M. Akoudjin, S. Kiéma, M. Sangare, J. César, J. Bouyer et al., , 2016.

, Influence des activités agricoles sur la végétation le long d'un gradient pluviométrique nordsud du Burkina Faso. VertigO-La Revue Électronique En Sciences de L'environnement, p.16

,

N. Alexandratos, J. ;. Et-bruinsma, and R. Fao, , 2012.

O. Arino, D. Gross, F. Ranera, M. Leroy, P. Bicheron et al., GlobCover: ESA service for Global land cover from MERIS, International Geoscience and Remote Sensing Symposium (IGARSS), pp.2412-2415, 2007.

,

D. Arvor, M. Jonathan, M. S. Meirelles, V. Dubreuil, and L. Et-durieux, Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil. International Journal of Remote Sensing, vol.32, issue.22, pp.7847-7871, 2011.
URL : https://hal.archives-ouvertes.fr/halshs-00623706

D. Arvor, M. Meirelles, V. Dubreuil, A. Bégué, and Y. E. Et-shimabukuro, Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices, Applied Geography, vol.32, issue.2, pp.702-713, 2012.
URL : https://hal.archives-ouvertes.fr/cirad-00820484

C. Atzberger, Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs, Remote Sensing, vol.5, issue.2, pp.949-981, 2013.

,

M. Baatz and A. Et-schäpe, Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation, Angewandte Geographische Informationsverarbeitung XII, vol.58, pp.12-23, 2000.

. Bafd, Banque africaine de développement (BAfD), Organisation de coopération et de développement économiques (OCDE), Programme des Nations Unies pour le développement (PNUD), 2015.

, Références bibliographiques

E. Barona, N. Ramankutty, G. Hyman, and O. T. Et-coomes, The role of pasture and soybean in deforestation of the Brazilian Amazon, Environmental Research Letters, vol.5, issue.2, 2010.

,

P. A. Barros-de-sousa, R. S. Borges, and R. Et-ribeiro-dias, Atlas do Tocantins: subsídios ao planejamento da gestão territorial, 2012.

A. Bégué, D. Arvor, B. Bellon, J. Betbeder, D. De-abelleyra et al., Remote Sensing and Cropping Practices: A Review, Remote Sensing, vol.10, issue.1, p.99, 2018.

A. Bégué, D. Arvor, C. Lelong, E. Vintrou, and M. Et-simoes, Agricultural Systems Studies Using Remote Sensing, Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, pp.113-130, 2015.

A. Bégué, L. Leroux, D. Lo-seen, J. P. Tonneau, and P. Et-morant, Observation spatiale pour l'agriculture en Afrique: potentiels et défis, Notes techniques: AFD, 12, 2016.

U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Et-heynen, Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS Journal of Photogrammetry and Remote Sensing, 2004.

A. Bey, . Sánchez-paus, A. Díaz, D. Maniatis, G. Marchi et al., Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation, Remote Sensing, vol.8, issue.10, 2016.

R. Biancalani, F. Nachtergaele, M. Petri, and S. Et-bunning, Land degradation assessment in drylands: methodology and results, 2013.

M. Bisquert, A. Bégué, and M. Et-deshayes, Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series, International Journal of Applied Earth Observation and Geoinformation, vol.37, pp.72-82, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02602428

M. Bisquert, A. Bégué, M. Deshayes, and D. Et-ducrot, Environmental evaluation of MODIS-derived land units. GIScience et Remote Sensing, vol.54, pp.64-77, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02606011

,

M. Bisquert, G. Bordogna, A. Bégué, G. Candiani, M. Teisseire et al., A simple fusion method for image time series based on the estimation of image temporal validity, Remote Sensing, vol.7, issue.1, pp.704-724, 2015.

R. Bivand, T. Keitt, and B. Et-rowlingson, rgdal: Bindings for the Geospatial Data Abstraction Library, 2016.

R. Bivand and N. Et-lewin-koh, maptools: Tools for Reading and Handling Spatial Objects, 2016.

T. Blaschke, G. J. Hay, M. Kelly, S. Lang, P. Hofmann et al., , 2014.

, Geographic Object-Based Image Analysis -Towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing : Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), vol.87, pp.180-191

,

T. Blaschke, K. Johansen, and D. Et-tiede, Object-Based Image Analysis for Vegetation Mapping and Monitoring, Advances in Environmental Remote Sensing: Sensor, Algorithms, and Applications, pp.241-271, 2011.

É. L. Bolfe, D. D. Victória, E. Contini, G. Bayma-silva, L. Spinelli-araujo et al., Matopiba em crescimento agrícola Aspectos territoriais e socioeconômicos, Revista de Política Agrícola, vol.25, issue.4, pp.38-62, 2016.

S. Bonny, L'intensification écologique de l'agriculture : voies et défis, Innovation and Sustainable Development in Agriculture and Food Symposium, pp.1-11, 2010.

E. Borghi, A. Luchiari-junior, J. C. Avanzi, L. Bortolon, E. S. Oliveira-bortolon et al., Estado da arte da agricultura e pecuária do estado do Tocantins. Embrapa Pesca e Aquicultura. Documentos 13. Palmas. Consulté à l'adresse, 2015.

C. Boryan, Z. Yang, R. Mueller, and M. Craig, Monitoring US agriculture: The US department of agriculture, national agricultural statistics service, cropland data layer program, Geocarto International, vol.26, issue.5, pp.341-358, 2011.

J. Bouma, The land use systems approach to planning sustainable land management at several scales, ITC Journal, issue.3/4, pp.237-242, 1997.

É. Bourget and L. Et-le-dû-blayo, Définition d'unités paysagères par télédétection en Bretagne: méthodes et critiques, Norois, issue.3, pp.69-83, 2010.

A. Bridhikitti and T. J. Et-overcamp, Estimation of Southeast Asian rice paddy areas with different ecosystems from moderate-resolution satellite imagery. Agriculture, Ecosystems and Environment, vol.146, pp.113-120, 2012.

J. Brossier, Système et système de production, Cahiers Des Sciences Humaines, vol.23, pp.377-390, 1987.

J. C. Brown, J. H. Kastens, A. C. Coutinho, C. Victoria, and C. R. Bishop, Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data, 2013.

, Références bibliographiques

J. Bruinsma, The resources outlook: by how much do land, water and crop yields need to increase by 2050?, Looking ahead in world food and agriculture: perspectives to 2050, pp.233-278, 2011.

B. E. Bunker, J. A. Tullis, J. D. Cothren, J. Casana, and M. H. Et-aly, Object-based Dimensionality Reduction in Land Surface Phenology Classification, AIMS Geosciences, vol.2, issue.4, pp.302-328, 2016.

P. Burger, S. Berton, R. Billaz, and A. Et-lebreton, Agroécologie, une transition vers des modes de vie et de développement viables. Paroles d'acteurs, Groupe de Travail Désertification, pp.1-93, 2011.

L. Busetto and L. Et-ranghetti, MODIStsp: An R package for automatic preprocessing of MODIS Land Products time series, Computers et Geosciences, vol.97, pp.40-48, 2016.

H. Cai, S. Zhang, K. Bu, J. Yang, and L. Chang, Integrating geographical data and phenological characteristics derived from MODIS data for improving land cover mapping, Journal of Geographical Sciences, vol.21, issue.4, pp.705-718, 2011.

E. Cano, J. Denux, M. Bisquert, L. Hubert-moy, and V. Et-chéret, Improved forest-cover mapping based on MODIS time series and landscape stratification, International Journal of Remote Sensing, vol.38, issue.7, pp.1865-1888, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01600887

M. J. Cheema and W. G. Et-bastiaanssen, Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis, Agricultural Water Management, vol.97, issue.10, pp.1541-1552, 2010.

,

J. Chen, J. Chen, A. Liao, X. Cao, L. Chen et al., Global land cover mapping at 30 m resolution: A POK-based operational approach, ISPRS Journal of Photogrammetry and Remote Sensing, vol.103, pp.7-27, 2015.

,

J. Chen, P. Jönsson, M. Tamura, Z. Gu, B. Matsushita et al., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter, 2004.

, , vol.91, pp.332-344

J. Cihlar, Land cover mapping of large areas from satellites: Status and research priorities, International Journal of Remote Sensing, vol.21, issue.6, pp.1093-1114, 2000.

,

. Cilss, Les Paysages de l'Afrique de l'Ouest: Une Fenêtre sur un monde en pleine évolution, 2016.

, Geological Survey Earth Resources Observation and Science

, Références bibliographiques

J. Cohen, A coefficient of agreement for nominal scales, Educational and Psychological Measurement, vol.20, issue.1, pp.37-46, 1960.

Y. Collin, Rapport d'information n°504 fait au nom de la délégation sénatoriale à la prospective sur le défi alimentaire à l'horizon 2050. Sénat, Délégation à la prospective, 2012.

F. Colson, J. Bogaert, and R. Et-ceulemans, Fragmentation in the Legal Amazon, Brazil: Can landscape metrics indicate agricultural policy differences? Ecological Indicators, vol.11, pp.1467-1471, 2011.

. Conab, Soja: série histórica de produtividade (safras, Disponible à l'adresse, 2000.

, Convention européenne du paysage. Série Des Traités Européens -N° 176, 2000.

S. Corgne, L. Hubert-moy, and J. Et-betbeder, Monitoring of Agricultural Landscapes Using Remote Sensing Data, Land Surface Remote Sensing in Agriculture and Forest, pp.221-247, 2016.

,

C. S. Daughtry, P. C. Doraiswamy, E. R. Hunt, A. J. Stern, I. Mcmurtrey et al., Remote sensing of crop residue cover and soil tillage intensity. Soil and Tillage Research, vol.91, pp.101-108, 2006.

C. A. De-almeida, Paysage des systèmes de production agropastoraux de l'État du Rondônia-Amazonie brésilienne, 2016.

C. A. De-almeida, A. C. Coutinho, J. C. Esquerdo, M. Adami, A. Venturieri et al., High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data, Acta Amazonica, vol.46, issue.3, pp.291-302, 2016.

C. A. De-almeida, M. Mourão, N. Dessay, A. Lacques, A. Monteiro et al., Typologies and Spatialization of Agricultural Production Systems in Rondônia, vol.5, 2016.

T. I. De-almeida, N. C. Penatti, L. G. Ferreira, A. E. Arantes, and C. H. Amaral, Principal component analysis applied to a time series of MODIS images: the spatio-temporal variability of the Pantanal wetland, Brazil. Wetlands Ecology and Management, vol.23, issue.4, pp.737-748, 2015.

C. A. Références-bibliographiques-de-bie, Comparative performance analysis of agro-ecosystems, 2000.

E. E. De-miranda, L. A. Magalhães, and C. A. Et-de-carvalho, Proposta de delimitação territorial do Matopiba. Nota Técnica 1. Campinas, São Paulo, 2014.

O. De-schutter, Report presented at the 16th Session of the United Nations Human Rights Council, 2010.

. Consulté,

C. H. De-souza, W. R. Cervi, J. C. Brown, J. V. Rocha, and R. A. Et-lamparelli, Mapping and evaluating sugarcane expansion in Brazil's savanna using MODIS and intensity analysis: a case-study from the state of Tocantins, Journal of Land Use Science, vol.12, issue.6, pp.457-476, 2017.

J. Deffontaines, Analyse du paysage et étude régionale des systèmes de production agricole, Economie Rurale, vol.98, pp.3-13, 1973.

J. Deffontaines, Du paysage comme moyen de connaissance de l'activité agricole à l'activité agricole comme moyen de production du paysage, Thème et variations: nouvelles recherches rurales au sud, pp.305-322, 1997.

J. Deffontaines, C. Thenail, and J. Et-baudry, Agricultural systems and landscape patterns: how can we build a relationship?, Landscape and Urban Planning, vol.31, issue.1-3, pp.3-10, 1995.
URL : https://hal.archives-ouvertes.fr/hal-02700701

, , pp.1031-1034

Y. Dembélé, Cartographie des zones socio-rurales : un outil d'aide à la planification pour la gestion de l'eau en agriculture, 2010.

. Dgess/maah, Rapport général des résultats définitifs de la campagne agricole 2016/2017 et des perspectives de la situation alimentaire et nutritionnelle, 2017.

V. Dheeravath, P. S. Thenkabail, G. Chandrakantha, P. Noojipady, G. P. Reddy et al., Irrigated areas of India derived using MODIS 500 m time series for the years, ISPRS Journal of Photogrammetry and Remote Sensing, vol.65, issue.1, pp.42-59, 2001.

,

D. Gregorio, A. Et-jansen, and L. J. , Land Cover Classification System (LCCS): Classification Concepts and User Manual. FAO, vol.53, 2000.

J. Dixon, A. Gulliver, and D. Et-gibbon, Farming Systems and Poverty: Improving Farmers' Livelihoods in a Changing World, FAO et World Bank, 2001.

G. Fischer, E. Hizsnyik, and D. Et-wiberg, Scarcity and abundance of land resources: competing uses and the shrinking land resource base, 2011.

G. Fischer, F. Nachtergaele, S. Prieler, H. T. Van-velthuizen, L. Verelst et al., , 2012.

, Global Agro-ecological Zones (GAEZ v3.0) -Model Documentation. IIASA

G. Fischer, H. Velthuizen, . Van, M. Shah, and F. Et-nachtergaele, Global Agro-ecological Assessment for Agriculture in the 21st Century: Methodology and Results, Consulté à l'adresse, 2002.

P. F. Fisher, A. J. Comber, and R. Wadsworth, Land use and land cover: contradiction or complement, Re-Presenting GIS, pp.85-98, 2005.

G. M. Foody, Status of land cover classification accuracy assessment. Remote Sensing of Environment, vol.80, pp.185-201, 2002.

E. W. Forgy, Cluster analysis of multivariate data: efficiency versus interpretability of classifications, Consulté à l'adresse, vol.21, pp.768-769, 1965.

A. C. Fornaro, Logística e agronegócio globalizado no Estado do Tocantins: um estudo sobre a expansão das fronteiras agrícolas modernas no território brasileiro, 2012.

S. E. Franklin and M. A. Et-wulder, Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas, Progress in Physical Geography, vol.26, issue.2, pp.173-205, 2002.

L. O. Fresco, Comparing Anglophone and Francophone approaches to farming systems research and extension. Networking Paper no.1. Farming Systems Support Project, 1984.

L. O. Fresco and E. Westphal, A hierarchical classification of farm systems, Experimental Agriculture, vol.24, issue.4, pp.399-419, 1988.

T. Friedrich, R. Derpsch, and A. Et-kassam, Overview of the global spread of conservation agriculture, Field Actions Science Reports, pp.1-7, 2012.

,

S. Fritz, L. See, I. Mccallum, L. You, A. Bun et al., Mapping global cropland and field size, Global Change Biology, vol.21, issue.5, pp.1980-1992, 2015.

,

S. Fritz, L. See, and F. Et-rembold, Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa, International Journal of Remote Sensing, vol.31, issue.9, pp.576-587, 2010.

,

M. Galochet, V. Godard, and M. Et-hotyat, Unités paysagères et biodiversité des îlots boisés : De l'image satellitale à l'analyse de terrain -Version française de: Land Units and the Biodiversity of Forest Islets: From Satellite Images to Ground Analysis, Landscape ecology in agroecosystems management, pp.317-330, 2001.

D. Garrity, J. Dixon, and J. Et-boffa, Understanding African farming systems. Science and policy implications, 2012.

S. Geng, C. E. Hess, and J. Et-auburn, Sustainable Agricultural Systems: Concepts and Definitions, Journal of Agronomy and Crop Science, vol.165, issue.2-3, pp.73-85, 1990.

,

U. Gessner, M. Machwitz, T. Esch, A. Tillack, V. Naeimi et al., Multisensor mapping of West African land cover using MODIS, ASAR and TanDEM-X/TerraSAR-X data, Remote Sensing of Environment, vol.164, pp.282-297, 2015.

,

C. M. Gevaert and F. J. García-haro, A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion, Remote Sensing of Environment, vol.156, pp.34-44, 2015.

,

C. C. Gibson, E. Ostrom, and T. K. Et-ahn, The concept of scale and the human dimensions of global change: A survey, Ecological Economics, vol.32, issue.2, pp.92-92, 2000.

M. Girard and C. Girard, Traitement des données de télédétection -Environnement et ressources naturelles, 2010.

N. Girard, La région: une notion géographique? Ethnologie Française, vol.37, pp.107-112, 2004.

A. Gonin and B. Et-tallet, Quel avenir pour l'élevage dans le bassin cotonnier de l'Ouest du Burkina Faso ? Dynamiques agro-pastorales et recompositions territoriales, Autrepart, vol.60, issue.1, pp.95-110, 2012.

, Remote Sensing, vol.8, p.19

A. Guenot and M. Et-huchet-bourdon, Rôle du coton sur la filière maïs au Burkina Faso, 2014.

, Économie Rurale, Agricultures, Alimentations, Territoires, vol.341, pp.107-119

M. K. Gumma, P. S. Thenkabail, P. Teluguntla, M. N. Rao, I. A. Mohammed et al., Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data, International Journal of Digital Earth, vol.9, issue.10, pp.981-1003, 2016.

R. P. Gupta, R. K. Tiwari, V. Saini, and N. Srivastava, A Simplified Approach for Interpreting Principal Component Images, Advances in Remote Sensing, vol.2, pp.111-119, 2013.

,

H. C. Gurgel and N. J. Ferreira, Annual and interannual variability of NDVI in Brazil and its connections with climate, International Journal of Remote Sensing, vol.24, pp.3595-3609, 2003.

,

R. Hadria, B. Duchemin, F. Baup, T. Le-toan, A. Bouvet et al., Combined use of optical and radar satellite data for the detection of tillage and irrigation operations: Case study in Central Morocco, Agricultural Water Management, vol.96, issue.7, pp.1120-1127, 2009.
URL : https://hal.archives-ouvertes.fr/ird-00389251

O. Hagolle, M. Huc, D. Villa-pascual, and G. Et-dedieu, A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENuS and Sentinel-2 Images, Remote Sensing, vol.7, issue.3, pp.2668-2691, 2015.

M. Hall-beyer, Comparison of Single-Year and Multiyear NDVI Time Series Principal Components in Cold Temperate Biomes, IEEE Transactions on Geoscience and Remote Sensing, issue.11, pp.2568-2574, 2003.

J. A. Hartigan and M. A. Wong, Algorithm AS 136: A k-means clustering algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol.28, issue.1, pp.100-108, 1979.

G. J. Hay and G. Et-castilla, Geographic object-based image analysis (GEOBIA): A new name for a new discipline, Lecture Notes in Geoinformation and Cartography, pp.75-89, 2008.

,

G. W. Hazeu, M. J. Metzger, C. A. Mücher, M. Perez-soba, C. Renetzeder et al., European environmental stratifications and typologies: An overview. Agriculture, Ecosystems and Environment, vol.142, pp.29-39, 2011.

R. J. Hijmans, raster: Geographic Data Analysis and Modeling, 2016.

C. Hirano, C. Da-silva-chagas, F. C. Saraiva-do-amaral, N. C. De-sousa-neto, N. Pereira et al., Aptidão agrícola das terras do Estado de Tocantins. Boletim de pesquisa n o 40, EMBRAPA-SNLCS. Consulté à l'adresse, 1989.

Y. Hirosawa, S. E. Marsh, and D. H. Et-kliman, Application of standardized principal component analysis of land-cover characterization using multitemporal AVHRR data, Remote Sensing of Environment, vol.58, issue.3, pp.267-281, 1996.

. Hlpe, Nutrition and food systems. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, 2017.

A. Huete, K. Didan, T. Miura, E. .. Rodriguez, X. Gao et al., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, vol.83, pp.195-213, 2002.

I. Ibge, Instituto Brasileiro de Geografia e Estatística e Ministério do Meio Ambiente, 2004.

. Ibge, Instituto Brasileiro de Geografia e Estatística, Produção Agrícola Municipal (PAM), 2014.

D. ,

. Ibge, Instituto Brasileiro de Geografia e Estatística, Produção Agrícola Municipal (PAM), 2015.

D. ,

, Base cartográfica contínua do Brasil (escala 1:250 000), versão 2017. Diretoria de geociências coordenação de cartografia do Instituto Brasileiro de Geografia e Estatística, 2017.

J. Inglada, A. Vincent, M. Arias, and C. Et-marais-sicre, Improved early crop type identification by joint use of high temporal resolution sar and optical image time series, 2016.

, Remote Sensing, vol.8

, Projeto TerraClass Amazônia : Mapeamento do Uso e Cobertura da Terra na Amazônia Legal Brasileira

. Inpe, Projeto TerraClass Cerrado : Mapeamento do Uso e Cobertura Vegetal do Cerrado

. Iucn/unep-wcmc, Version octobre 2017. Disponible à l'adresse : www.protectedplanet, Pattern Recognition Letters, vol.31, issue.8, pp.651-666, 2010.

C. Jenkerson, User guide: Earth resources observation and science (EROS) center science processing architecture (ESPA) on demand interface, 2013.

P. Jouve, La dimension spatiale des systèmes de culture: Comparaison entre agriculture tempérée et agriculture tropicale, Cahiers Agricultures, vol.15, issue.3, pp.255-260, 2006.

M. Karlson, M. Ostwald, H. Reese, H. R. Bazié, and B. Et-tankoano, Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species, International Journal of Applied Earth Observation and Geoinformation, vol.50, pp.80-88, 2016.

,

G. Kirches, C. Brockmann, M. Boettcher, M. Peters, S. Bontemps et al., Land Cover CCI-Product User Guide-Version 2, 2014.

. Louvain-la-neuve,

C. A. Klink and R. B. Machado, Conservation of the Brazilian Cerrado, Conservation Biology, 2005.

K. Knauer, U. Gessner, R. Fensholt, G. Forkuor, and C. Et-kuenzer, Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: The role of population growth and implications for the environment, Remote Sensing, issue.2, p.9, 2017.

,

, Références bibliographiques

K. Knauer, U. Gessner, R. Fensholt, and C. Et-kuenzer, An ESTARFM fusion framework for the generation of large-scale time series in cloud-prone and heterogeneous landscapes, Remote Sensing, vol.8, issue.5, 2016.

V. Labatut and H. Et-cherifi, Accuracy measures for the comparison of classifiers, The 5th International Conference on Information Technology, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00611319

. Lada, Mapping Land Use Systems at global and regional scales for Land Degradation Assessment Analysis, Nachtergaele F. et Petri M. LADA Technical, 2008.

I. Rome,

N. S. Lam, Fractals and Scale in Environmental Assessment and Monitoring, Scale and Geographic Inquiry: Nature, Society, and Method, pp.23-40, 2008.

,

M. J. Lambert, F. Waldner, and P. Et-defourny, Cropland mapping over Sahelian and Sudanian agrosystems: A Knowledge-based approach using PROBA-V time series at 100-m. Remote Sensing, vol.8, 2016.

E. F. Lambin, H. J. Geist, and E. Et-lepers, Dynamics of land-use and land-cover change in tropical regions, Annual Review of Environment and Resources, vol.28, issue.1, pp.205-241, 2003.

,

E. F. Lambin, H. J. Geist, and R. R. Et-rindfuss, Introduction: Local Processes with Global Impacts. Land-Use and Land-Cover Change, Local Processes and Global Impact, pp.2-8, 2006.

,

P. Lassalle, J. Inglada, J. Michel, M. Grizonnet, and J. Malik, A Scalable Tile-Based Framework for Region-Merging Segmentation, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.10, pp.5473-5485, 2015.

D. Leenhardt, F. Angevin, A. Biarnès, N. Colbach, and C. Et-mignolet, Describing and locating cropping systems on a regional scale. A review. Agronomy for Sustainable Development, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00886439

C. Lelong, C. Alexandre, and S. Et-dupuy, Discrimination of tropical agroforestry systems in very high resolution satellite imagery using object-based hierarchical classification: A casestudy in Cameroon, 2014.

B. Leutner and N. Horning, RStoolbox: tools for remote sensing data analysis, 2017.

U. Ligges, T. Short, and P. Et-kienzle, signal: Signal processing, 2015.

A. A. Lima, F. N. Oliveira, and A. R. Et-de-aquino, Solos e aptidão agrícola das terras do Estado do Tocantins, Embrapa Agroindústria Tropical. Documentos, p.31, 2000.

S. P. Lloyd, Least Squares Quantization in PCM, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.

D. B. Lobell and C. B. Field, Global scale climate-crop yield relationships and the impacts of recent warming, Environmental Research Letters, vol.2, issue.1, p.7, 2007.

T. R. Loveland and R. S. Et-defries, Observing and monitoring land use and land cover change. Ecosystems And Land Use Change, vol.153, pp.231-246, 2004.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol.1, pp.281-297, 1967.

J. P. Malingreau, E. Bartholomé, and E. Et-barisano, Surveillance de la production agricole en Afrique de l'Ouest. Nécessité d'une intégration de différentes plates-formes satellitaires, Proceedings of the Symposium SPOT 1, utilisation des images, bilans, résultats, pp.353-370, 1987.

. Paris,

R. Massey, T. T. Sankey, R. G. Congalton, K. Yadav, P. S. Thenkabail et al., MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types. Remote Sensing of Environment, vol.198, pp.490-503, 2017.

,

E. Mathys and A. Gardner, USAID Office of Food for Peace Burkina Faso Food Security Country Framework FY 2010 -FY, Food and Nutrition Technical Assistance II Project (FANTA-2), 2009.

P. A. Matson, W. J. Parton, A. G. Power, and M. J. Swift, Agricultural Intensification and Ecosystem Properties, Science, vol.277, issue.5325, pp.504-509, 1997.

,

V. Maus, G. Câmara, R. Cartaxo, A. Sanchez, F. M. Ramos et al., A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping, 2016.

, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.8, pp.3729-3739

H. Mcnairn, C. Champagne, J. Shang, D. Holmstrom, and G. Et-reichert, Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories, ISPRS Journal of Photogrammetry and Remote Sensing, vol.64, issue.5, pp.434-449, 2009.

,

, Références bibliographiques

Q. Mcnemar, Note on the sampling error of the difference between correlated proportions or percentages, Psychometrika, vol.12, issue.2, pp.153-157, 1947.

Z. Mingwei, Z. Qingbo, C. Zhongxin, L. Jia, Z. Yong et al., Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data, International Journal of Applied Earth Observation and Geoinformation, vol.10, issue.4, pp.476-485, 2008.

, Ministère de l'éducation nationale et ministère de l'enseignement supérieur et de la recherche, Journal Officiel numéro complémentaire, vol.11, p.10848, 1980.

C. Monfreda, N. Ramankutty, and J. A. Et-foley, Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochemical Cycles, issue.1, p.22, 2008.

S. Mücher, L. De-simone, H. Kramer, A. De-wit, L. Roupioz et al., , 2016.

, A new Global Agro-Environmental Stratification (GAES), vol.70

S. Mu-cher, L. De-simone, H. Kramer, A. De-wit, L. Roupioz et al., A new Global Agro-Environmental Stratification (GAES), 2016.

C. Müller, A. Bondeau, A. Popp, K. Waha, and M. Et-fader, Climate Change Impacts on Agricultural Yields, 2010.

F. Nachtergaele and M. Et-petri, Mapping Land Use Systems at Global and Regional Scales for Land Degradation Assessment Analysis, 2013.

D. Nasa-lp, MOD09A1 data product. NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC)

D. Nasa-lp, MOD13Q1 version 5 data product. NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center. Disponible à l'adresse

D. Nir, Regional geography considered from the systems' approach, Geoforum, vol.18, issue.2, pp.187-202, 1987.

R. V. O'neill, D. L. Deangelis, J. B. Waide, and T. F. Et-allen, Hierarchical Concept of Ecosystems, Monographs in Population Biology Series, vol.23, 1986.

. Ocha-rowca, Burkina Faso: Settlements. United Nations Office for the Coordination of Humanitarian Affairs (OCHA) Regional Office for West and Central Africa (ROWCA), 2015.

D. ,

. Ocha-rowca, United Nations Office for the Coordination of Humanitarian Affairs (OCHA) Regional Office for West and Central Africa (ROWCA), Second Administrative Level Boundaries Project (SALB), 2017.

D. , Burkina Faso : Atlas des cartes d'occupation du sol. Projet amélioration de la résilience des populations sahéliennes aux mutations environnementales -REPSAHEL, 2015.

M. Ozdogan and C. E. Et-woodcock, Resolution dependent errors in remote sensing of cultivated areas, Remote Sensing of Environment, vol.103, issue.2, pp.203-217, 2006.

,

A. M. Pacheco, H. Mcnairn, and A. Et-merzouki, Evaluating TerraSAR-X for the identification of tillage occurrence over an agricultural area in Canada. Remote Sensing for Agriculture, Ecosystems, and Hydrology Xii, vol.7824, pp.1-7, 2010.

A. Pacheco and H. Et-mcnairn, Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping, Remote Sensing of Environment, vol.114, issue.10, pp.2219-2228, 2010.

,

S. Panigrahy, K. R. Manjunath, and S. S. Ray, Deriving cropping system performance indices using remote sensing data and GIS, International Journal of Remote Sensing, vol.26, issue.12, pp.2595-2606, 2005.

S. Panigrahy, S. S. Ray, K. R. Manjunath, P. S. Pandey, S. K. Sharma et al., A Spatial Database of Cropping System and its Characteristics to Aid Climate Change Impact Assessment Studies, Journal of the Indian Society of Remote Sensing, vol.39, issue.3, pp.355-364, 2011.

,

S. Parmentier, Scaling-up agroecological approaches: what, why and how, 2014.

K. Pearson, Principal components analysis. The London, Edinburgh, and Dublin Philosophical Magazine and, Journal of Science, vol.6, issue.2, p.559, 1901.

A. R. Pedroso-da-silva and M. G. Et-almeida, O agronegócio e o Estado do Tocantins: o atual estágio de consolidação, Caminhos de Geografia, vol.8, issue.21, 2007.

F. Plet, La geographie rurale française : quelques jalons. Sociétés Contemporaines, pp.85-106, 2003.

S. G. Plexida, A. I. Sfougaris, I. P. Ispikoudis, and V. P. Et-papanastasis, Selecting landscape metrics as indicators of spatial heterogeneity-Acomparison among Greek landscapes, International Journal of Applied Earth Observation and Geoinformation, vol.26, issue.1, pp.26-35, 2014.

,

J. D. Plourde, B. C. Pijanowski, and B. K. Et-pekin, Evidence for increased monoculture cropping in the Central United States. Agriculture, Ecosystems and Environment, vol.165, pp.50-59, 2013.

,

R. G. Pontius and M. Et-millones, Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment, International Journal of Remote Sensing, vol.32, issue.15, pp.4407-4429, 2011.

B. Qiu, M. Zhong, Z. Tang, and C. Wang, A new methodology to map double-cropping croplands based oncontinuous wavelet transform, International Journal of Applied Earth Observation and Geoinformation, vol.26, issue.1, pp.97-104, 2014.

R. Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2016.

F. Ramoino, F. Pera, and O. Et-arino, S2 prototype LC map at 20m of Africa, Users Feedback Compendium, 2016.

I. Rome and . Consulté,

P. F. Ribeiro, J. L. Santos, J. Santana, L. Reino, P. J. Leitão et al., Landscape makers and landscape takers: links between farming systems and landscape patterns along an intensification gradient, Landscape Ecology, vol.31, issue.4, pp.791-803, 2016.

,

J. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, 2006.

, Références bibliographiques

D. Rizzo, E. Marraccini, S. Lardon, H. Rapey, M. Debolini et al., Farming systems designing landscapes: Land management units at the interface between agronomy and geography, Geografisk Tidsskrift, vol.113, issue.2, pp.71-86, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01004293

,

J. M. Robbez-masson, J. C. Foltête, L. Cabello, and M. Et-flitti, Prise en compte du contexte spatial dans l'instrumentation de la notion de paysage. Application à une segmentation géographique assistée, Revue Internationale de Géomatique, vol.9, pp.173-195, 1999.
URL : https://hal.archives-ouvertes.fr/hal-02683682

K. D. Rodrigues-de-melo, A. Polastrine, R. M. Amaral, A. Lopes, C. L. Sayão-lobato et al., Caracterização da bovinocultura leiteira nos municípios de Taguatinga, Combinado e Lavandeira, região sudeste do Estado do Tocantins, Brasil, p.3, 2010.

G. H. Rosenfield and K. Fitzpatrick-lins, A coefficient of agreement as a measure of thematic classification accuracy, Photogrammetric Engineering and Remote Sensing, vol.52, issue.2, pp.223-227, 1986.

J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Et-deering, Monitoring Vegetation Systems in the Great Okains with ERTS, Third Earth Resources Technology Satellite-1 Symposium, vol.1, pp.325-333, 1973.

B. Rudorff, J. Risso, D. Aguiar, F. Gonçalves, M. Salgado et al., , 2015.

, Análise Geoespacial da Dinâmica das Culturas Anuais no Bioma Cerrado, 2000.

M. C. Rufino, C. Atzberger, G. Baldi, K. Butterbach-bahl, T. S. Rosenstock et al., , 2016.

, Targeting landscapes to identify mitigation options in smallholder agriculture, Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture, pp.15-36

R. Sahajpal, X. Zhang, R. C. Izaurralde, I. Gelfand, and G. C. Et-hurtt, Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt, Computers and Electronics in Agriculture, vol.108, pp.173-182, 2014.

,

J. M. Salmon, M. A. Friedl, S. Frolking, D. Wisser, and E. M. Douglas, Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data, International Journal of Applied Earth Observation and Geoinformation, vol.38, pp.321-334, 2015.

, Références bibliographiques

A. Savitzky and M. J. Et-golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Analytical Chemistry, vol.36, issue.8, pp.1627-1639, 1964.

,

N. F. Sayre, Ecological and geographical scale: parallels and potential for integration, Progress in Human Geography, vol.29, issue.3, pp.276-290, 2005.

M. Sebillotte, Les systèmes de culture. Réflexion sur l'intérêt et l'emploi de cette notion à partir de l'expérience acquise en région de grande culture, Séminaire du département d'agronomie de l'INRA, pp.63-80, 1982.

M. Sebillotte, Système de culture, un concept opératoire pour les agronomes, INRA, pp.165-196, 1990.

L. See, S. Fritz, L. You, N. Ramankutty, M. Herrero et al., Improved global cropland data as an essential ingredient for food security, Global Food Security, 2015.

. Seplan, Superintendência de Planejamento e Gestão Central de Políticas Públicas, Diretoria de Zoneamento Ecológico-Econômico, Palmas: Secretaria do Planejamento (SEPLAN), 2012.

S. Siebert, J. Hoogeveen, and K. Et-frenken, Irrigation in Africa, Europe and Latin America -Update of the Digital Global Map of Irrigation Areas to Version 4, Frankfurt Hydrology Paper, issue.5, p.135, 2006.

S. Siebert, M. Kummu, M. Porkka, P. Döll, N. Ramankutty et al., A global data set of the extent of irrigated land from 1900 to, Hydrology and Earth System Sciences, vol.19, issue.3, pp.1521-1545, 2005.

L. A. Silva, Biomas presentes no Estado do Tocantins. Consultoria Legislativa da Câmara dos Deputados, 2007.

S. Skakun, N. Kussul, A. Y. Shelestov, M. Lavreniuk, and O. Et-kussul, Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.8, pp.3712-3719, 2016.

,

N. Slonim, E. Aharoni, and K. Et-crammer, Hartigan's k-means versus Lloyd's k-means -Is it time for a change?, IJCAI International Joint Conference on Artificial Intelligence, pp.1677-1684, 2013.

R. Solano, K. Didan, A. Jacobson, and A. Et-huete, MODIS Vegetation Index User's Guide, 2010.

K. Soudani, G. Le-maire, E. Dufrêne, C. François, N. Delpierre et al., Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sensing of Environment, vol.112, pp.2643-2655, 2008.

K. R. Spangler, A. H. Lynch, and S. A. Spera, Precipitation Drivers of Cropping Frequency in the Brazilian Cerrado: Evidence and Implications for Decision-Making, vol.9, pp.201-213, 2017.

S. Spera, Agricultural Intensification Can Preserve the Brazilian Cerrado: Applying Lessons From Mato Grosso and Goiás to Brazil's Last Agricultural Frontier, Tropical Conservation Science, vol.10, 2017.

T. J. Stomph, L. O. Fresco, and H. Et-van-keulen, Land use system evaluation: Concepts and methodology, Agricultural Systems, vol.44, issue.3, pp.90222-90224, 1994.

A. H. Strahler, L. Boschetti, G. M. Foody, M. A. Friedl, M. C. Hansen et al., Global Land Cover Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps. Scientific and Technical Research series, 2006.

A. H. Strahler, C. E. Woodcock, and J. A. Smith, On the nature of models in remote sensing, Remote Sensing of Environment, vol.20, issue.2, pp.121-139, 1986.

A. Thiombiano and D. Et-kampmann, Atlas de la Biodiversité de L'Afrique de L'Ouest, 2010.

G. Trimble-germany, U United Nations. Department of Economic and Social Affairs. Population Division, World Population Prospects: The 2017 Revision, Key Findings and Advance Tables, 2014.

. Esa/p/wp, , vol.248

, Landsat Level-2 Science Products. U.S. Geological Survey Earth Resources Observation and Science Center (USGS/EROS), Science Processing Architecture (ESPA) On Demand Interface

B. D. Wardlow and S. L. Egbert, Large-area crop mapping using time-series MODIS 250m NDVI data: An assessment for the, Remote Sensing of Environment, vol.112, issue.3, pp.1096-1116, 2008.

M. J. Warrens, Properties of the quantity disagreement and the allocation disagreement, International Journal of Remote Sensing, vol.36, issue.5, pp.1439-1446, 2015.

,

J. A. Wiens, Spatial Scaling in Ecology, Functional Ecology, vol.3, issue.4, p.385, 1989.

H. Willer and J. Et-lernoud, Research Institute of Organic Agriculture (FiBL), Frick, and IFOAM -Organics International, 2017.

C. E. Woodcock and A. H. Strahler, The factor of scale in remote sensing, Remote Sensing of Environment, vol.21, issue.3, pp.311-332, 1987.

W. Bank, , 2010.

D. C. Washington and . Bank, License: CC BY 3.0 IGO

, World Development Indicators (Agriculture, value added, % of GDP), 2017.

D. , Population estimates and projections (Population growth, annual %), 2018.

D. ,

J. Xiong, P. S. Thenkabail, M. K. Gumma, P. Teluguntla, J. Poehnelt et al., Automated cropland mapping of continental Africa using Google Earth Engine cloud computing, ISPRS Journal of Photogrammetry and Remote Sensing, vol.126, pp.225-244, 2017.

J. Xiong, P. S. Thenkabail, J. C. Tilton, M. K. Gumma, P. Teluguntla et al., Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on google earth engine, 2017.

, Remote Sensing, issue.10, p.9

L. You and S. Wood, An entropy approach to spatial disaggregation of agricultural production, Agricultural Systems, vol.90, issue.1-3, pp.329-347, 2006.

L. You, S. Wood, U. Wood-sichra, and W. Wu, Generating global crop distribution maps: From census to grid, Agricultural Systems, vol.127, pp.53-60, 2014.

,

, Références bibliographiques

S. S. Young and A. Et-anyamba, Comparison of NOAA/NASA PAL and NOAA GVI data for vegetation change studies over China, Photogrammetric Engineering and Remote Sensing, vol.65, pp.679-696, 1999.

L. Zhong, L. Yu, X. Li, L. Hu, and P. Et-gong, Rapid corn and soybean mapping in US Corn Belt and neighboring areas, Scientific Reports, vol.6, 2016.

I. S. Zonneveld, The land unit -A fundamental concept in landscape ecology, and its applications, Landscape Ecology, vol.3, issue.2, pp.67-86, 1989.

A. Bellón, B. Bégué, A. Lo-seen, D. De-almeida, C. A. Simões et al., A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series, Remote Sensing, vol.9, p.600, 2017.

B. Bellón, A. Bégué, D. Lo-seen, V. Lebourgeois, B. A. Evangelista et al., Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach, International Journal of Applied Earth Observations and Geoinformation, vol.68, pp.127-138, 2018.

, References 1. FAO. How to feed the world in 2050, Proceedings of the Expert Meeting on How to Feed the World in 2050, pp.24-26, 2009.

F. Nachtergaele and M. Petri, Mapping Land Use Systems at Global and Regional Scales for Land Degradation Assessment Analysis, vol.FAO, 2013.

A. Bégué, D. Arvor, C. Lelong, E. Vintrou, and M. Simoes, Agricultural systems studies using remote sensing, Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, pp.113-130, 2015.

P. M. Driessen, N. T. Konijn, and . Land, Use Systems Analysis, 1992.

. Fao, Guidelines for Land-Use Planning, vol.FAO, 1993.

D. Arvor, M. Jonathan, M. S. Meirelles, V. Dubreuil, and L. Durieux, Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil, Int. J. Remote Sens, vol.32, pp.7847-7871, 2011.
URL : https://hal.archives-ouvertes.fr/halshs-00623706

M. J. Cheema and W. G. Bastiaanssen, Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis, Agr. Water Manag, vol.97, pp.1541-1552, 2010.

J. K. Kiptala, Y. Mohamed, M. L. Mul, M. J. Cheema, and P. Van-der-zaag, Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin, Eastern Africa. Phys. Chem. Earth, vol.66, pp.112-122, 2013.

B. D. Wardlow and S. L. Egbert, Large-area crop mapping using time-series MODIS 250m NDVI data: An assessment for the U.S. Central Great Plains. Remote Sens. Environ, vol.112, pp.1096-1116, 2008.

G. J. Hay and G. Castilla, Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline, Object-Based Image Analysis

T. Blaschke, S. Lang, and G. J. Hay, , pp.75-89, 2008.

M. Bisquert, A. Bégué, and M. Deshayes, Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series, Int. J. Appl. Earth Obs, vol.37, pp.72-82, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02602428

M. Bisquert, A. Bégué, M. Deshayes, and D. Ducrot, Environmental evaluation of MODIS-derived land units. GIsci. Remote Sens, vol.54, pp.64-77, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02606011

L. A. Silva, Biomas Presentes no Estado do Tocantins

, Consultoria Legislativa da Câmara dos Deputados: Brasília, Brazil, 2007.

. Conab and . Soja, Série Histórica de Produtividade (Safras, p.15, 2000.

C. A. Almeida, A. C. Coutinho, J. C. Esquerdo, M. Adami, A. Venturieri et al., High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data, Acta Amaz, vol.46, pp.291-302, 2016.
URL : https://hal.archives-ouvertes.fr/ird-01630575

. Inpe and . Projeto, Terraclass Cerrado: Mapeamento do uso e Cobertura Vegetal do Cerrado, p.22, 2017.

. Inpe and . Projeto, Terraclass Amazônia: Mapeamento do uso e Cobertura da Terra na Amazônia Legal Brasileira, p.22, 2017.

I. Ibge and . Brasileiro-de-geografia-e-estatística, Produção Agrícola Municipal (PAM) 2014. Available online, p.15, 2017.

A. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao et al., Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ, vol.83, pp.195-213, 2002.

K. Soudani, G. Le-maire, E. Dufrêne, C. François, N. Delpierre et al., Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sens. Environ, vol.112, pp.2643-2655, 2008.

L. Busetto and L. Ranghetti, MODIStsp: An R Package for Preprocessing of MODIS Time Series, p.15, 2017.

R. Solano, K. Didan, A. Jacobson, and A. Huete, MODIS Vegetation Index User's Guide (MOD13 Series); Vegetation Index and Phenology Lab, 2010.

J. Chen, P. Jönsson, M. Tamura, Z. Gu, B. Matsushita et al., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter, Remote Sens. Environ, vol.91, pp.332-344, 2004.

A. Savitzky and M. J. Golay, Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem, vol.36, pp.1627-1639, 1964.

J. R. Eastman and M. Fulk, Long sequence time series evaluation using standardized principal components. Photogramm. Eng. Remote Sens, vol.59, pp.991-996, 1993.

H. C. Gurgel and N. J. Ferreira, Annual and interannual variability of NDVI in Brazil and its connections with climate, Int. J. Remote Sens, vol.24, pp.3595-3609, 2003.

M. Hall-beyer, Comparison of single-year and multiyear NDVI time series principal components in cold temperate biomes, IEEE Trans. Geosci. Remote Sens, vol.41, pp.2568-2574, 2003.

Y. Hirosawa, S. E. Marsh, and D. H. Kliman, Application of standardized principal component analysis of land-cover characterization using multitemporal AVHRR data. Remote Sens. Environ, vol.58, pp.267-281, 1996.

T. Wang, X. Kou, Y. Xiong, P. Mou, J. Wu et al., Temporal and spatial patterns of NDVI and their relationship to precipitation in the Loess Plateau of China, Int. J. Remote Sens, vol.31, pp.1943-1958, 2010.

S. S. Young and A. Anyamba, Comparison of NOAA/NASA PAL and NOAA GVI data for vegetation change studies over China. Photogramm. Eng. Remote Sens, vol.65, pp.679-696, 1999.

B. Leutner and N. Horning, Rstoolbox: Tools for Remote Sensing Data Analysis. R Package Version 0.1.8, p.15, 2017.

. Trimble, Ecognition Developer© 9

G. Trimble-germany, , 2014.

M. Baatz and A. Schäpe, Multiresolution segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation, Angewandte Geographische Informations-Verarbeitung, XII, pp.12-23, 2000.

U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS J. Photogramm, vol.58, pp.239-258, 2004.

C. A. Almeida, M. Mourão, N. Dessay, A. Lacques, A. Monteiro et al., Typologies and spatialization of agricultural production systems in Rondônia, Brazil: Linking land use, socioeconomics and territorial configuration, vol.5, 2016.

H. Cai, S. Zhang, K. Bu, J. Yang, and L. Chang, Integrating geographical data and phenological characteristics derived from MODIS data for improving land cover mapping, J. Geogr. Sci, vol.21, pp.705-718, 2011.

E. Cano, J. Denux, M. Bisquert, L. Hubert-moy, and V. Chéret, Improved forest-cover mapping based on MODIS time series and landscape stratification, Int. J. Remote Sens, vol.38, pp.1865-1888, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01600887

B. Bellón, Int J Appl Earth Obs Geoinformation, vol.68, pp.127-138, 2018.

R. Arvor, J. Damien, M. , P. Meirelles, M. Simões et al., Classification of MODIS EVI time series for crop mapping in the state of mato grosso, Brazil, Int. J. Remote Sens, vol.32, issue.22, pp.7847-7871, 2011.
URL : https://hal.archives-ouvertes.fr/halshs-00623706

C. Atzberger, Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs, Remote Sens, vol.5, issue.2, pp.949-981, 2013.

A. Bégué, . Damien, C. Arvor, . Lelong, . Elodie et al., Agricultural systems studies using remote sensing. Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, pp.113-130, 2015.

M. Baatz, . Schäpe, and . Arno, Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation, Angewandte Geographische Informationsverarbeitung XII, vol.58, pp.12-23, 2000.

E. Barona, . Ramankutty, . Navin, . Hyman, . Glenn et al., The role of pasture and soybean in deforestation of the Brazilian Amazon, Environ. Res. Lett, vol.5, issue.2, 2010.

B. Bellón, A. Bégué, D. Lo-seen, C. Aparecido-de-almeida, and M. , A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI, Time Series'. Remote Sensing, vol.9, issue.6, 2017.

. Bisquert, . Mar, . Bordogna, . Gloria, . Bégué et al., A simple fusion method for image time series based on the estimation of image temporal validity, Remote Sens, vol.7, issue.1, pp.704-724, 2015.

T. Blaschke, G. Hay, J. Hay, . Maggi, . Kelly et al., Geographic object-based image analysis-towards a new paradigm, International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS), vol.87, pp.180-191, 2014.

É. L. Bolfe, C. De, . Victória, . Daniel, . Contini et al., Matopiba Em Crescimento Agrícola Aspectos Territoriais E. Revista de Política Agrícola, vol.25, issue.4, pp.38-62, 2016.

J. Brown, J. H. Christopher, C. Coutinho, A. De, C. Victoria et al., Classifying multiyear agricultural land use data from mato grosso using time-series MODIS vegetation index data, Remote Sens. Environ, vol.130, pp.39-50, 2013.

B. E. Bunker, J. A. Tullis, J. D. Cothren, J. Casana, and M. H. Aly, Object-based dimensionality reduction in land surface phenology classification, AIMS Geosci, vol.2, issue.4, pp.302-328, 2016.

L. Busetto and L. Ranghetti, MODIStsp: an R package for automatic preprocessing of MODIS land products time series, Comput. Geosci, vol.97, pp.40-48, 2016.

H. Cai, . Zhang, . Shuwen, . Bu, Y. Kun et al., Integrating geographical data and phenological characteristics derived from MODIS data for improving land cover mapping, J. Geog. Sci, vol.21, issue.4, pp.705-718, 2011.

E. Cano, . Denux, . Bisquert, H. -. Mar, . Laurence et al., Improved forest-cover mapping based on MODIS time series and landscape stratification, Int. J. Remote Sens, vol.38, issue.7, pp.1865-1888, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01600887

M. J. Cheema and W. G. Bastiaanssen, Land use and land cover classification in the irrigated indus basin using growth phenology information from satellite data to support water management analysis, Agric. Water Manage, vol.97, issue.10, pp.1541-1552, 2010.

J. Chen, . Jönsson, . Per, . Tamura, . Masayuki et al., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter, Remote Sens. Environ, vol.91, issue.3, pp.332-344, 2004.

J. Cihlar, Land cover mapping of large areas from satellites: status and research priorities, Int. J. Remote Sens, vol.21, issue.6, pp.1093-1114, 2000.

J. Cohen, A coefficient of agreement for nominal scales, Educ. Psychol. Meas, vol.20, issue.1, pp.37-46, 1960.

. Dubreuil, . Vincent, . Damien, . Arvor, and D. Nathan, Monitoring the pioneer frontier and agricultural intensification in mato grosso using SPOT vegetation images. Revue Française de Photogrammétrie et de Télédétection, vol.200, pp.2-10, 2012.
URL : https://hal.archives-ouvertes.fr/halshs-00725818

. Fao, How to Feed the World in 2050'. Insights from an Expert Meeting at FAO, vol.2050, 2009.

G. M. Foody, Status of land cover classification accuracy assessment, Remote Sens. Environ, vol.80, issue.1, pp.185-201, 2002.

A. Fornaro and . Caselli, Logística E Agronegócio Globalizado No Estado Do Tocantins: Um Estudo Sobre a Expansão Das Fronteiras Agrícolas Modernas No Território Brasileiro, 2012.

S. E. Franklin and M. A. Wulder, Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas, Prog. Phys. Geogr, vol.26, issue.2, pp.173-205, 2002.

C. M. Gevaert, F. García-haro, and . Javier, A comparison of STARFM and an unmixing-based algorithm for landsat and MODIS data fusion, 2015.

B. Bellón, Int J Appl Earth Obs Geoinformation, vol.68, pp.127-138, 2018.

, , vol.156, pp.34-44

X. Guan, . Huang, . Chong, . Liu, . Gaohuan et al., Mapping rice cropping systems in vietnam using an NDVI-based time-series similarity measurement based on DTW distance, Remote Sens, vol.8, issue.1, 2016.

M. Gumma, . Krishna, P. S. Thenkabail, . Teluguntla, . Pardharsadhi et al., Mapping rice-fallow cropland areas for short-season grain legumes intensification in south asia using MODIS 250 M time-series data, Int. J. Digital Earth, vol.9, issue.10, pp.981-1003, 2016.

J. A. Hartigan, . Wong, and A. Manchek, Algorithm AS 136: a K-Means clustering algorithm, J. R. Stat. Soc. Ser. C (Appl. Stat.), vol.28, issue.1, pp.100-108, 1979.

G. J. Hay and G. Castilla, Geographic object-based image analysis (GEOBIA): a new name for a new discipline, pp.75-89, 2008.

, Instituto Brasileiro de Geografia E Estatística, Produção Agrícola Municipal (PAM), 2014.

J. Calli, User Guide: Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) on Demand Interface, 2013.

K. Knauer, . Gessner, . Ursula, . Fensholt, . Rasmus et al., An ESTARFM fusion framework for the generation of large-Scale time series in cloud-prone and heterogeneous landscapes, Remote Sens, vol.8, issue.5, 2016.

. Labatut, . Vincent, and H. Cherifi, Accuracy measures for the comparison of classifiers, The 5th International Conference on Information Technology, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00611319

D. Leenhardt, . Angevin, . Frédérique, A. Biarnès, . Colbach et al., Describing and locating cropping systems on a regional scale. A review, Agron. Sustain. Dev, vol.30, issue.1, pp.131-138, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00886439

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, pp.281-297, 1967.

Q. Mcnemar, Note on the sampling error of the difference between correlated proportions or percentages, Psychometrika, vol.12, issue.2, pp.153-157, 1947.

. Mingwei, . Zhang, . Qingbo, . Zhou, . Zhongxin et al., Crop discrimination in northern China with double cropping systems using fourier analysis of time-series MODIS data, Int. J. Appl. Earth Obs. Geoinf, vol.10, issue.4, pp.476-485, 2008.

A. Pedroso-da-silva, . Roberto, M. De-almeida, and . Geralda, Agronegócio E O Estado Do Tocantins: O Atual Estágio de Consolidação, Caminhos de Geografia, vol.8, issue.21, p.1, 2007.

R. Pontius, . Gilmore, and M. Millones, Death to kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment, Int. J. Remote Sens, vol.32, issue.15, pp.4407-4429, 2011.

. R-core-team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2016.

G. H. Rosenfield and K. Fitzpatrick-lins, A coefficient of agreement as a measure of thematic classification accuracy, Photogramm. Eng. Remote Sens, vol.52, issue.2, pp.223-227, 1986.

L. Silva and . Conceição, Biomas presentes No Estado Do tocantins, Consultoria Legislativa da Câmara dos Deputados, 2007.

R. Solano, K. Didan, A. Jacobson, and A. Huete, MODIS Vegetation Index User's Guide (MOD13 Series, 2010.

K. R. Spangler, A. H. Lynch, and S. A. Spera, Precipitation drivers of cropping frequency in the Brazilian Cerrado: evidence and implications for decisionmaking, Weather Clim. Soc, vol.9, issue.2, pp.201-213, 2017.

S. A. Spera, S. Avery, . Cohn, L. K. Vanwey, J. F. Mustard et al., Recent cropping frequency, expansion, and abandonment in Mato Grosso, Brazil had selective land characteristics, Environ. Res. Lett, vol.9, issue.6, p.64010, 2014.

A. H. Strahler, L. Boschetti, G. M. Foody, M. A. Friedl, M. C. Hansen et al., Global Land Cover Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps, Scientific and Technical Research Series, 2006.

D. Tilman, . Balzer, . Christian, J. Hill, and B. L. Befort, Global food demand and the sustainable intensification of agriculture, Proc. Natl. Acad. Sci. U. S. A, vol.108, issue.50, pp.20260-20264, 2011.

G. Trimble-germany, , 2014.

E. Vintrou, A. Desbrosse, . Bégué, . Agnès, . Traoré et al., Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products, Int. J. Appl. Earth Obs. Geoinf, vol.14, issue.1, pp.83-93, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02595948

B. D. Wardlow and S. L. Egbert, Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the U.S. central great plains, Remote Sens. Environ, vol.112, issue.3, pp.1096-1116, 2008.

J. Xiong, P. S. Thenkabail, . Gumma, K. Murali, . Teluguntla et al., Automated cropland mapping of continental africa using google earth engine cloud computing, ISPRS J. Photogramm. Remote Sens, vol.126, pp.225-244, 2017.

K. Zanter, Landsat 8 (L8) Data Users Handbook. LSDS-1574 Version, 2016.

B. Bellón, Int J Appl Earth Obs Geoinformation, vol.68, pp.127-138, 2018.