Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

International Journal of Remote Sensing

Volumn 37, Issue 14, 2016, Pages 3196-3231

  Waldner, François ^a   De Abelleyra, Diego ^b   Verón, Santiago R ^b,c   Zhang, Miao ^d   Wu, Bingfang ^d   Plotnikov, Dmitry ^e   Bartalev, Sergey ^e   Lavreniuk, Mykola ^f   Skakun, Sergii ^f,g   Kussul, Nataliia ^f   Le Maire, Guerric ^h,i   Dupuy, Stéphane ^j   Jarvis, Ian ^k   Defourny, Pierre ^a  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^b INSTITUTO NACIONAL DE TECNOLOGÍA AGROPECUARIA INTA   (Argentina)

^c UNIVERSIDAD DE BUENOS AIRES   (Argentina)

^d INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^e Space Research Institute of Russian Academy of Sciences ^*  (Russian Federation)

^f INSTITUTE OF HYDROBIOLOGY   (Ukraine)

^g UNIVERSITY OF MARYLAND   (United States)

^h CIRAD   (France)

ⁱ EMBRAPA CERRADOS   (Brazil)

^j UMR TETIS   (France)

^k AGRICULTURE AND AGRI FOOD CANADA   (Canada)

more..

Author keywords

[No Author keywords available]

Indexed keywords

CALIBRATION; CROPS; IMAGE RESOLUTION; INFRARED DEVICES; RADIOMETERS; SATELLITE IMAGERY;

ACCURACY INDICATORS; ASSESSMENT AND MONITORING; CLASSIFICATION METHODS; CONFUSION MATRICES; MAPPING METHODOLOGY; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; NEAR-INFRARED CHANNELS; OVERALL ACCURACIES;

MAPPING;

AGRICULTURAL ECOSYSTEM; AGRICULTURAL LAND; MAPPING METHOD; MODIS; SATELLITE DATA; SPATIAL RESOLUTION;

UKRAINE;

EID: 84982913053     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2016.1194545     Document Type: Article

References (93)

1. C.Alcantara, T.Kuemmerle, A.V.Prishchepov, and V.C.Radeloff. 2012. “Mapping Abandoned Agriculture with Multi-Temporal MODIS Satellite Data.” Remote Sensing of Environment 124:334–347. doi:10.1016/j.rse.2012.05.019.
2. J.D.Allen, 1990. “A Look at the Remote Sensing Applications Program of the National Agricultural Statistics Service.” Journal of Official Statistics 6 (4):393–409.
3. D.Arvor, M.Meirelles, V.Dubreuil, A.Bégué, and Y.E.Shimabukuro. 2012. “Analyzing the Agricultural Transition in Mato Grosso, Brazil, Using Satellite-Derived Indices.” Applied Geography 32 (2):702–713. doi:10.1016/j.apgeog.2011.08.007.
4. E.C.Barrett, 2013. Introduction to Environmental Remote Sensing. New York:Routledge.
5. S.A.Bartalev, V.A.Egorov, D.V.Ershov, A.S.Isaev, E.A.Loupian, D.E.Plotnikov, and I.A.Uvarov. 2011. “The Vegetation Mapping over Russia Using MODIS Spectroradiometer Satellite Data.” Contemporary Earth Remote Sensing From Space 8:285–302.
6. S.A.Bartalev, V.A.Egorov, E.A.Loupian, and S.A.Khvostikov. 2014. “A New Locally-Adaptive Classification Method LAGMA for Large-Scale Land Cover Mapping Using Remote-Sensing Data.” Remote Sensing Letters 5 (1):55–64. doi:10.1080/2150704X.2013.870675.
7. S.A.Bartalev, D.E.Plotnikov, and E.A.Loupian. 2016. “Mapping of Arable Land in Russia Using Multi-Year Time Series of MODIS Data and the LAGMA Classification Technique.” Remote Sensing Letters 7 (3):269–278. doi:10.1080/2150704X.2015.1130874.
8. G.E.Battese, R.M.Harter, and W.A.Fuller. 1988. “An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data.” Journal of the American Statistical Association 83 (401):28–36. doi:10.1080/01621459.1988.10478561.
9. U.C.Benz, P.Hofmann, G.Willhauck, I.Lingenfelder, and M.Heynen. 2004. “Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information.” ISPRS Journal of Photogrammetry and Remote Sensing 58 (3–4):239–258. doi:10.1016/j.isprsjprs.2003.10.002.
10. C.M.Biradar, P.S.Thenkabail, P.Noojipady, Y.Li, V.Dheeravath, H.Turral, M.Velpuri, et al. 2009. “A Global Map of Rainfed Cropland Areas (GMRCA) at the End of Last Millennium Using Remote Sensing.” International Journal of Applied Earth Observation and Geoinformation 11 (2):114–129. doi:10.1016/j.jag.2008.11.002.
11. C.Boryan, Z.Yang, R.Mueller, and M.Craig. 2011. “Monitoring US Agriculture:The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program.” Geocarto International 26 (5):341–358. doi:10.1080/10106049.2011.562309.
12. L.Boschetti, S.P.Flasse, and P.A.Brivio. 2004. “Analysis of the Conflict Between Omission and Commission in Low Spatial Resolution Dichotomic Thematic Products:The Pareto Boundary.” Remote Sensing of Environment 91 (3–4):280–292. doi:10.1016/j.rse.2004.02.015.
13. L.Breiman, 2001. “Random Forests.” Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
14. M.L.Campagnolo, and E.L.Montano. 2014. “Estimation of Effective Resolution for Daily MODIS Gridded Surface Reflectance Products.” IEEE Transactions onGeoscience and Remote Sensing 52 (9):5622–5632. doi:10.1109/TGRS.2013.2291496.
15. B.Caputo, K.Sim, F.Furesjo, and A.Smola. 2002. “Appearance-based Object Recognition using SVMs:Which Kernel Should I Use?” Proceedings of NIPS Workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision, Whistler, Vol. 2002.
16. C.Champagne, H.McNairn, B.Daneshfar, and J.Shang. 2014. “A Bootstrap Method for Assessing Classification Accuracy and Confidence for Agricultural Land Use Mapping in Canada.” International Journal of Applied Earth Observation and Geoinformation 29:44–52. doi:10.1016/j.jag.2013.12.016.
17. B.Chen, F.Qiu, B.Wu, and H.Du. 2015. “Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty.” Remote Sensing 7 (5):5980–6004. doi:10.3390/rs70505980.
18. C.Conrad, R.R.Colditz, S.Dech, D.Klein, and P.L.G.Vlek. 2011. “Temporal Segmentation of MODIS Time Series for Improving Crop Classification in Central Asian Irrigation Systems.” International Journal of Remote Sensing 32 (23):8763–8778. doi:10.1080/01431161.2010.550647.
19. C.Conrad, S.Dech, O.Dubovyk, S.Fritsch, D.Klein, F.Löw, G.Schorcht, and J.Zeidler. 2014. “Derivation of Temporal Windows for Accurate Crop Discrimination in Heterogeneous Croplands of Uzbekistan Using Multitemporal Rapideye Images.” Computers and Electronics in Agriculture 103:63–74. doi:10.1016/j.compag.2014.02.003.
20. C.Conrad, S.Fritsch, J.Zeidler, G.Rücker, and S.Dech. 2010. “Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data.” Remote Sensing 2 (4):1035–1056. doi:10.3390/rs2041035.
21. J.L.Cushnie, 1987. “The Interactive Effect of Spatial Resolution and Degree of Internal Variability within Land-Cover Types on Classification Accuracies.” International Journal of Remote Sensing 8 (1):15–29. doi:10.1080/01431168708948612.
22. A.G.Definiens, 2009. Definiens Ecognition Developer 8 User Guide. Munchen, Germany:Definens AG.
23. J.Delrue, L.Bydekerke, H.Eerens, S.Gilliams, I.Piccard, and E.Swinnen. 2013. “Crop Mapping in Countries with Small-Scale Farming:A Case Study for West Shewa, Ethiopia.” International Journal of Remote Sensing 34 (7):2566–2582. doi:10.1080/01431161.2012.747016.
24. V.Dheeravath, P.S.Thenkabail, G.Chandrakantha, P.Noojipady, G.P.O.Reddy, C.M.Biradar, M.K.Gumma, and M.Velpuri. 2010. “Irrigated Areas of India Derived Using MODIS 500 M Time Series for the Years 2001–2003.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1):42–59. doi:10.1016/j.isprsjprs.2009.08.004.
25. P.C.Doraiswamy, J.L.Hatfield, T.J.Jackson, B.Akhmedov, J.Prueger, and A.Stern. 2004. “Crop Condition and Yield Simulations Using Landsat and MODIS.” Remote Sensing of Environment 92 (4):548–559. doi:10.1016/j.rse.2004.05.017.
26. G.Duveiller, and P.Defourny. 2010. “A Conceptual Framework to Define the Spatial Resolution Requirements for Agricultural Monitoring Using Remote Sensing.” Remote Sensing of Environment 114 (11):2637–2650. doi:10.1016/j.rse.2010.06.001.
27. G.Duveiller, R.Lopez-Lozano, and A.Cescatti. 2015. “Exploiting the Multi-Angularity of the MODIS Temporal Signal to Identify Spatially Homogeneous Vegetation Cover:A Demonstration for Agricultural Monitoring Applications.” Remote Sensing of Environment 166:61–77. doi:10.1016/j.rse.2015.06.001.
28. S.Estel, T.Kuemmerle, C.Alcántara, C.Levers, A.Prishchepov, and P.Hostert. 2015. “Mapping Farmland Abandonment and Recultivation across Europe Using MODIS NDVI Time Series.” Remote Sensing of Environment 163:312–325. doi:10.1016/j.rse.2015.03.028.
29. S.Foerster, K.Kaden, M.Foerster, and S.Itzerott. 2012. “Crop Type Mapping Using Spectral–Temporal Profiles and Phenological Information.” Computers and Electronics in Agriculture 89:30–40. doi:10.1016/j.compag.2012.07.015.
30. J.A.Foley, N.Ramankutty, K.A.Brauman, E.S.Cassidy, J.S.Gerber, M.Johnston, N.D.Mueller, et al. 2011. “Solutions for a Cultivated Planet.” Nature 478 (7369):337–342. doi:10.1038/nature10452.
31. S.Fritz, and L.See. 2008. “Identifying and Quantifying Uncertainty and Spatial Disagreement in the Comparison of Global Land Cover for Different Applications.” Global Change Biology 14 (5):1057–1075. doi:10.1111/j.1365-2486.2007.01519.x.
32. S.Fritz, L.See, I.McCallum, L.You, A.Bun, E.Moltchanova, M.Duerauer, et al. 2015. “Mapping Global Cropland and Field Size.” Global Change Biology 21 (5):1980–1992. doi:10.1111/gcb.12838.
33. S.Fritz, L.See, and F.Rembold. 2010. “Comparison of Global and Regional Land Cover Maps with Statistical Information for the Agricultural Domain in Africa.” International Journal of Remote Sensing 31 (9):2237–2256. doi:10.1080/01431160902946598.
34. F.J.Gallego, N.Kussul, S.Skakun, O.Kravchenko, A.Shelestov, and O.Kussul. 2014. “Efficiency Assessment of Using Satellite Data for Crop Area Estimation in Ukraine.” International Journal of Applied Earth Observation and Geoinformation 29:22–30. doi:10.1016/j.jag.2013.12.013.
35. C.Giri, B.Pengra, J.Long, and T.R.Loveland. 2013. “Next Generation of Global Land Cover Characterization, Mapping, and Monitoring.” International Journal of Applied Earth Observation and Geoinformation 25:30–37. doi:10.1016/j.jag.2013.03.005.
36. T.O.Hammond, and D.L.Verbyla. 1996. “Optimistic Bias in Classification Accuracy Assessment.” International Journal of Remote Sensing 17 (6):1261–1266. doi:10.1080/01431169608949085.
37. F.Hannerz, and A.Lotsch. 2006. Assessment of Land Use and Cropland Inventories for Africa. CEEPA Discussion Papers 22. Pretoria:Centre of Environmental Economics and Policy in Africa, University of Pretoria.
38. G.J.Husak, M.T.Marshall, J.Michaelsen, D.Pedreros, C.Funk, and G.Galu. 2008. “Crop Area Estimation Using High and Medium Resolution Satellite Imagery in Areas with Complex Topography.” Journal of Geophysical Research:Atmospheres (1984–2012) 113 (D14). doi:10.1029/2007JD009175.
39. P.Jönsson, and L.Eklundh. 2004. “TIMESAT–A Program for Analyzing Time-Series of Satellite Sensor Data.” Computers & Geosciences 30 (8):833–845. doi:10.1016/j.cageo.2004.05.006.
40. C.Justice, P.Defourny, et al. 2007. “Developing a Strategy for Global Agricultural Monitoring in the Framework of Group on Earth Observations (GEO) Workshop Report.” In GEOSS Operational Agricultural Monitoring System. Rome:Food and Agriculture Organization.
41. W.H.Kruskal, and W.Allen Wallis. 1952. “Use of Ranks in One-Criterion Variance Analysis.” Journal of the American Statistical Association 47 (260):583–621. doi:10.1080/01621459.1952.10483441.
42. N.Kussul, S.Skakun, A.Shelestov, M.Lavreniuk, B.Yailymov, and O.Kussul. 2015. “Regional Scale Crop Mapping Using Multi-Temporal Satellite Imagery.” ISPRS -International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3:45–52. doi:10.5194/isprsarchives-XL-7-W3-45-2015.
43. N.Kussul, S.Skakun, A.Shelestov, and O.Kussul. 2014. “The Use of Satellite SAR Imagery to Crop Classification in Ukraine within JECAM Project.” In Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, 1497–1500. IEEE.
44. M.-J.Lambert, F.Waldner, and P.Defourny. 2016. “Cropland Mapping over Sahelian and Sudanian Agrosystems:A Knowledge-Based Approach Using PROBA-V Time Series at 100-m.” Remote Sensing 8 (3):232. doi:10.3390/rs8030232.
45. B.A.Latif, R.Lecerf, G.Mercier, and L.Hubert-Moy. 2008. “Preprocessing of Low-Resolution Time Series Contaminated by Clouds and Shadows.” IEEE Transactions on Geoscience and Remote Sensing 46 (7):2083–2096. doi:10.1109/TGRS.2008.916473.
46. J.Li, X.Chen, L.Tian, J.Huang, and L.Feng. 2015. “Improved Capabilities of the Chinese High-Resolution Remote Sensing Satellite GF-1 for Monitoring Suspended Particulate Matter (SPM) in Inland Waters:Radiometric and Spatial Considerations.” ISPRS Journal of Photogrammetry and Remote Sensing 106:145–156. doi:10.1016/j.isprsjprs.2015.05.009.
47. F.Löw, U.Michel, S.Dech, and C.Conrad. 2013. “Impact of Feature Selection on the Accuracy and Spatial Uncertainty of Per-Field Crop Classification Using Support Vector Machines.” ISPRS Journal of Photogrammetry and Remote Sensing 85:102–119. doi:10.1016/j.isprsjprs.2013.08.007.
48. F.Löw, and G.Duveiller. 2014. “Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing.” Remote Sensing 6 (9):9034–9063. doi:10.3390/rs6099034.
49. F.Löw, P.Knöfel, and C.Conrad. 2015. “Analysis of Uncertainty in Multi-Temporal Object-Based Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 105:91–106. doi:10.1016/j.isprsjprs.2015.03.004.
50. G.Mallinis, and N.Koutsias. 2012. “Comparing Ten Classification Methods for Burned Area Mapping in a Mediterranean Environment Using Landsat TM Satellite Data.” International Journal of Remote Sensing 33 (14):4408–4433. doi:10.1080/01431161.2011.648284.
51. N.Matton, G.S.Canto, F.Waldner, S.Valero, D.Morin, J.Inglada, M.Arias, S.Bontemps, B.Koetz, and P.Defourny. 2015. “An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series.” Remote Sensing 7 (10):13208–13232. doi:10.3390/rs71013208.
52. Q.McNemar, 1947. “Note on the Sampling Error of the Difference between Correlated Proportions or Percentages.” Psychometrika 12 (2):153–157. doi:10.1007/BF02295996.
53. U.Meier, D.C.Ciresan, L.M.Gambardella, and J.Schmidhuber. 2011. “Better Digit Recognition with a Committee of Simple Neural Nets.” In 2011 International Conference on Document Analysis and Recognition (ICDAR), 1250–1254. IEEE.
54. J.Meng, X.Du, and B.Wu. 2013. “Generation of High Spatial and Temporal Resolution NDVI and Its Application in Crop Biomass Estimation.” International Journal of Digital Earth 6 (3):203–218. doi:10.1080/17538947.2011.623189.
55. Z.Miao, L.Qiangzi, and W.Bingfang. 2012. “Investigating the Capability of Multi-Temporal Landsat Images for Crop Identification in High Farmland Fragmentation Regions.” In 2012 First International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 1–4. IEEE.
56. K.Millard, and M.Richardson. 2015. “On the Importance of Training Data Sample Selection in Random Forest Image Classification:A Case Study in Peatland Ecosystem Mapping.” Remote Sensing 7 (7):8489–8515. doi:10.3390/rs70708489.
57. H.Müller, P.Rufin, P.Griffiths, A.J.Siqueira, and P.Hostert. 2015. “Mining Dense Landsat Time Series for Separating Cropland and Pasture in a Heterogeneous Brazilian Savanna Landscape.” Remote Sensing of Environment 156:490–499. doi:10.1016/j.rse.2014.10.014.
58. T.Murakami, S.Ogawa, N.Ishitsuka, K.Kumagai, and G.Saito. 2001. “Crop Discrimination with Multitemporal SPOT/HRV Data in the Saga Plains, Japan.” International Journal of Remote Sensing 22 (7):1335–1348. doi:10.1080/01431160151144378.
59. P.Olofsson, S.V.Stehman, C.E.Woodcock, D.Sulla-Menashe, A.M.Sibley, J.D.Newell, M.A.Friedl, and M.Herold. 2012. “A Global Land-Cover Validation Data Set, Part I:Fundamental Design Principles.” International Journal of Remote Sensing 33 (18):5768–5788. doi:10.1080/01431161.2012.674230.
60. M.Ozdogan, and C.E.Woodcock. 2006. “Resolution Dependent Errors in Remote Sensing of Cultivated Areas.” Remote Sensing of Environment 103 (2):203–217. doi:10.1016/j.rse.2006.04.004.
61. Y.Pan, T.Hu, X.Zhu, J.Zhang, and X.Wang. 2012. “Mapping Cropland Distributions Using a Hard and Soft Classification Model.” IEEE Transactions on Geoscience and Remote Sensing 50 (11):4301–4312. doi:10.1109/TGRS.2012.2193403.
62. J.M.Peña-Barragán, M.K.Ngugi, R.E.Plant, and J.Six. 2011. “Object-Based Crop Identification Using Multiple Vegetation Indices, Textural Features and Crop Phenology.” Remote Sensing of Environment 115 (6):1301–1316. doi:10.1016/j.rse.2011.01.009.
63. D.Pflugmacher, O.N.Krankina, W.B.Cohen, M.A.Friedl, D.Sulla-Menashe, R.E.Kennedy, P.Nelson, et al. 2011. “Comparison and Assessment of Coarse Resolution Land Cover Maps for Northern Eurasia.” Remote Sensing of Environment 115 (12):3539–3553. doi:10.1016/j.rse.2011.08.016.
64. K.Pittman, M.C.Hansen, I.Becker-Reshef, P.V.Potapov, and C.O.Justice. 2010. “Estimating Global Cropland Extent with Multi-Year MODIS Data.” Remote Sensing 2 (7):1844–1863. doi:10.3390/rs2071844.
65. D.Plotnikov, S.A.Bartalev, and V.A.Egorov. 2010. “The Recognition Features to Map Arable Lands Based on Multi-Annual MODIS Earth Observation Data [In Russian].” Contemporary Remote Sensing from Space 7:330–341.
66. R.G.Pontius, and M.Millones. 2011. “Death to Kappa:Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment.” International Journal of Remote Sensing 32 (15):4407–4429. doi:10.1080/01431161.2011.552923.
67. A.Savitzky, and M.J.E.Golay. 1964. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures.” Analytical Chemistry 36 (8):1627–1639. doi:10.1021/ac60214a047.
68. Y.Shao, and R.S.Lunetta. 2012. “Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points.” ISPRS Journal of Photogrammetry and Remote Sensing 70:78–87. doi:10.1016/j.isprsjprs.2012.04.001.
69. A.Y.Shelestov, A.N.Kravchenko, S.V.Skakun, S.V.Voloshin, and N.N.Kussul. 2013. “Geospatial Information System for Agricultural Monitoring.” Cybernetics and Systems Analysis 49 (1):124–132. doi:10.1007/s10559-013-9492-5.
70. J.Shen, J.Liu, X.Lin, R.Zhao, and S.Xu. 2011. “Cropland Extraction from Very High Spatial Resolution Satellite Imagery by Object-Based Classification Using Improved Mean Shift and One-Class Support Vector Machines.” Sensor Letters 9 (3):997–1005. doi:10.1166/sl.2011.1361.
71. S.Skakun, N.Kussul, A.Y.Shelestov, M.Lavreniuk, and O.Kussul. 2015. “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 PP 99:1–8.
72. S.V.Skakun, and R.M.Basarab. 2014. “Reconstruction of Missing Data in Time-Series of Optical Satellite Images Using Self-Organizing Kohonen Maps.” Journal of Automation and Information Sciences 46:19–26. doi:10.1615/JAutomatInfScien.v46.i12.
73. Soares, D. de M., L. S. Galvão, and A. R. Formaggio. 2008. “Crop Area Estimate from Original and Simulated Spatial Resolution Data and Landscape Metrics.” Scientia Agricola 65 (5):459–467. doi:10.1590/S0103-90162008000500003.
74. S.P.Sweeney, and T.P.Evans. 2012. “An Edge-Oriented Approach to Thematic Map Error Assessment.” Geocarto International 27 (1):31–56. doi:10.1080/10106049.2011.622052.
75. B.Tan, C.E.Woodcock, J.Hu, P.Zhang, M.Ozdogan, D.Huang, W.Yang, Y.Knyazikhin, and R.B.Myneni. 2006. “The Impact of Gridding Artifacts on the Local Spatial Properties of MODIS Data:Implications for Validation, Compositing, and Band-To-Band Registration across Resolutions.” Remote Sensing of Environment 105 (2):98–114. doi:10.1016/j.rse.2006.06.008.
76. P.S.Thenkabail, and Z.Wu. 2012. “An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data.” Remote Sensing 4 (12):2890–2918. doi:10.3390/rs4102890.
77. F.Tsai, and W.Philpot. 1998. “Derivative Analysis of Hyperspectral Data.” Remote Sensing of Environment 66 (1):41–51. doi:10.1016/S0034-4257(98)00032-7.
78. T.G.Van Niel, and T.R.McVicar. 2004. “Determining Temporal Windows for Crop Discrimination with Remote Sensing:A Case Study in South-Eastern Australia.” Computers and Electronics in Agriculture 45 (1–3):91–108. doi:10.1016/j.compag.2004.06.003.
79. C.Vancutsem, J.-F.Pekel, P.Bogaert, and P.Defourny. 2007. “Mean Compositing, an Alternative Strategy for Producing Temporal Syntheses. Concepts and Performance Assessment for SPOT VEGETATION Time Series.” International Journal of Remote Sensing 28 (22):5123–5141. doi:10.1080/01431160701253212.
80. C.Vancutsem, E.Marinho, F.Kayitakire, L.See, and S.Fritz. 2012. “Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale.” Remote Sensing 5 (1):19–41. doi:10.3390/rs5010019.
81. V.Vapnik, 2000. The Nature of Statistical Learning Theory. New York:Springer Science & Business Media.
82. E.Vintrou, A.Desbrosse, A.Bégué, S.Traoré, C.Baron, and D.L.Seen. 2012a. “Crop Area Mapping in West Africa Using Landscape Stratification of MODIS Time Series and Comparison with Existing Global Land Products.” International Journal of Applied Earth Observation and Geoinformation 14 (1):83–93. doi:10.1016/j.jag.2011.06.010.
83. E.Vintrou, D.Ienco, A.Begue, and M.Teisseire. 2013. “Data Mining, a Promising Tool for Large-Area Cropland Mapping.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (5):2132–2138. doi:10.1109/JSTARS.2013.2238507.
84. F.Waldner, G.S.Canto, and P.Defourny. 2015. “Automated Annual Cropland Mapping Using Knowledge-Based Temporal Features.” ISPRS Journal of Photogrammetry and Remote Sensing 110:1–13. doi:10.1016/j.isprsjprs.2015.09.013.
85. F.Waldner, M.A.B.Ebbe, K.Cressman, and P.Defourny. 2015a. “Operational Monitoring of the Desert Locust Habitat with Earth Observation:An Assessment.” ISPRS International Journal of Geo-Information 4 (4):2379–2400. doi:10.3390/ijgi4042379.
86. F.Waldner, S.Fritz, A.D.Gregorio, and P.Defourny. 2015b. “Mapping Priorities to Focus Cropland Mapping Activities:Fitness Assessment of Existing Global, Regional and National Cropland Maps.” Remote Sensing 7 (6):7959–7986. doi:10.3390/rs70607959.
87. F.Waldner, S.Fritz, A.D.Gregorio, D.Plotnikov, S.Bartalev, N.Kussul, P.Gong, et al. 2016. “A Unified Cropland Layer at 250 m for Global Agriculture Monitoring.” Data 1 (1):3. doi:10.3390/data1010003.
88. B.D.Wardlow, and S.L.Egbert. 2008. “Large-Area Crop Mapping Using Time-Series MODIS 250 M NDVI Data:An Assessment for the US Central Great Plains.” Remote Sensing of Environment 112 (3):1096–1116. doi:10.1016/j.rse.2007.07.019.
89. J.D.Watts, R.L.Lawrence, P.R.Miller, and C.Montagne. 2009. “Monitoring of Cropland Practices for Carbon Sequestration Purposes in North Central Montana by Landsat Remote Sensing.” Remote Sensing of Environment 113 (9):1843–1852. doi:10.1016/j.rse.2009.04.015.
90. W.Wu, R.Shibasaki, P.Yang, Q.Zhou, and H.Tang. 2008. “Remotely Sensed Estimation of Cropland in China:A Comparison of the Maps Derived from Four Global Land Cover Datasets.” Canadian Journal of Remote Sensing 34 (5):467–479. doi:10.5589/m08-059.
91. Z.Wu, P.S.Thenkabail, R.Mueller, A.Zakzeski, F.Melton, L.Johnson, C.Rosevelt, J.Dwyer, J.Jones, and J.P.Verdin. 2014. “Seasonal Cultivated and Fallow Cropland Mapping Using MODIS-Based Automated Cropland Classification Algorithm.” Journal of Applied Remote Sensing 8 (1):083685–083685. doi:10.1117/1.JRS.8.083685.
92. X.Xiao, S.Boles, J.Liu, D.Zhuang, S.Frolking, C.Li, W.Salas, and B.Moore. 2005. “Mapping Paddy Rice Agriculture in Southern China Using Multi-Temporal MODIS Images.” Remote Sensing of Environment 95 (4):480–492. doi:10.1016/j.rse.2004.12.009.
93. L.Zhong, P.Gong, and G.S.Biging. 2014. “Efficient Corn and Soybean Mapping with Temporal Extendability:A Multi-Year Experiment Using Landsat Imagery.” Remote Sensing of Environment 140:1–13. doi:10.1016/j.rse.2013.08.023.