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This page provides an overview, alphabetically by author(s), of all manuals and articles useful for WPH Earth observation. See here for documentation on big data in general and on the other ESSnet Big Data workpackages.

Manuals

Articles

  • D. Bargiel, F. Neuendorf, M. Schlund & U. Soergel (2014): Classification of Crops in Different European Regions Based on TerraSAR-X Data, In: 10TH EUROPEAN CONFERENCE ON SYNTHETIC APERTURE RADAR (EUSAR 2014)
  • M. Bettio, J. Delincé, P. Bruyas, W. Croi & G. Eiden (2002): Area Frame Surveys: Aim, Principals and Operational Surveys. Building Agri-Environmental Indicators, Focussing on the European Area Frame Survey LUCAS, 12–27
  • M. Bossard, J. Feranec & J. Otahel (2000): CORINE Land Cover Technical Guide: Addendum 2000
  • G. Büttner (2014): CORINE Land Cover and Land Cover Change Products. In Land Use and Land Cover Mapping in Europe, 55–74
  • Crammer, and Singer. 2002. “On the Algorithmic Implementation of Multiclass Kernel-Based Vector Machines.” Journal of Machine Learning Research - JMLR
  • Csillik, Ovidiu, Mariana Belgiu, Gregory P. Asner, and Maggi Kelly. 2019. “Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2.” Remote Sensing. https://doi.org/10.3390/rs11101257
  • Demarez, Valérie, Florian Helen, Claire Marais-Sicre, and Frédéric Baup. 2019. “In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series.” Remote Sensing. https://doi.org/10.3390/rs11020118
  • C. De Bernardis, F. Vicente-Guijalba, T. Martinez-Marin, J.M. Lopez-Sanchez (2016): “Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion with SAR and Temperature.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2016.2539498
  • Dimitrov, Petar, Qinghan Dong, Herman Eerens, Alexander Gikov, Lachezar Filchev, Eugenia Roumenina, and Georgi Jelev. 2019. “Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications.” Remote Sensing. https://doi.org/10.3390/rs11111370
  • Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, et al. 2012. “Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120: 25–36. https://doi.org/10.1016/j.rse.2011.11.026
  • Ehrlich, D., T. Kemper, M. Pesaresi, and C. Corbane. 2018. “Built-up Area and Population Density: Two Essential Societal Variables to Address Climate Hazard Impact.” Environmental Science and Policy 90: 73–82. https://doi.org/10.1016/j.envsci.2018.10.001
  • EUROSTAT. 2003. “The Lucas Survey - European Statisticians Monitor Territory. Office for Official Publications of the European Communities.”
  • Feng, Siwen, Jianjun Zhao, Tingting Liu, Hongyan Zhang, Zhengxiang Zhang, and Xiaoyi Guo. 2019. “Crop Type Identification and Mapping Using Machine Learning Algorithms and Sentinel-2 Time Series Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2019.2922469
  • Ghosh, Ashish, Niladri Shekhar Mishra, and Susmita Ghosh. 2011. “Fuzzy Clustering Algorithms for Unsupervised Change Detection in Remote Sensing Images.” Information Sciences. https://doi.org/10.1016/j.ins.2010.10.016
  • O. Hagolle, G. Dedieu, B. Mougenot, V. Debaecker, B. Duchemin, A. Meygret (2008): Correction of Aerosol Effects on Multi-Temporal Images Acquired with Constant Viewing Angles: Application to Formosat-2 Images, Remote Sensing of Environment 112: 1689–1701 https://doi.org/10.1016/j.rse.2007.08.016
  • Hagolle, O., M. Huc, D. Villa Pascual, and G. Dedieu. 2010. “A Multi-Temporal Method for Cloud Detection, Applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 Images.” Remote Sensing of Environment 114: 1747–55. https://doi.org/10.1016/j.rse.2010.03.002
  • Hagolle, Olivier, Mireille Huc, David Villa Pascual, and Gerard Dedieu. 2015. “A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images.” Remote Sensing 7: 2668–91. https://doi.org/10.3390/rs70302668
  • Hagolle, Olivier, Sylvia Sylvander, Mireille Huc, Martin Claverie, Dominique Clesse, Cécile Dechoz, Vincent Lonjou, and Vincent Poulain. 2015. “SPOT-4 (Take 5): Simulation of Sentinel-2 Time Series on 45 Large Sites.” Remote Sensing 7: 12242–64. https://doi.org/10.3390/rs70912242
  • Hansen, M. C., R. Sohlberg, R. S. Defries, and J. R.G. Townshend. 2000. “Global Land Cover Classification at 1 Km Spatial Resolution Using a Classification Tree Approach.” International Journal of Remote Sensing. https://doi.org/10.1080/014311600210209
  • Helber, P, B Bischke, A Dengel, and D Borth. 2019. “A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Hennig, Ernest I., Christian Schwick, Tomáš Soukup, Erika Orlitová, Felix Kienast, and Jochen A.G. Jaeger. 2015. “Multi-Scale Analysis of Urban Sprawl in Europe: Towards a European de-Sprawling Strategy.” Land Use Policy, 483–98. https://doi.org/10.1016/j.landusepol.2015.08.001
  • Hennig, Ernest I., Tomáš Soukup, Erika Orlitová, Christian Schwick, Felix Kienast, and Jochen A.G. Jaeger. 2016. “Annexes 1-5: Urban Sprawl in Europe. Joint EEA-FOEN Report.” Luxembourg. https://doi.org/10.2800/143470b
  • Huang, Xin, and Liangpei Zhang. 2013. “An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery.”
  • Hütt, Christoph, and Guido Waldhoff. 2018. “Multi-Data Approach for Crop Classification Using Multitemporal, Dual-Polarimetric TerraSAR-X Data, and Official Geodata.” European Journal of Remote Sensing. https://doi.org/10.1080/22797254.2017.1401909
  • Ienco, DIno, Raffaele Gaetano, Claire Dupaquier, and Pierre Maurel. 2017. “Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks.” IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2017.2728698
  • Inglada, Jordi, Marcela Arias, Benjamin Tardy, Olivier Hagolle, Silvia Valero, David Morin, Gèrard Dedieu, et al. 2015. “Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery.” Remote Sensing 7: 12356–79. https://doi.org/10.3390/rs70912356
  • Inglada, Jordi, Arthur Vincent, Marcela Arias, Benjamin Tardy, David Morin, and Isabel Rodes. 2017. “Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series.” Remote Sensing 9: 95. https://doi.org/10.3390/rs9010095
  • Jackson, Qiong, and David A. Landgrebe. 2002. “Adaptive Bayesian Contextual Classification Based on Markov Random Fields.” IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2002.805087
  • Jia, Kun, Qiangzi Li, Yichen Tian, Bingfang Wu, Feifei Zhang, and Jihua Meng. 2012. “Crop Classification Using Multi-Configuration SAR Data in the North China Plain.” International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2011.587844
  • Khatami, Reza, Giorgos Mountrakis, and Stephen V. Stehman. 2016. “A Meta-Analysis of Remote Sensing Research on Supervised Pixel-Based Land-Cover Image Classification Processes: General Guidelines for Practitioners and Future Research.” Remote Sensing of Environment 177: 89–100. https://doi.org/10.1016/j.rse.2016.02.028
  • S. Lawrence, C. Lee Giles & A. Chung Tsoi (1997): Lessons in Neural Network Training: Overfitting May Be Harder than Expected, In: Proceedings of the National Conference on Artificial Intelligence
  • Y. LeCun & Y. Bengio (1995): Convolutional Networks for Images, Speech, and Time Series. The Handbook of Brain Theory and Neural Networks, 3361 (10): 1995
  • Ledley, RS, M Buas, and TJ Golab. 1990. “Fundamentals of True-Color Image Processing.” In 10th International Conference on Pattern Recognition, 791–95
  • Li, Qingting, Cuizhen Wang, Bing Zhang, and Linlin Lu. 2015. “Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data.” Remote Sensing. https://doi.org/10.3390/rs71215820
  • Li, Y., X. Zhu, Y. Pan, J. Gu, A. Zhao, and X Liu. 2014. “A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery.” Remote Sensing 6 (9): 9034–63
  • Liu, Peng, Hui Zhang, and Kie B. Eom. 2017. “Active Deep Learning for Classification of Hyperspectral Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (2): 712–24. https://doi.org/10.1109/JSTARS.2016.2598859
  • Ma, Lei, Manchun Li, Xiaoxue Ma, Liang Cheng, Peijun Du, and Yongxue Liu. 2017. “A Review of Supervised Object-Based Land-Cover Image Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 277–93. https://doi.org/10.1016/j.isprsjprs.2017.06.001
  • Ma, Lei, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, and Brian Alan Johnson. 2019. “Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review.” ISPRS Journal of Photogrammetry and Remote Sensing 152: 166–77. https://doi.org/10.1016/j.isprsjprs.2019.04.015 “Matplotib.” n.d. Accessed September 19, 2019. https://matplotlib.org/. Nabielek, K, D Hamers, and D Evers. 2016. “Cities in Europe.” PBL Publishers 521 (2470)
  • Navarro, Ana, João Rolim, Irina Miguel, João Catalão, Joel Silva, Marco Painho, Zoltán Vekerdy, et al. 2017. “Regional Scale Cropland Carbon Budgets: Evaluating a Geospatial Agricultural Modeling System Using Inventory Data.” International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1016/j.jag.2017.04.009
  • OCED. 2018. Rethinking Urban Sprawl. Rethinking Urban Sprawl. https://doi.org/10.1787/9789264189881-en
  • Pedregosa, Fabian, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30
  • Pelletier, C, S Valero, J Inglada, N Champion, and G Dedieu. 2016. “Assessing the Robustness of Random Forests to Map Land Cover with High Resolution Satellite Image Time Series over Large Areas.” Remote Sensing 187: 156–68
  • Pelletier, Charlotte, Silvia Valero, Jordi Inglada, Nicolas Champion, and Gérard Dedieu. 2016. “Assessing the Robustness of Random Forests to Map Land Cover with High Resolution Satellite Image Time Series over Large Areas.” Remote Sensing of Environment 187: 156–68. https://doi.org/10.1016/j.rse.2016.10.010
  • Peña-Barragán, José M., Moffatt K. Ngugi, Richard E. Plant, and Johan Six. 2011. “Object-Based Crop Identification Using Multiple Vegetation Indices, Textural Features and Crop Phenology.” Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2011.01.009
  • Pesaresi, Martino, Christina Corbane, Andreea Julea, Aneta J. Florczyk, Vasileios Syrris, and Pierre Soille. 2016. “Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas.” Remote Sensing 8 (4). https://doi.org/10.3390/rs8040299
  • Pesaresi, Martino, Vasileios Syrris, and Andreea Julea. 2016. “A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning.” Remote Sensing 8 (5). https://doi.org/10.3390/rs8050399
  • Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104 https://doi.org/10.1016/j.isprsjprs.2011.11.002
  • Rufin, Philippe, David Frantz, Stefan Ernst, Andreas Rabe, Patrick Griffiths, Mutlu özdoğan, and Patrick Hostert. 2019. “Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning.” Remote Sensing. https://doi.org/10.3390/rs11030232
  • Santaella, Julio. 2019. “In-Depth Review of Satellite Imagery / Earth Observation Technology in Official Statistics.” In Conference of European Statisticians, 67th Plenary Session, Paris, France. “Sentinel-5P Pre-Operations Data Hub.” n.d. Accessed September 19, 2019. https://s5phub.copernicus.eu/dhus/#/home
  • Soille, P., A. Burger, D. De Marchi, P. Kempeneers, D. Rodriguez, V. Syrris, and V. Vasilev. 2018. “A Versatile Data-Intensive Computing Platform for Information Retrieval from Big Geospatial Data.” Future Generation Computer Systems 81: 30–40. https://doi.org/10.1016/j.future.2017.11.007
  • Sonobe, Rei, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, and Hideki Shimamura. 2015. “Discrimination of Crop Types with TerraSAR-X-Derived Information.” Physics and Chemistry of the Earth. https://doi.org/10.1016/j.pce.2014.11.001
  • Sun, Zhongchang, Ru Xu, Wenjie Du, Lei Wang, and Lu Dengsheng. 2019. “High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine.” Remote Sensing. “Sustainable Development.” n.d. Accessed September 19, 2019. https://sustainabledevelopment.un.org/sdgs
  • Szegedy, C, V Vanhoucke, S Ioffe, J Shlens, and Z Wojna. 2016. “Rethinking the Inception Architecture for Computer Vision.” In IEEE Conference on Computer Vision and Pattern Recognition, 2818–26
  • Veloso, Amanda, Stéphane Mermoz, Alexandre Bouvet, Thuy Le Toan, Milena Planells, Jean François Dejoux, and Eric Ceschia. 2017. “Understanding the Temporal Behavior of Crops Using Sentinel-1 and Sentinel-2-like Data for Agricultural Applications.” Remote Sensing of Environment 199: 415–26. https://doi.org/10.1016/j.rse.2017.07.015
  • Xie, Qinghua, Jinfei Wang, Chunhua Liao, Jiali Shang, Juan M. Lopez-Sanchez, Haiqiang Fu, and Xiuguo Liu. 2019. “On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAt-2 Polarimetric SAR Data.” Remote Sensing. https://doi.org/10.3390/rs11070776
  • Yesou, Herve, Eric Pottier, Gregoire Mercier, Manuel Grizonnet, Sadri Haouet, Alain Giros, Robin Faivre, Claire Huber, and Julien Michel. 2016. “Synergy of Sentinel-1 and Sentinel-2 Imagery for Wetland Monitoring Information Extraction from Continuous Flow of Sentinel Images Applied to Water Bodies and Vegetation Mapping and Monitoring.” In International Geoscience and Remote Sensing Symposium (IGARSS) https://doi.org/10.1109/IGARSS.2016.7729033