricultural management, in particular the lack of fertilization and irrigation could explain the reduced crop growth in summer, thereby contributing to shortages in the wheat production chain.

Published
2024
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2. Forecasting Sunflower Grain Yield by Assimilating Leaf Area Index into a Crop Model

Author

Ronan Trépos, Luc Champolivier, Jean-François Dejoux, Ahmad Al Bitar, Pierre Casadebaig, and Philippe Debaeke

Subjects
remote sensing, data assimilation, sunflower, crop model, leaf area index (LAI), Science
Abstract

Forecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains. With such information, they can optimize their logistics and thus reduce the financial and environmental costs of grain storage. To provide these predictions, data assimilation approaches involving the crop model SUNFLO are used. The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter. They combine the simulation of the crop model and time series of leaf area index (LAI) derived from remote sensors and extracted over 281 fields near Toulouse, France. A sensitivity analysis is used to identify the most relevant model inputs to consider into the data assimilation process. Results show that data assimilation leads to statistically significant better predictions than the simulation alone (from an RMSE of 9.88 q·ha−1 to an RMSE 7.49 q·ha−1). Significant improvement is achieved by relying on smoothed LAI rather than raw LAI. Nevertheless, there is still an over estimation of the grain yield that can be partially explained by the limiting factors observed on the fields and the forecast yield still need improvements to meet the required applications’ accuracy.

Published
2020
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3. Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

Author

Nicolas Karasiak, Jean-François Dejoux, Mathieu Fauvel, Jérôme Willm, Claude Monteil, and David Sheeren

Subjects
tree species, forest, biodiversity, time series, spatial autocorrelation, cross-validation, accuracy, Science
Abstract

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

Published
2019
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4. Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation

Author

David Morin, Milena Planells, Dominique Guyon, Ludovic Villard, Stéphane Mermoz, Alexandre Bouvet, Hervé Thevenon, Jean-François Dejoux, Thuy Le Toan, and Gérard Dedieu

Subjects
forest structure parameters, aboveground biomass, map, forest plantation, Sentinel, ALOS-PALSAR, support vector regression, data combinations, management tools, Science
Abstract

Temperate forests are under climatic and economic pressures. Public bodies, NGOs and the wood industry are looking for accurate, current and affordable data driven solutions to intensify wood production while maintaining or improving long term sustainability of the production, biodiversity, and carbon sequestration. Free tools and open access data have already been exploited to produce accurate quantitative forest parameters maps suitable for policy and operational purposes. These efforts have relied on different data sources, tools, and methods that are tailored for specific forest types and climatic conditions. We hypothesized we could build on these efforts in order to produce a generic method suitable to perform as well or better in a larger range of settings. In this study we focus on building a generic approach to create forest parameters maps and confirm its performance on a test site: a maritime pine (Pinus pinaster) forest located in south west of France. We investigated and assessed options related with the integration of multiple data sources (SAR L- and C-band, optical indexes and spatial texture indexes from Sentinel-1, Sentinel-2 and ALOS-PALSAR-2), feature extraction, feature selection and machine learning techniques. On our test case, we found that the combination of multiple open access data sources has synergistic benefits on the forest parameters estimates. The sensibility analysis shows that all the data participate to the improvements, that reach up to 13.7% when compared to single source estimates. Accuracy of the estimates is as follows: aboveground biomass (AGB) 28% relative RMSE, basal area (BA) 27%, diameter at breast height (DBH) 20%, age 17%, tree density 24%, and height 13%. Forward feature selection and SVR provided the best estimates. Future work will focus on validating this generic approach in different settings. It may prove beneficial to package the method, the tools, and the integration of open access data in order to make spatially accurate and regularly updated forest structure parameters maps effortlessly available to national bodies and forest organizations.

Published
2019
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5. Kalideos OSR MiPy : un observatoire pour la recherche et la démonstration des applications de la télédétection à la gestion des territoires

Author

Jean-François Dejoux, Gérard Dedieu, Olivier Hagolle, Danièle Ducrot, Jean-Claude Menaut, Eric Ceschia, Frédéric Baup, Valérie Demarez, Claire Marais-Sicre, Moamed Kadiri, and Simon Gascoin

Subjects
Instruments and machines, QA71-90, Applied optics. Photonics, TA1501-1820, Cellular telephone services industry. Wireless telephone industry, HE9713-9715
Abstract

Ces dernières années, le CESBIO a mis en place un Observatoire Spatial Régional (OSR), dispositif d'observation couplant mesures de terrain et télédétection dans le sud-ouest de la France. L'OSR se base sur des acquisitions mensuelles de données satellitaires à résolution décamétrique depuis 2002 et sur des sites expérimentaux lourdement instrumentés (mesures en continu de flux d'eau et de carbone) à partir de 2004. Ce dispositif a été reconnu service d'observation par l'INSU/CNRS en 2007 et site Kalideos par le CNES fin 2009 sous le nom de « OSR MiPy ». Le site atelier correspond à l'emprise d'une image Spot, soit environ 50 x 50 km, et couvre une grande diversité de milieux (pédologie, topographie), d'occupation et d'utilisation des sols, de pratiques et de modalités de gestion (agricole, forestière…) et de conditions climatiques (fort gradient de déficits hydriques estivaux). Pour la télédétection, ce site a servi la préparation de SMOS et soutient maintenant en priorité la préparation des missions VENµS et Sentinel-2. Les aspects radar, imagerie thermique et les approches multi-capteurs se développent depuis peu. Le traitement du signal, la physique de la mesure et l'amélioration de la qualité des données constituent le premier axe de recherche. Au niveau thématique, le CESBIO a pour priorité le suivi et la modélisation des agrosystèmes de grandes cultures. L'implication récente d'autres partenaires scientifiques ou de gestionnaires a permis d'initier des travaux sur d'autres aspects, comme la biodiversité, l'aménagement du territoire, le suivi de l'extension urbaine, les risques environnementaux, la santé des forêts, l'enfrichement, la diversité et la productivité des prairies. La valorisation des 10 années d'archives 2002-2011 débute et semble très pertinente pour la caractérisation en haute et en basse résolution des conséquences d'années climatiques atypiques (2003, 2011) sur les éco-agro-systèmes. L'extrapolation des résultats obtenus sur ce site atelier à toute la région Midi-Pyrénées ou à la chaine des Pyrénées est aussi initiée.

Published
2014
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6. Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Author

David Sheeren, Mathieu Fauvel, Veliborka Josipović, Maïlys Lopes, Carole Planque, Jérôme Willm, and Jean-François Dejoux

Subjects
tree species, forest, time series, classification, smoothing, Whittaker, phenology, biodiversity, Science
Abstract

Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0 . 93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

Published
2016
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7. Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

Author

Luc Champolivier, Remy Fieuzal, Aubin Allies, Amanda Veloso, Jean-François Dejoux, Frédéric Baup, Antoine Roumiguie, and Anne Jacquin

Subjects
Synthetic aperture radar, Atmospheric Science, 3D optical data storage, Rapeseed, QC801-809, crop height, Geophysics. Cosmic physics, Sampling (statistics), rapeseed, Normalized Difference Vegetation Index, law.invention, Ocean engineering, law, Environmental science, Sentinel-1, Biomass, Computers in Earth Sciences, Radar, crop monitoring, Landsat-8, plant water content, Water content, TC1501-1800, Remote sensing, Crop season
Abstract

This article aims to evaluate the potential of multitemporal and multiorbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass, and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive normalized difference vegetation index (NDVI), green fraction cover (fCover), and green area index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by synthetic aperture radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these remote sensing indicators (RSI) were physically interpreted, compared with each other to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017–2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement in their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest), and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multiorbital SAR data can be merged without significantly degrading the performance of SAR-based relationships while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring.

Published
2021

8. Sentinel-HR Phase 0 Report

Author

Julien Michel, Olivier Hagolle, Jean-Marc Delvit, Martin Thierry, Lebegue Laurent, David Laubier, Jean Philippe Mallet, Legenne Jérome, Joël Michaud, Vinasco-Salinas Juan, Christelle Iliopoulos, Sébastien Le Corre, Pauline Dusseux, Andreas Max Kääb, Hervé Yesou, Jeremie Mouginot, Stephanie Battiston, Stephen Clandillon, Bergsma, Erwin W. J., Etienne Berthier, Antoine Lucas, Jean-François Dejoux, Gérard Dedieu, Anne Puissant, Sylvain Ferrant, David Sheeren, Antoine Lefevre, Jean-Philippe Cantou, Jean-François Crétaux, Rafael Almar, Camille Lelong, Pierre Maurel, Jean-François Faure, Jean Nabucet, Thomas Corpetti, Pierre-Alexis Herrault, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National d'Études Spatiales [Toulouse] (CNES), Centre National d'Etudes Spatiales - Direction Des Lanceurs. (CNES), Ecole et Observatoire des Sciences de la Terre (EOST), Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Pacte, Laboratoire de sciences sociales (PACTE), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Sciences Po Grenoble - Institut d'études politiques de Grenoble (IEPG), Université Grenoble Alpes (UGA), University of Oslo (UiO), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Kermap [Rennes], Institut National de l'Information Géographique et Forestière [IGN] (IGN), Institut de Recherche pour le Développement (IRD), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), UMR 228 Espace-Dev, Espace pour le développement, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Guyane (UG)-Université des Antilles (UA)-Université de Montpellier (UM), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), Centre National de la Recherche Scientifique (CNRS), CNES - Centre national d'études spatiales, CESBIO, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Météo-France -Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Sciences Po Grenoble - Institut d'études politiques de Grenoble (IEPG ), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Météo-France -Institut de Recherche pour le Développement (IRD)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Empenn, Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (NantesUniv – IGARUN), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (NantesUniv – IGARUN), and Michel, Julien

Subjects
Télédétection, Time archive, [SDU.STU] Sciences of the Universe [physics]/Earth Sciences, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Radiometry
Published
2022

9. Optimal dates for deciduous tree species mapping using full years sentinel-2 time series in south west France

Author

Jean-François Dejoux, David Sheeren, Claude Monteil, Mathieu Fauvel, N. Karasiak, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
lcsh:Applied optics. Photonics, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, Satellite image time series, 02 engineering and technology, lcsh:Technology, 01 natural sciences, Forest, Spatial analysis, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Mathematics, lcsh:T, Phenology, lcsh:TA1501-1820, Vegetation, Spectral bands, Biodiversity, 15. Life on land, Annual cycle, Deciduous, lcsh:TA1-2040, Map, Satellite Image Time Series, France, Sentinel-2, lcsh:Engineering (General). Civil engineering (General), Scale (map), Spatial autocorrelation, Tree species
Abstract

International audience; The free to use Sentinel-2 (S2) sensors with 5-day revisit time at high spatial resolution in 10 spectral bands is a revolution in the remote sensing domain. Including 6 spectral bands in the near infrared, with 3 dedicated for the red-edge (where the vegetation significatively increases), these european satellites are very promising for mapping tree species distribution at a national scale. Here, we study the contribution of three one-year S2 Satellite Image Time Series (SITS) for mapping deciduous species distribution in the southwest of France. The annual cycle of vegetation (called phenology) can contribute to the identification of tree species. For some specific dates, species can have different phenological behaviours (senesence, flowering…). To train and validate the maps, we used the Support Vector Machine algorithm with a spatial cross-validation method. To train the algorithm with the same number of samples per species, we decided to undersample each class to the smallest class using a K-means clustering method. Moreover, a Sequential Feature Selection (SFS) has been implemented to detect the optimal dates per species. Our results are promising with high accuracy for Red oak andWillow (average score of the three one-year respectively F1-Combining double low line-0.99, F1-Combining double low line-0.94) based on the optimal dates. However, it appears that the performances when using the each full SITS are far below the optimal dates models (average ΔF1-Combining double low line-0.32). We did not find, except for Willow and Red oak, that the optimal dates were the same for each year. Perspectives is to find an algorithm robust to temporal or spectral noise and to smooth the time series.

Published
2020
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10. OCL

Author

Jean-Noël Aubertot, Pierre Casadebaig, Luc Champolivier, Philippe Debaeke, Emmanuelle Bret-Mestries, Célia Seassau, Pierre Maury, Jean-François Dejoux, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Terres Inovia, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Ministry in charge of agriculture (DGER), Ministry in charge of agriculture (CASDAR), INRA (Agriculture Scientific Direction, Environment and Agronomy Scientific Division), Region Occitanie, French National Research Agency, PROMOSOL, GIS GCHP2E, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, Decision support system, sunflower, decision support, 030309 nutrition & dietetics, lcsh:TP670-699, Agricultural engineering, Diversification (marketing strategy), 01 natural sciences, Biochemistry, oil concentration, Unit (housing), 03 medical and health sciences, Production (economics), collaborative research, Productivity, 0303 health sciences, agronomy, Sunflower, General partnership, Joint (building), Business, lcsh:Oils, fats, and waxes, Agronomy and Crop Science, 010606 plant biology & botany, Food Science
Abstract

In order to make more efficient plant breeding and gain in competitiveness, the sector of oil-protein crops decided to intensify agronomic research on sunflower crop. The "Sunflower" Joint Technological Unit (Unite Mixte Technologique (UMT) "Tournesol", in French) was launched in the Toulouse area in 2006, associating closely INRA and Terres Inovia. First focused on improving oil production through an agronomic approach, the UMT was renewed in 2011 with a broader partnership and a more assertive orientation towards the development of decision-making tools. The objective of this paper is to highlight the relevance and productivity of this user-oriented research facility. The main results relate to (i) the co-construction of a simulation model (SUNFLO) that can be parameterized and manipulated by Terres Inovia engineers, (ii) the joint exploration of supra-field scales and new methods for agronomic diagnosis and yield forecasting based on remote sensing, (iii) the tuning and dissemination of operational decision rules, (iv) the production of essential knowledge on emergent and/or damaging fungal diseases, as well as on complex interactions between genotype, environment and crop management. After a concluding symposium in 2016, new requests for sunflower research were formulated by the participants. They also advocated for a diversification of crops to consider in order to better meet the needs of the whole oil-protein sector.; In order to make more efficient plant breeding and gain in competitiveness, the sector of oil-protein crops decided to intensify agronomic research on sunflower crop. The "Sunflower" Joint Technological Unit (Unité Mixte Technologique (UMT) "Tournesol", in French) was launched in the Toulouse area in 2006, associating closely INRA and Terres Inovia. First focused on improving oil production through an agronomic approach, the UMTwas renewed in 2011 with a broader partnership and a more assertive orientation towards the development of decision-making tools. The objective of this paper is to highlight the relevance and productivity of this user-oriented research facility. The main results relate to (i) the co-construction of a simulation model (SUNFLO) that can be parameterized and manipulated by Terres Inovia engineers, (ii) the joint exploration of supra-field scales and new methods for agronomic diagnosis and yield forecasting based on remote sensing, (iii) the tuning and dissemination of operational decision rules, (iv) the production of essential knowledge on emergent and/or damaging fungal diseases, as well as on complex interactions between genotype, environment and crop management. After a concluding symposium in 2016, new requests for sunflower research were formulated by the participants. They also advocated for a diversification of crops to consider in order to better meet the needs of the whole oil-protein sector. Keywords: sunflower / collaborative research / agronomy / decision support / oil concentration Résumé-L'agronomie du tournesol : 10 années de recherche en partenariat dans l'UMT Tournesol à Toulouse. Afin de valoriser les avancées en sélection végétale et de gagner en compétitivité, la filière oléoprotéagineuse a jugé nécessaire d'intensifier la recherche agronomique sur le tournesol. L'Unité Mixte Technologique (UMT) « Tournesol » associant étroitement l'INRA et Terres Inovia a été mise en place en région toulousaine en 2006 à Toulouse. Tout d'abord centrée sur l'amélioration de la production d'huile de tournesol par une approche agronomique, elle a bénéficié d'un partenariat élargi lors de son renouvellement en 2011 affirmant une orientation plus marquée vers le développement d'outils pour la décision. Cet article illustre la pertinence et la productivité de ce dispositif en s'appuyant sur quelques résultats significatifs, en particulier : (i) la co-construction d'un modèle de simulation (SUNFLO) paramétrable et manipulable par les ingénieurs de Terres Inovia, (ii) l'exploration en commun des échelles supra-parcellaires et de la télédétection pour le diagnostic agronomique et la prévision des rendements, (iii) la construction et la diffusion de règles de décision opérationnelles, (iv) la production de connaissances sur des maladies émergentes ou nuisibles, ainsi que sur des interactions complexes entre le génotype, l'environnement et la conduite de culture. À l'issue d'un colloque conclusif en 2016, de nouvelles demandes à la recherche ont été formulées. Elles passent également par une diversification des objets d'étude pour mieux répondre aux besoins de la filière oléoprotéagineuse (the full text is available in French on https://www.ocl-journal.org/10.1051/ocl/2020006/olm). Mots clés : tournesol / recherche collaborative / agronomie / aide à la décision / teneur en huile ☆ Contribution to the Topical Issue "Sunflower / Tournesol".

Published
2020
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11. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world

Author

Nicolas Bellemans, Mickael Savinaud, Laurentiu Nicola, Jordi Inglada, Pierre C. Sibiry Traoré, Olivier Hagolle, Souleymane Sidi Traore, Kamal Labbassi, Silvia Valero, Cosmin Udroiu, Sophie Bontemps, Valentine Lebourgeois, Adolph Nyamugama, Andrii Shelestov, Jamal Ezzahar, Gérard Dedieu, Nataliia Kussul, Eric Guzzonato, Pierre Defourny, Agnès Bégué, Norakhan Salh, Cosmin Cara, Benjamin Koetz, Vincent Simonneaux, Terrence Newby, Zhang Miao, Thierry Rabaute, Jean-François Dejoux, Abderrazak El Harti, Earth and Life Institute [Louvain-La-Neuve] (ELI), Université Catholique de Louvain = Catholic University of Louvain (UCL), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), CS-Systèmes d'Information [Toulouse] (CS-SI), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), European Space Agency 'Data User Element' program through the Sen2-Agri project ESRIN 400109979/14/I-AM, Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and UCL - SST/ELI/ELIE - Environmental Sciences

Subjects
010504 meteorology & atmospheric sciences, Computer science, F08 - Systèmes et modes de culture, 0208 environmental biotechnology, Cartographie de l' utilisation des terres, Cloud computing, Cropland, 02 engineering and technology, Performance de culture, 01 natural sciences, Agriculture monitoring, 2. Zero hunger, Environmental resource management, Geology, Transparency (graphic), [SDE]Environmental Sciences, Crop type mapping, Exploit, Télédétection, Soil Science, Surveillance des cultures, Machine learning, Computers in Earth Sciences, Observation satellitaire, 0105 earth and related environmental sciences, Remote sensing, business.industry, A01 - Agriculture - Considérations générales, 15. Life on land, 020801 environmental engineering, Data access, 13. Climate action, Agriculture, Système d'exploitation agricole, Satellite, Sentinel-2, U30 - Méthodes de recherche, business, Scale (map), Cropping
Abstract

International audience; The convergence of new EO data flows, new methodological developments and cloud computing infrastructure calls for a paradigm shift in operational agriculture monitoring. The Copernicus Sentinel-2 mission providing a systematic 5-day revisit cycle and free data access opens a completely new avenue for near real-time crop specific monitoring at parcel level over large countries. This research investigated the feasibility to propose methods and to develop an open source system able to generate, at national scale, cloud-free composites, dynamic cropland masks, crop type maps and vegetation status indicators suitable for most cropping systems. The so-called Sen2-Agri system automatically ingests and processes Sentinel-2 and Landsat 8 time series in a seamless way to derive these four products, thanks to streamlined processes based on machine learning algorithms and quality controlled in situ data. It embeds a set of key principles proposed to address the new challenges arising from countrywide 10 m resolution agriculture monitoring. The full-scale demonstration of this system for three entire countries (Ukraine, Mali, South Africa) and five local sites distributed across the world was a major challenge met successfully despite the availability of only one Sentinel-2 satellite in orbit. In situ data were collected for calibration and validation in a timely manner allowing the production of the four Sen2-Agri products over all the demonstration sites. The independent validation of the monthly cropland masks provided for most sites overall accuracy values higher than 90%, and already higher than 80% as early as the mid-season. The crop type maps depicting the 5 main crops for the considered study sites were also successfully validated: overall accuracy values higher than 80% and F1 Scores of the different crop type classes were most often higher than 0.65. These respective results pave the way for countrywide crop specific monitoring system at parcel level bridging the gap between parcel visits and national scale assessment. These full-scale demonstration results clearly highlight the operational agriculture monitoring capacity of the Sen2-Agri system to exploit in near real-time the observation acquired by the Sentinel-2 mission over very large areas. Scaling this open source system on cloud computing infrastructure becomes instrumental to support market transparency while building national monitoring capacity as requested by the AMIS and GEOGLAM G-20 initiatives.

Published
2019
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12. A snow cover climatology for the Pyrenees from MODIS snow products

Author

Mireille Huc, R Sánchez, Renaud Marti, Camille Szczypta, Simon Gascoin, Olivier Hagolle, Jean-François Dejoux, Lionel Jarlan, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Géographie de l'environnement (GEODE), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Confederación hidrográfica del Ebro - CHE (SPAIN), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, 0207 environmental engineering, 02 engineering and technology, Land cover, lcsh:Technology, 01 natural sciences, lcsh:TD1-1066, Hydrology (agriculture), medicine, Satellite imagery, lcsh:Environmental technology. Sanitary engineering, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, lcsh:T, lcsh:Geography. Anthropology. Recreation, Elevation, Vegetation, 15. Life on land, Snowpack, Seasonality, medicine.disease, Snow, lcsh:G, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, 13. Climate action, Climatology, Environmental science
Abstract

The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50% of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Published
2015
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13. Assessment of a Markov logic model of crop rotations for early crop mapping

Author

Julien Osman, Jean-François Dejoux, Jordi Inglada, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Markov Logic Networks, 010504 meteorology & atmospheric sciences, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Agricultural engineering, Horticulture, Logic model, 01 natural sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], early crop type mapping, crop rotations, Information system, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, Warning system, Land use, Agroforestry, business.industry, Frame (networking), Forestry, 15. Life on land, Crop rotation, Field (geography), Computer Science Applications, Agriculture, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Animal Science and Zoology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, Agronomy and Crop Science
Abstract

A prediction model for crop rotations is proposed.The model uses machine learning techniques applied to historic data.It allows the introduction of expert knowledge without re-learning from data.The model is assessed on real data over several years and a large area. Detailed and timely information on crop area, production and yield is important for the assessment of environmental impacts of agriculture, for the monitoring of the land use and management practices, and for food security early warning systems. A machine learning approach is proposed to model crop rotations which can predict with good accuracy, at the beginning of the agricultural season, the crops most likely to be present in a given field using the crop sequence of the previous 3-5years. The approach is able to learn from data and to integrate expert knowledge represented as first-order logic rules. Its accuracy is assessed using the French Land Parcel Information System implemented in the frame of the EU's Common Agricultural Policy. This assessment is done using different settings in terms of temporal depth and spatial generalization coverage. The obtained results show that the proposed approach is able to predict the crop type of each field, before the beginning of the crop season, with an accuracy as high as 60%, which is better than the results obtained with current approaches based on remote sensing imagery.

Published
2015
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14. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications

Author

Jean-François Dejoux, Milena Planells, Thuy Le Toan, Amanda Veloso, Stéphane Mermoz, Alexandre Bouvet, Eric Ceschia, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Soil Science, Climate change, Crops, 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, law.invention, Crop, law, optical data, [SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture, Computers in Earth Sciences, Radar, Remotely sensing, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Food security, business.industry, [SDE.IE]Environmental Sciences/Environmental Engineering, Geology, 15. Life on land, Multi-temporal Sentinel-1 data, 13. Climate action, Agriculture, Remote sensing (archaeology), Environmental science, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Crop monitoring information is essential for food security and to improve our understanding of the role of agriculture on climate change, among others. Remotely sensing optical and radar data can help to map crop types and to estimate biophysical parameters, especially with the availability of an unprecedented amount of free Sentinel data within the Copernicus programme. These datasets, whose continuity is guaranteed up to decades, offer a unique opportunity to monitor crops systematically every 5 to 10 days. Before developing operational monitoring methods, it is important to understand the temporal variations of the remote sensing signal of different crop types in a given region. In this study, we analyse the temporal trajectory of remote sensing data for a variety of winter and summer crops that are widely cultivated in the world (wheat, rapeseed, maize, soybean and sunflower). The test region is in southwest France, where Sentinel-1 data have been acquired since 2014. Because Sentinel-2 data were not available for this study, optical satellites similar to Sentinel-2 are used, mainly to derive NDVI, for a comparison between the temporal behaviors with radar data. The SAR backscatter and NDVI temporal profiles of fields with varied management practices and environmental conditions are interpreted physically. Key findings from this analysis, leading to possible applications of Sentinel-1 data, with or without the conjunction of Sentinel-2, are then described. This study points out the interest of SAR data and particularly the VH/VV ratio, which is poorly documented in previous studies.

Published
2017
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15. Télédétection de la trame verte arborée en haute résolution par morphologie mathématique

Author

Fanny Rhoné, Eric Maire, Jean-François Dejoux, Gérard Dedieu, Claire Marais-Sicre, Sylvain Guillerme, Géographie de l'environnement (GEODE), Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
politiques publiques environnementales, télédétection, 0211 other engineering and technologies, Trame verte arborée, 021107 urban & regional planning, [SHS.GEO]Humanities and Social Sciences/Geography, 02 engineering and technology, General Medicine, 010501 environmental sciences, 01 natural sciences, transformation chapeau haut de forme, morphologie mathématique, arbre hors forêt, haie, 0105 earth and related environmental sciences
Abstract

International audience; Cet article pointe d'abord les causes et/es enjeux des verrous cartographiques dans !a mise en oeuvre de la politique de la trame verte et bleue (TVB). Une connaissance de l'emprise précise de la trame verte arborée par les acteurs locaux apparaît incontournable. Nous proposons ensuite une méthode de télédétection associée à des algorithmes de morphologie mathématique pour extraire la trame verte arborée à l'échelle métrique à partir d'une image à très haute résolution spatiale. La méthode proposée est une séquence de quatre étapes : 1) analyse en composantes principales (ACP), 2) segmentation par la transformation chapeau haut de forme, 3) élimination du bruit morphologique, 4) restauration des contours arborés par dilatation géodésique. Enfin, une discussion sur les perspectives en termes de conséquences sur les méthodologies de télédétection et sur les politiques publiques environnementales termine l'article.

Published
2012
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16. Tree species classification in temperate forests using formosat-2 satellite image time series

Author

Jean-François Dejoux, Veliborka Josipović, David Sheeren, Mailys Lopes, Carole Planque, Mathieu Fauvel, Jérôme Willm, Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Sheeren, David, Centre National d'Études Spatiales - CNES (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Institut de Recherche pour le Développement - IRD (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), and Institut National Polytechnique de Toulouse - INPT (FRANCE)

Subjects
Time series, 010504 meteorology & atmospheric sciences, Biodiversité et Ecologie, Science, Multispectral image, Forest management, 0211 other engineering and technologies, whittaker, 02 engineering and technology, Overfitting, 01 natural sciences, phenology, Biodiversity and Ecology, forest, Temperate forest, espèce arborée, tree species, Forest ecology, time series, classification, smoothing, biodiversity, Forest, Milieux et Changements globaux, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Whittaker, Hyperspectral imaging, Biodiversity, 15. Life on land, Classification, Geography, Phenology, 13. Climate action, General Earth and Planetary Sciences, Satellite Image Time Series, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Temperate rainforest, Smoothing, Tree species, forêt tempérée
Abstract

International audience; Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ=0.93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

Published
2016
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17. Evaluation of environmentally-friendly crop management systems based on very early sowing dates for winter oilseed rape in France

Author

Romain Roche, Jean-François Dejoux, Raymond Reau, Patrick Saulas, and Jean-Marc Meynard

Subjects
2. Zero hunger, 0106 biological sciences, Nitrogen management, Forestry, 04 agricultural and veterinary sciences, Nitrate leaching, Semis, 01 natural sciences, Plant production, Political science, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Crop management, Agronomy and Crop Science, Inorganic nitrogen, 010606 plant biology & botany
Abstract

Notre objectif est l'evaluation de nouveaux itineraires techniques du colza bases sur des semis tres precoces, afin d'ameliorer le bilan environnemental de cette culture sans affecter son bilan economique. Sur un reseau de 36 essais pendant 3 annees en France, les nouveaux itineraires techniques se sont averes plus efficaces que les itineraires techniques actuellement recommandes au niveau des performances environnementales : absorption, au cours de l'automne, de la quasi totalite de l'azote mineral du sol meme apres epandage d'effluent organique ; teneur en nitrate de l'eau percolee inferieure a 50 mg.l -1 ; reduction de l'emploi de molluscicides, meme en annee de forte infestation de limaces. En terme de marge brute, les resultats moyens furent favorables aux semis tres precoces qui permirent, sur la majorite des essais, un rendement superieur ou egal a celui des semis a date normale. Une typologie des essais reliant la difference de rendement entre les deux itineraires techniques a differents facteurs limitants (implantation de la culture, phoma, nutrition azotee), est utilisee afin de definir le domaine de validite des nouveaux itineraires techniques.

Published
2003
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18. Impact of climate and land cover changes on snow cover in a small Pyrenean catchment

Author

Camille Szczypta, C Vigneau, Jean-François Dejoux, Simon Gascoin, Olivier Hagolle, Pascal Fanise, Thomas Houet, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Géographie de l'environnement (GEODE), Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0010,DRIIHM / IRDHEI,Dispositif de recherche interdisciplinaire sur les Interactions Hommes-Milieux(2011), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse - Jean Jaurès (UT2J), and Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Labex DRIIHM, Pyrenees, Climate change, Land cover, Snow field, OHM Haut-Vicdessos, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Snowpack, Snow, [SDE.ES]Environmental Sciences/Environmental and Society, Land cover change, climate change, Observatoires Hommes-Millieux, 13. Climate action, Climatology, land caver change, Environmental science, Climate model, Precipitation, Meltwater, Snow cover, Water Science and Technology
Abstract

International audience; The seasonal snow in the Pyrenees Mountains is an essential source of runoff for hydropower production and crop irrigation in Spain and France. The Pyrenees are expected to undergo strong environmental perturbations over the 21st century because of climate change (rising temperatures) and the abandonment of agro-pastoral areas (reforestation). Both changes are happening at similar timescales and are expected to have an impact on snow cover. The effect of climate change on snow in the Pyrenees is well understood , but the effect of land cover changes is much less documented. Here, we analyze the response of snow cover to a combination of climate and land cover change scenarios in a small Pyrenean catchment (Bassiès, 14.5 km 2 , elevation range 940–2651 m a.s.l.) using a distributed snowpack evolution model. Climate scenarios were constructed from the output of regional climate model projections, whereas land cover scenarios were generated based on past observed changes and an inductive pattern-based model. The model was validated over a snow season using in situ snow depth measurements and high-resolution snow cover maps derived from SPOT (Satellite Pour l'Observation de la Terre – Earth Observation Satellite) satellite images. Model projections indicate that both climate and land cover changes reduce the mean snow depth. However, the impact on the snow cover duration is moderated in reforested areas by the shading effect of trees on the snow surface radiation balance. Most of the significant changes are expected to occur in the transition zone between 1500 m a.s.l. and 2000 m a.s.l. where (i) the projected increase in air temperatures decreases the snow fraction of the precipitation and (ii) the land cover changes are concentrated. However, the consequences on the runoff are limited because most of the meltwater originates from high-elevation areas of the catchment, which are less affected by climate change and reforestation.

Published
2015
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19. [Untitled]

Author

Philippe Leterme, Jean-Marc Meynard, Jean-François Dejoux, Sylvie Recous, and Isabelle Trinsoutrot

Subjects
Rapeseed, Soil Science, chemistry.chemical_element, Sowing, Lessivage, Plant Science, Mineralization (soil science), Nitrogen, chemistry.chemical_compound, Nitrate, chemistry, Agronomy, Leaching (agriculture), Nitrogen cycle
Abstract

For environmental purposes, very early sowing of winter rapeseed may reduce winter nitrate leaching thanks to the high N uptake capacities of rapeseed in autumn. However, freezing could lead to high losses of leaf nitrogen, amounting to more than 100 kg N ha-1 (Dejoux et al., 1999). Here we investigated the agronomic and environmental consequences of the decomposition of fallen leaves, based on field and laboratory studies with 15N labeled leaves (C:N=9). The potential kinetics of decomposition of leaves were measured by incubation in the laboratory. In the field, all leaves were removed at beginning of winter and replaced by labeled leaves, artificially frozen at −15°C , which were laid on the soil surface. Compared on a thermal time basis, decomposition proceeded as quickly in the field as in the incubations and was complete after 116 normalized days at 15 °C. The proportion of 15N derived from labeled leaves, absorbed again by the rape plants, was 28% at flowering and 24% at harvest. This high N recovery is assumed to result from the synchronization of leaves decomposition and active N absorption by rape in spring. Leaf N mineralization did not increase soil N mineral content at flowering or at harvest, but we observed a 40% loss of 15N. As no leaching was simulated, this loss was supposed to be gaseous. Such a high percentage could be explained by the fact that the decomposing leaves lay on the soil surface, and by climatic conditions conducive to such emissions. For environmental purposes, the quantity and nature of these gaseous N emissions have to be studied for other climatic conditions and types of leaves. As a proportion of N is reabsorbed, N fertilizer application rates could be reduced accordingly.

Published
2000
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20. Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

Author

Aubin Allies, Antoine Roumiguie, Remy Fieuzal, Jean-Francois Dejoux, Anne Jacquin, Amanda Veloso, Luc Champolivier, and Frederic Baup

Subjects
Crop modeling, dry mass (DM), green area index (GAI), Landsat-8, optical, rapeseed, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

This article investigates the potential of the assimilation of both synthetic aperture radar (SAR)-derived dry mass (DM) and optically derived green area index (GAI) in an agro-meteorological model (i.e., SAFY) for a better rapeseed crops modeling. The GAI was derived from both 566 Sentinel-2 and 149 Landsat-8 images, whereas DM was derived from 884 Sentinel-1 images acquired from six different orbits. The ground data were collected during 3 agricultural years on 43 rapeseed fields located in three study areas in France with contrasted pedoclimatic conditions. Results show that the temporal evolutions of both DM and GAI can be accurately simulated over the 43 monitored rapeseed fields (R2 = 0.84 and 0.92, respectively, and relative root mean square error, RMSEr = 41% and 28%, respectively) for the best satellite configuration. Tested assimilation scenarios reveal that the concomitant assimilation of SAR and optical data allows a significant better control of the model than the assimilation of SAR or optical data alone. Small differences in the simulations of the model are observed when it is controlled by either multisensor optical and/or multiorbital SAR data or monosensor optical and/or mono-orbital SAR data. The discrepancies of performance between fields push toward the strengthening of this study by considering other rapeseed fields with ground observations acquired worldwide for the calibration of SAR-derived DM. These results are however promising in view of the development of a near-real time assimilation scheme as a decision support tool for farmers and decision makers.

Published
2022
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21. Kalideos OSR MiPy : un observatoire pour la recherche et la démonstration des applications de la télédétection à la gestion des territoires

Author

Mohamed Kadiri, Eric Ceschia, Valérie Demarez, Frédéric Baup, Danielle Ducrot, Claire Marais-Sicre, Jean-Claude Menaut, Olivier Hagolle, Jean-François Dejoux, Gérard Dedieu, Simon Gascoin, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
QA71-90, 010504 meteorology & atmospheric sciences, [SDE.MCG]Environmental Sciences/Global Changes, 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, Instruments and machines, Computer Science Applications, TA1501-1820, HE9713-9715, Applied optics. Photonics, Electrical and Electronic Engineering, 020701 environmental engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Cellular telephone services industry. Wireless telephone industry, 0105 earth and related environmental sciences
Abstract

International audience; Ces dernières années, le CESBIO a mis en place un Observatoire Spatial Régional 'OSR' : un dispositif d'observation couplant mesures de terrain et télédétection dans le sud-ouest de la France. L'OSR se base sur des acquisitions mensuelles de données satellitaires à résolution décamétrique depuis 2002 et sur des sites expérimentaux lourdement instrumentés (mesures en continu de flux d'eau et de carbone) à partir de 2004. Ce dispositif a été reconnu service d'observation par l'INSU/CNRS en 2007 et site KALIDEOS par le CNES fin 2009 : 'KALIDEOS OSR MiPy'. Le site atelier correspond à une emprise d'image SPOT, soit environ 50x50 km et couvre une grande diversité de milieux (pédologie, topographie), d'occupation et d'utilisation des sols, de pratiques et de modalités de gestion (agricole, forestière...) et de conditions climatiques (fort gradient de déficits hydriques estivaux). Pour la télédétection, ce site a servi la préparation de SMOS, et il soutient maintenant en priorité à la préparation des missions VENμS et Sentinel-2. Les aspects radar, imagerie thermique et les approches multi-capteurs se développent depuis peu. Le traitement du signal, la physique de la mesure et l'amélioration de la qualité des données constituent le premier axe de recherche. Au niveau thématique, le CESBIO a pour priorité les suivis et les modélisations des agrosystèmes de grandes cultures. L'implication récente d'autres partenaires scientifiques ou gestionnaires a permis d'initier des travaux sur d'autres aspects, comme la biodiversité, l'aménagement du territoire, le suivi de l'extension urbaine, les risques environnementaux, la santé des forêts, l'enfrichement, la diversité et la productivité des prairies. La valorisation des 10 années d'archives 2002-2011 débute et semble très pertinente pour la caractérisation en haute et en basse résolution des conséquences d'années climatiques atypiques (2003, 2011) sur les éco-agro-systèmes. L'extrapolation des résultats obtenus sur ce site atelier à toute la région Midi-Pyrénées ou à la chaine des Pyrénées est aussi initiée.

Published
2012

22. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data

Author

Valérie Demarez, Danielle Ducrot, Pierre Béziat, Eric Ceschia, Gérard Dedieu, Martin Claverie, Mireille Huc, Benoît Duchemin, Pascal Keravec, Claire Marais-Sicre, Remy Fieuzal, Jean-François Dejoux, Olivier Hagolle, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
010504 meteorology & atmospheric sciences, Soil Science, Context (language use), 01 natural sciences, Crop, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Helianthus annuus, LUE, Computers in Earth Sciences, Crop model, 0105 earth and related environmental sciences, Remote sensing, Formosat-2, 2. Zero hunger, Biomass (ecology), High spatial and temporal resolution remote, Phenology, Geology, 04 agricultural and veterinary sciences, 15. Life on land, High spatial and temporal resolution remote sensing data, sensing data, Sunflower, Maize, Data set, Dry aboveground biomass, Temporal resolution, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Light-use efficiency, Multitemporal green area index
Abstract

The recent availability of high spatial and temporal resolution (HSTR) remote sensing data (Formosat-2, and future missions of Ven mu s and Sentinel-2) offers new opportunities for crop monitoring. In this context, we investigated the perspective offered by coupling a simple algorithm for yield estimate (SAFY) with the Formosat-2 data to estimate crop production over large areas. With a limited number of input parameters, the SAFY model enables the simulation of time series of green area index (GAI) and dry aboveground biomass (DAM). From 2006 to 2009, 95 Formosat-2 images (8 m, 1 day revisit) were acquired for a 24 x 24 km(2) area southwest of Toulouse, France. This study focused on two summer crops: irrigated maize (Zea mays) and sunflower (Helianthus annuus). Green area index (GAI) time series were deduced from Formosat-2 NDVI time series and were used to calibrate six major parameters of the SAFY model. Four of those parameters (partition-to-leaf and senescence function parameters) were calibrated per crop type based on the very dense 2006 Formosat-2 data set The retrieved values of these parameters were consistent with the in situ observations and a literature review. Two of the major parameters of the SAFY model (emergence day and effective light-use efficiency) were calibrated per field relative to crop management practices. The estimated effective light-use efficiency values highlighted the distinction between the C4 (maize) and 0 (sunflower) plants, and were linked to the reduction of the photosynthesis rate due to water stress. The model was able to reproduce a large set of GAI temporal shapes, which were related to various phenological behaviours and to crop type. The biomass was well estimated (relative error of 28%), especially considering that biomass measurements were not used for the calibration. The grain yields were also simulated using harvest index coefficients and were compared with grain yield statistics from the French Agricultural Statistics for the department of Haute-Garonne. The inter-annual variation in the simulated grain yields of sunflower was consistent with the reported variation. For maize, significant discrepancies were observed with the reported statistics.

Published
2012
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23. Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

Author

Aubin Allies, Antoine Roumiguie, Jean-Francois Dejoux, Remy Fieuzal, Anne Jacquin, Amanda Veloso, Luc Champolivier, and Frederic Baup

Subjects
Biomass, crop height, crop monitoring, Landsat-8, plant water content, rapeseed, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

This article aims to evaluate the potential of multitemporal and multiorbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass, and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive normalized difference vegetation index (NDVI), green fraction cover (fCover), and green area index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by synthetic aperture radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these remote sensing indicators (RSI) were physically interpreted, compared with each other to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017–2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement in their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest), and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multiorbital SAR data can be merged without significantly degrading the performance of SAR-based relationships while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring.

Published
2021
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