Production of a dynamic cropland mask by processing remote sensing image series at high temporal and spatial resolutions

Remote Sensing

Volumn 8, Issue 1, 2016, Pages

  Valero, Silvia ^a   Morin, David ^a   Inglada, Jordi ^a   Sepulcre, Guadalupe ^b   Arias, Marcela ^a   Hagolle, Olivier ^a   Dedieu, Gérard ^a   Bontemps, Sophie ^b   Defourny, Pierre ^b   Koetz, Benjamin ^c  

^a UNIVERSITÉ DE TOULOUSE   (France)

^b UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^c ESA ESRIN   (Italy)

Author keywords

Cropland mapping; Dynamic classification; Random forests; Satellite image time series; Sentinel 2

Indexed keywords

CULTIVATION; DECISION TREES; PATTERN RECOGNITION; REMOTE SENSING; SATELLITE IMAGERY; TIME SERIES;

AGRICULTURAL PRACTICES; DYNAMIC CLASSIFICATION; FEATURE EXTRACTION AND CLASSIFICATION; PATTERN RECOGNITION TECHNIQUES; RANDOM FORESTS; SATELLITE IMAGES; SATELLITE OBSERVATIONS; SENTINEL-2;

MAPPING;

EID: 84957869508     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8010055     Document Type: Article

References (63)

1. Thenkabail, P.S. Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution. Remote Sens. 2010, 2, 2305-2312.
2. Pinter, P.J.; Hatfield, J.L.; Schepers, J.S.; Barnes, E.M.; Moran, M.S.; Daughtry, C.S.T.; Upchurch, D.R. Remote sensing for crop management. Photogram. Eng. Remote Sen. 2003, 69, 647-664.
3. Whitcraft, A.K.; Vermote, E.F.; Becker-Reshef, I.; Justice, C.O. Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations. Remote Sens. Environ. 2015, 156, 438-447.
4. Fritz, S.; See, L.; McCallum, I.; You, L.; Bun, A.; Moltchanova, E.; Duerauer, M.; Albrecht, F.; Schill, C.; Perger, C.; et al. Mapping global cropland and field size. Glob. Chang. Biol. 2015, doi:10.1111/gcb.12838.
5. Duveiller, G.; Defourny, P. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing. Remote Sens. Environ. 2010, 114, 2637-2650.
6. Whitcraft, A.K.; Becker-Reshef, I.; Justice, C.O. Agricultural growing season calendars derived from MODIS surface reflectance. Int. J. Digit. Earth 2014, DOI:10.1080/17538947.2014.894147.
7. Becker-Reshef, I.; Justice, C.; Sullivan, M.; Vermote, E.; Tucker, C.; Anyamba, A.; Small, J.; Pak, E.; Masuoka, E.; Schmaltz, J.; et al. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project. Remote Sens. 2010, 2, 1589-1609.
8. Bartholomé, E.M; Belward, A.S. GLC2000: A new approach to global land cover mapping from Earth Observation data. Int. J. Remote Sens. 2005, 26, 1959-1977.
9. Bontemps, S.; Defourny, P.; van Bogaert, E.; Arino, O.; Kalogirou, V.; Perez, J.R. GlobCover 2009: Products Description and Validation Report; European Spatial Agency and Université Catholique de Louvain: Frascati, Italy, 2011.
10. Defourny, P.; Bicheron, P.; Brockman, C.; Bontemps, S.; van Bogaert, E.; Vancutsem, C. The first 300 m global land cover map for 2005 using ENVISAT MERIS time series: A product of the GlobCover system. In Proceedings of the 33th International Symposium of Remote Sensing of Environment, Stresa, Italy, 4-8 May 2009.
11. Latham, J.; Cumani, R.; Rosati, I. Bloise, M. Global Land Cover SHARE (GLC-SHARE) Database Beta-Release Version 1.0. 2014. Available online: http://www.glcn.org/downs/prj/glcshare/ GLC_SHARE_beta_v1.0_2014.pdf (accessed on 31 May 2015).
12. Friedl, M.A.; Mclver, D.K.; Hodges, J.C.F.; Zhang, X.Y.; Muchoney, D.; Strahler, A.H.; Woodcock, C.E.; Gopal, S.; Schneider, A.; Cooper, A.; et al. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens. Environ. 2002, 83, 287-302.
13. Bontemps, S.; Defourny, P.; Brockmann, C.; Herold, M.; Kalogirou, V.; Arino, O. New Global Land Cover mapping exercise in the framework of the ESA Climate Change Initiative. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012.
14. Pittman, K.; Hansen, M.C.; Becker-Reshef, I.; Potapov, P.V.; Justice, C.O. Estimating global cropland extent with multi-year MODIS data. Remote Sens. 2010, 2, 1844-1863.
15. Biradar, C.M.; Thenkabail, P.S.; Noojipady, P.; Li, Y.; Dheeravath, V.; Turral, H.; Velpuri, M.; Gumma, M.K.; Gangalakunta, O.R.P.; Cai, X.L.; et al. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. Int. J. Appl. Earth Obs. Geoinf. 2009, 11, 114-129.
16. Thenkabail, P.S.; Biradar, C.M.; Noojipady, P.; Dheeravath, V.; Li, Y.J.; Velpuri, M.; Gumma, M.; Reddy, G.P.O.; Turral, H.; Cai, X.L. A Global irrigated area map (GIAM) using remote sensing at the end of the last millennium. Int. J. Remote Sens. 2009, 30, 3679-3733.
17. Portmann, F.T.; Siebert, S.; Döll, P. MIRCA2000-Global Monthly Irrigated and Rainfed Crop Areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 2010, doi:10.1029/2008GB003435.
18. Brink, A.B.; Bodart, C.; Brodsky, L.; Defourney, P.; Ernst, C.; Donney, F.; Lupi, A.; Tuckova; K. Anthropogenic pressure in East Africa monitoring 20 years of land cover changes by means of medium resolution satellite data. Int. J. Appl. Earth Obs. Geoinf. 2014, 28, 60-69.
19. Atzberger, C. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sens. 2013, 5, 949-981.
20. Global Information and Early Warning System (GIEWS) on Food and Agriculture. Food and Agriculture Organization (FAO). Available online: http://www.fao.org/giews/english/index.htm (accessed on 18 December 2015).
21. United States Department of Agriculture (USDA). Foreign Agricultural Service (FAS). Available online:http://www.fas.usda.gov (accessed on 31 May 2015).
22. Baruth, B.; Royer, A.; Genovese, G.; Klisch, A. The use of remote sensing within the MARS crop yield monitoring system of the European Commission. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 36, 935-941.
23. Global Monitoring of Food Security (GMFS) Program of the European Union. Available online:http://www.gmfs.info (accessed on 18 December 2015).
24. Wu, B.; Gommes, R.; Zhang,M.; Zeng, H.; Yan, N.; Zou, W.; Zheng, Y.; Zhang, N.; Chang, S.; Xing, Q.; Van der Heijden, A. Global Crop Monitoring: A Satellite-Based Hierarchical Approach. Remote Sens. 2015, 7, 3907-3933.
25. Becker-Reshef, I.; Justice, C.; Doorn, B.; Reynolds, C.; Anyamba, A.; Tucker, C.J.; Korontzi, S. NASA's contribution to the Group on Earth Observations (GEO) Global Agricultural Monitoring System of Systems. NASA Earth Obs. 2009, 21, 24-29.
26. Wardlow, B.; Egbert, S.; Kastens, J. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ. 2007, 108, 290-310.
27. Shao, Y.; Lunetta, R.; Ediriwickrema, J.; Liames, J. Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data. Photogramm. Eng. Remote Sens. 2010, 75 73-84.
28. Wardlow, B.D.; Egbert, S.L. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains. Remote Sens. Environ. 2008, 112, 1096-1116.
29. Löw, F.; Duveiller, G. Defining the spatial resolution requirements for crop identification using optical remote sensing. Remote Sens. 2014, 6, 9034-9063.
30. Leroux, L.; Jolivot, A.; Bégué, A.; Seen, D.L.; Zoungrana, B. How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes. Remote Sens. 2014, 6, 8541-8564.
31. Hannerz, F.; Lotsch, A. Assessment of Land Use and Cropland Inventories for Africa; Centre for Environmental Economics and Policy in Africa, University of Pretoria: Pretoria, South Africa, 2006.
32. Global Cropland Area Database at 30 m Resolution (GCAD30). Available online: https://earthdata.nasa. gov/our-community/community-data-system-programs/measures-projects/global-cropland-area-database (accessed on 18 December 2015).
33. Bontemps, S.; Arias M.; Cara, C.; Dedieu, G.; Guzzonato, E.; Hagolle, O.; Inglada, J.; Matton, N.; Morin, D.; Popescu, R.; et al. Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with Sentinel-2. Remote Sens. 2015, 7, 16062-16090, doi:10.3390/rs71215815.
34. Hagolle, O.; Sylvander, S.; Huc, M.; Claverie, M.; Clesse, D.; Dechoz, C.; Lonjou, V.; Poulain, V. SPOT-4 (Take5): Simulation of Sentinel-2 time series on 45 large sites. Remote Sens. 2015, 7, 12242-12264, doi:10.3390/rs70912242.
35. Breiman, L. Random Forests. Mach. Learn. 2001, 40, 5-32.
36. Matton, N.; Sepulcre Canto, G.; Waldner, F.; Valero, S.; Morin, D.; Inglada, J.; Arias, M.; Bontemps, S.; Koetz, B.; Defourny, P. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sens. 2015, 7, 13208-13232.
37. Rouse, J.W.; Hass, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium- Volume I: Technical Presentations; NASA:Washington, DC, USA, 1973; pp. 309-317, NASA SP-351.
38. Gao, B.-C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257-266.
39. Jakubauskas, M.E.; Legates, D.R.; Kastens, J.H. Crop identification using harmonic analysis of time-series AVHRR NDVI data. Comput. Electr. Agric. 2002, 37, 127-139.
40. Zhang, X.; Friedl, M.A.; Schaaf, C.B.; Strahler, A.H.; Hodges, J.G.F.; Gao, F.; Reed, B.C.; Huete, A.R. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 2003, 84, 471-475.
41. Jonsson, P.; Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 2002, 40, 1824-1832.
42. Johnson, D.M.; Mueller, R. The 2009 cropland data layer. Photogramm. Eng. Remote Sens. 2010, 76, 1201-1205.
43. Breiman, L. Bagging predictors. Mach. Learn. 1996, 24, 123-140.
44. Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random Forests for land cover classification. Pattern Recog. Lett. 2006, 27, 294-300.
45. Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 2012, 67, 93-104.
46. Long, J.A.; Lawrence, R.L.; Greenwood, M.C.; Marshall, L.; Miller, P.R. Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest. GISci. Remote Sens. 2013, 50, 418-436.
47. Inglada, J.; Tardy, B.; Valero, S.; Arias, M.; Hagolle, O.; Dedieu, G.; Bontemps, S.; Sepulcre G.; Defourny, P. Algorithm selection for the operational production of crop maps in the frame of the Sentinel-2 Agriculture project. In Proceedings of the 4th International symposium on Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 22-26 September 2014.
48. Kamusoko, C.; Gamba, J.; Murakami, H. Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers. Land 2014, 3, 524-540.
49. Lobo, A.; Chic, O.; Casterad, A. Classification of Mediterranean crops with multisensor data: Per-pixel versus per-object statistics and image segmentation. Int. J. Remote Sens.1996, 17, 2385-2400.
50. Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogrmm. Remote Sens. 2010. 65, 2-16.
51. Hay, G.; Castilla, G. Object-based image analysis: Strengths, weakness, opportunities and threats. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2006, XXXVI-part 6 4-5.
52. Gao, Y.; Mas, J.F. A Comparison of the Performance of Pixel Based and Object Based Classifications over Images with Various Spatial Resolutions. Online J. Earth Sci. 2008, 2, 27-35.
53. Cleve, C.; Kelly, M.; Kearns, F.R.; Moritz, M. Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography. Comput. Environ. Urban Syst. 2008, 32, 317-326.
54. Peña-Barragán, J.M.; Ngugi, M.K.; Plant, R.E.; Six, J. Object based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Environ. 2011, 115, 1301-1316.
55. De Wit, A.J.W.; Clevers, J. Efficiency and accuracy of per-field classification for operational crop mapping. Int. J. Remote Sens. 2004, 25, 4091-4112.
56. Pearson, K. On lines and planes of closest fit to systems of points in space. Phil. Mag. 1901, 2, 559-572.
57. Comaniciu, D. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 603-619.
58. Lhermitte, S.; Verbesselt, J.; Jonckheere, I.; Nackaerts, K.; van Aardt, J.A.N.; Verstraeten, W.W.; Coppin, P. Hierarchical image segmentation based on similarity of NDVI time series. Remote Sens. Environ. 2008, 112, 506-521, doi:10.1016/j.rse.2007.05.018.
59. Kennedy, R.E.; Yang, Z.; Cohen, W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms. Remote Sens. Environ. 2010, 114, 2897-2910.
60. Eastman, J.R.; Fulk, M. Long sequence time series evaluation using standardized principal components. Photogram. Eng. Remote Sens. 1993, 59, 1307-1312.
61. Petitjean, F.; Kurtz, C.; Passat, N.; Gançarski, P. Spatio-temporal reasoning for the classification of satellite image time series. Pattern Recognit. Lett. 2012, 33, 1805-1815.
62. Sokolova, M.; Lapalme, G. A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 2009, 45, 427-437.
63. Carletta, J. Assessing agreement on classification tasks: The kappa statistic. Comput. Linguist. 1996, 22, 249-254.