W. Dorigo and R. De-jeu, Satellite soil moisture for advancing our understanding of earth system processes and climate change, International Journal of Applied Earth Observation and Geoinformation, vol.48, issue.1, 2016.
DOI : 10.1016/j.jag.2016.02.007

G. E. Batista, E. J. Keogh, O. M. Tataw, and V. M. De-souza, CID: an efficient complexity-invariant distance for time series, Data Mining and Knowledge Discovery, vol.44, issue.9, pp.634-669, 2014.
DOI : 10.1007/11612704_2

T. Blaschke, Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, vol.65, issue.1, pp.2-16, 2010.
DOI : 10.1016/j.isprsjprs.2009.06.004

T. Lillesand, R. Kiefer, and J. Chipman, Remote sensing and image interpretation, 2008.

F. Guttler, S. Alleaume, C. Corbane, D. Ienco, J. Nin et al., Exploring high repetitivity remote sensing time series for mapping and monitoring natural habitats — A new approach combining OBIA and k-partite graphs, 2014 IEEE Geoscience and Remote Sensing Symposium, 2014.
DOI : 10.1109/IGARSS.2014.6947344

Q. Zhu, G. E. Batista, T. Rakthanmanon, and E. J. Keogh, A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets, pp.999-1010, 2012.
DOI : 10.1137/1.9781611972825.86

T. Rakthanmanon, E. J. Keogh, S. Lonardi, and S. Evans, MDL-based time series clustering, Knowledge and Information Systems, vol.17, issue.2, pp.371-399, 2012.
DOI : 10.1007/s10115-008-0131-9

F. Petitjean, J. Inglada, and P. Gançarski, Satellite Image Time Series Analysis Under Time Warping, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.8, pp.3081-3095, 2012.
DOI : 10.1109/TGRS.2011.2179050

URL : https://hal.archives-ouvertes.fr/hal-00940767

F. Petitjean, C. Kurtz, N. Passat, and P. Gançarski, Spatio-temporal reasoning for the classification of satellite image time series, Pattern Recognition Letters, vol.33, issue.13, pp.1805-1815, 2012.
DOI : 10.1016/j.patrec.2012.06.009

URL : https://hal.archives-ouvertes.fr/hal-00636814

T. Rakthanmanon, B. J. Campana, A. Mueen, G. E. Batista, M. B. Westover et al., Addressing Big Data Time Series, ACM Transactions on Knowledge Discovery from Data, vol.7, issue.3, 2013.
DOI : 10.1145/2513092.2500489

Z. Jiang, S. Shekhar, X. Zhou, J. Knight, and J. Corcoran, Focal-Test-Based Spatial Decision Tree Learning: A Summary of Results, 2013 IEEE 13th International Conference on Data Mining, pp.320-329, 2013.
DOI : 10.1109/ICDM.2013.96

Z. Jiang, S. Shekhar, X. Zhou, J. Knight, and J. Corcoran, Focal-Test-Based Spatial Decision Tree Learning, IEEE Transactions on Knowledge and Data Engineering, vol.27, issue.6, pp.1547-1559, 2015.
DOI : 10.1109/TKDE.2014.2373383

X. Li and C. Claramunt, A Spatial Entropy-Based Decision Tree for Classification of Geographical Information, Transactions in GIS, vol.46, issue.3, pp.451-467, 2006.
DOI : 10.1023/A:1022699900025

Z. Jiang, S. Shekhar, P. Mohan, J. Knight, and J. Corcoran, Learning spatial decision tree for geographical classification, Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL '12, pp.2012-390
DOI : 10.1145/2424321.2424372

V. V. Vazirani, Approximation Algorithms, 2001.
DOI : 10.1007/978-3-662-04565-7

P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 2005.

D. Ienco, R. G. Pensa, and R. Meo, From Context to Distance, ACM Transactions on Knowledge Discovery from Data, vol.6, issue.1, 2012.
DOI : 10.1145/2133360.2133361

J. R. Jr, R. Haas, J. Schell, and D. Deering, Monitoring vegetation systems in the great plains with erts, NASA special publication, vol.309, p.351, 1974.

B. Gao, NDWI???A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, vol.58, issue.3, pp.257-266, 1996.
DOI : 10.1016/S0034-4257(96)00067-3

N. Zhang, Y. Hong, Q. Qin, L. Liu, N. Zhang et al., VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing, International Journal of Remote Sensing, vol.23, issue.1983, pp.4585-4609, 2013.
DOI : 10.1029/2009GL038906

M. Baatz, Multiresolution segmentation: an optimization approach for high quality multiscale image segmentation, Angewandte Geographische Informations verarbeitung XII, pp.12-23, 2000.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905