Mapping paddy rice in China in 2002, 2005, 2010 and 2014 with MODIS time series

Remote Sensing

Volumn 8, Issue 5, 2016, Pages

  Clauss, Kersten ^a   Yan, Huimin ^b   Kuenzer, Claudia ^c  

^a UNIVERSITY OF WÜRZBURG   (Germany)

^b INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^c GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Agriculture; Change detection; China; MODIS; OCSVM; Rice; SVM; Time series

Indexed keywords

AGRICULTURE; CLASSIFICATION (OF INFORMATION); FOOD SUPPLY; GREENHOUSE GASES; IMAGE RETRIEVAL; MAPPING; NETWORK SECURITY; RADIOMETERS; SATELLITE IMAGERY; SUPPORT VECTOR MACHINES; TIME SERIES;

CHANGE DETECTION; CHINA; MODIS; OCSVM; RICE;

TIME SERIES ANALYSIS;

EID: 84971414274     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8050434     Document Type: Article

References (80)

1. FAOSTAT Paddy Rice Production and Trade 2014. Available online: http://faostat3.fao.org (accessed on 3 February 2016).
2. National Bureau of Statistics of China. China Statistical Yearbook 2014; China Statistics Press: Beijing, China, 2014.
3. De Datta, S. Principles and Practices of Rice Production; Wiley: New York, NY, USA, 1981.
4. Kuenzer, C.; Knauer, K. Remote sensing of rice crop areas. Int. J. Remote Sens. 2013, 34, 2101-2139.
5. Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H. Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors 2015, 15, 769-791.
6. Bouvet, A.; Le Toan, T. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sens. Environ. 2011, 115, 1090-1101.
7. Ribbes, F.; Le Toan, T. Rice field mapping and monitoring with RADARSAT data. Int. J. Remote Sens. 1999, 20, 745-765.
8. Le Toan, T.; Ribbes, F.; Floury, N.; Fujita, M.; Kurosu, T.; Wang, L.F.; Floury, N.; Ding, K.H.; Kong, J.A.; Fujita, M.; et al. Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Trans. Geosci. Remote Sens. 1997, 35, 41-56.
9. Lam-Dao, N.; Le Toan, T.; Apan, A.; Bouvet, A.; Young, F.; Le-Van, T. Effects of changing rice cultural practices on C-band synthetic aperture radar backscatter using Envisat advanced synthetic aperture radar data in the Mekong River Delta. J. Appl. Remote Sens. 2009, 3, 033563.
10. Inoue, Y.; Kurosu, T.; Maeno, H.; Uratsuka, S.; Kozu, T.; Dabrowska-Zielinska, K.; Qi, J. Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables. Remote Sens. Environ. 2002, 81, 194-204.
11. Nguyen, D.; Clauss, K.; Cao, S.; Naeimi, V.; Kuenzer, C.; Wagner, W. Mapping rice seasonality in the Mekong Delta with multi-year Envisat ASAR WSM data. Remote Sens. 2015, 7, 15868-15893.
12. Nelson, A.; Setiyono, T.; Rala, A.; Quicho, E.; Raviz, J.; Abonete, P.; Maunahan, A.; Garcia, C.; Bhatti, H.; Villano, L.; et al. Towards an operational SAR-based rice monitoring system in Asia: Examples from 13 demonstration sites across Asia in the RIICE Project. Remote Sens. 2014, 6, 10773-10812.
13. Xiao, X.; Boles, S.; Frolking, S.; Li, C.; Babu, J.Y.; Salas, W.; Moore, B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens. Environ. 2006, 100, 95-113.
14. Gumma, M.K.; Nelson, A.; Thenkabail, P.S.; Singh, A.N. Mapping rice areas of South Asia using MODIS multitemporal data. J. Appl. Remote Sens. 2011, 5, 053547.
15. Manjunath, K.R.; More, R.S.; Jain, N.K.; Panigrahy, S.; Parihar, J.S. Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: A case study for South and Southeast Asian countries. Int. J. Remote Sens. 2015, 36, 6008-6030.
16. Motohka, T.; Nasahara, K.N.; Miyata, A.; Mano, M.; Tsuchida, S. Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset. Int. J. Remote Sens. 2009, 30, 4343-4357.
17. Nguyen, T.T.H.; De Bie, C.A.J.M.; Ali, A.; Smaling, E.M.A.; Chu, T.H. Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis. Int. J. Remote Sens. 2012, 33, 415-434.
18. Peng, D.; Huete, A.R.; Huang, J.;Wang, F.; Sun, H. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 13-23.
19. Boschetti, M.; Nutini, F.; Manfron, G.; Brivio, P.A.; Nelson, A. Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems. PLoS ONE 2014, 9.
20. Boschetti, M.; Nelson, A.; Nutini, F.; Manfron, G.; Busetto, L.; Barbieri, M.; Laborte, A.; Raviz, J.; Holecz, F.; Mabalay, M.; et al. Rapid assessment of crop status: An application of MODIS and SAR data to rice areas in Leyte, Philippines affected by Typhoon Haiyan. Remote Sens. 2015, 7, 6535-6557.
21. Asilo, S.; de Bie, K.; Skidmore, A.; Nelson, A.; Barbieri, M.; Maunahan, A. Complementarity of two rice mapping approaches: Characterizing strata mapped by hypertemporal MODIS and rice paddy identification using multitemporal SAR. Remote Sens. 2014, 6, 12789-12814.
22. Wang, J.; Xiao, X.; Qin, Y.; Dong, J.; Zhang, G.; Kou, W.; Jin, C.; Zhou, Y.; Zhang, Y. Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images. Sci. Rep. 2015, 5, 1-11.
23. Xiao, X.; Boles, S.; Liu, J.; Zhuang, D.; Frolking, S.; Li, C.; Salas, W.; Moore, B. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens. Environ. 2005, 95, 480-492.
24. Xiao, X.; Boles, S.; Frolking, S.; Salas, W.; Moore, B.; Li, C.; He, L.; Zhao, R. Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. Int. J. Remote Sens. 2002, 23, 3009-3022.
25. Sakamoto, T.; Yokozawa, M.; Toritani, H.; Shibayama, M.; Ishitsuka, N.; Ohno, H. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 2005, 96, 366-374.
26. Sakamoto, T.; Van Nguyen, N.; Ohno, H.; Ishitsuka, N.; Yokozawa, M. Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens. Environ. 2006, 100, 1-16.
27. Son, N.T.; Chen, C.F.; Chen, C.R.; Duc, H.N.; Chang, L.Y. A phenology-based classification of time-series MODIS data for rice crop monitoring in Mekong Delta, Vietnam. Remote Sens. 2014, 6, 135-156.
28. Chen, C.F.; Son, N.T.; Chang, L.Y.; Chen, C.R. Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta, Vietnam. Int. J. Remote Sens. 2011, 32, 5115-5134.
29. Guan, X.; Huang, C.; Liu, G.; Meng, X.; Liu, Q. Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sens. 2016, 8, 1-25.
30. Chen, C.-F.; Son, N.-T.; Chen, C.-R.; Cho, K.; Hsiao, Y.-Y.; Chiang, S.-H.; Chang, L.-Y. Assessing rice crop damage and restoration using remote sensing in tsunami-affected areas, Japan. J. Appl. Remote Sens. 2015, 9, 096002-1-096002-19.
31. Frolking, S.; Qiu, J.; Boles, S.; Xiao, X.; Liu, J.; Zhuang, Y.; Li, C.; Qin, X. Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Glob. Biogeochem. Cycles 2002, 16, 38:1-38:10.
32. Sun, H.; Huang, J.; Huete, A.R.; Peng, D.; Zhang, F. Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China. J. Zhejiang Univ. Sci. A 2009, 10, 1509-1522.
33. Qiu, B.; Li, W.; Tang, Z.; Chen, C.; Qi, W. Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecol. Indic. 2015, 56, 79-86.
34. Qiu, B.; Qi, W.; Tang, Z.; Chen, C.; Wang, X. Rice cropping density and intensity lessened in southeast China during the twenty-first century. Environ. Monit. Assess. 2016, 188, 1-12.
35. Shi, J.; Huang, J. Monitoring spatio-temporal distribution of rice planting area in the Yangtze River Delta region using MODIS images. Remote Sens. 2015, 7, 8883-8905.
36. Jin, C.; Xiao, X.; Dong, J.; Qin, Y.;Wang, Z. Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China. Front. Earth Sci. 2016, 10, 49-62.
37. Wang, J.; Huang, J.; Zhang, K.; Li, X.; She, B.;Wei, C.; Gao, J.; Song, X. Rice fields mapping in fragmented area using multi-temporal HJ-1A/B CCD images. Remote Sens. 2015, 7, 3467-3488.
38. Shi, J.; Huang, J.; Zhang, F. Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data. J. Zhejiang Univ. Sci. B 2013, 14, 934-946.
39. Zhou, Y.; Xiao, X.; Qin, Y.; Dong, J.; Zhang, G.; Kou, W.; Jin, C.; Wang, J.; Li, X. Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. Int. J. Appl. Earth Obs. Geoinf. 2016, 46, 1-12.
40. Zhao, Q.; Lenz-Wiedemann, V.; Yuan, F.; Jiang, R.; Miao, Y.; Zhang, F.; Bareth, G. Investigating within-field variability of rice from high resolution satellite imagery in Qixing Farm County, Northeast China. ISPRS Int. J. Geo-Inf. 2015, 4, 236-261.
41. Kontgis, C.; Schneider, A.; Ozdogan, M. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data. Remote Sens. Environ. 2015, 169, 255-269.
42. Dong, J.; Xiao, X.; Kou, W.; Qin, Y.; Zhang, G.; Li, L.; Jin, C.; Zhou, Y.;Wang, J.; Biradar, C.; Liu, J.; Moore, B. Tracking the dynamics of paddy rice planting area in 1986-2010 through time series Landsat images and phenology-based algorithms. Remote Sens. Environ. 2015, 160, 99-113.
43. Dong, J.; Xiao, X.; Menarguez, M.A.; Zhang, G.; Qin, Y.; Thau, D.; Biradar, C.; Moore, B. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 2016.
44. He, H.; Garcia, E.A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 2009, 21, 1263-1284.
45. Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011, 66, 247-259.
46. Tax, D.M.J.; Duin, R.P.W. Support vector data description. Mach. Learn. 2004, 54, 45-66.
47. Liu, B.; Dai, Y.; Li, X.; Lee, W.S.; Yu, P.S. Building text classifiers using positive and unlabeled examples. In Proceedings of the Third IEEE International Conference on Data Mining, Melbourne, FL, USA, 19-22 November 2003; pp. 179-186.
48. Schölkopf, B.; Williamson, R.C.; Smola, A.J.; Shawe-Taylor, J.; Platt, J.C. Support Vector Method for Novelty Detection. In 13th Annual Neural Information Processing Systems Conference (NIPS 1999); Solla, S.A., Leen, T.K., Müller, K.-R., Eds.; MIT Press: Denver, CO, USA, 2000; pp. 582-588.
49. Schölkopf, B.; Platt, J.C.; Shawe-Taylor, J.; Smola, A.J.; Williamson, R.C. Estimating the support of a high-dimensional distribution. Neural Comput. 2001, 13, 1443-1471.
50. National Bureau of Statistics of China. China Statistical Yearbook 2002; China Statistics Press: Beijing, China, 2002.
51. National Bureau of Statistics of China. China Statistical Yearbook 2005; China Statistics Press: Beijing, China, 2005.
52. National Bureau of Statistics of China. China Statistical Yearbook 2010; China Statistics Press: Beijing, China, 2010.
53. Li, L.; Friedl, M.; Xin, Q.; Gray, J.; Pan, Y.; Frolking, S. Mapping crop cycles in China using MODIS-EVI time series. Remote Sens. 2014, 6, 2473-2493.
54. Zhao, D.; Kuenzer, C.; Fu, C.;Wagner, W. Evaluation of the ERS scatterometer-derived soil water index to monitor water availability and precipitation distribution at three different scales in China. J. Hydrometeorol. 2008, 9, 549-562.
55. Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006; NASA EOSDIS Land Processes DAAC. NASA: College Park, MD, USA, 2015.
56. Didan, K. MYD13Q1 MODIS/Aqua Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006; NASA EOSDIS Land Processes DAAC. NASA: College Park, MD, USA, 2015.
57. Huete, A.R.; Justice, C.; van Leeuven, W. MODIS Vegetation Index (MOD13) Algorithm Theoretical Basis Document (ATBD) Version 3; University of Arizona: Tucson, AZ, USA, 1999.
58. Vermote, E.F.; El Saleous, N.Z.; Justice, C.O. Atmospheric correction of MODIS data in the visible to middle infrared: First results. Remote Sens. Environ. 2002, 83, 97-111.
59. Vermote, E.F.; Kotchenova, S. Atmospheric correction for the monitoring of land surfaces. J. Geophys. Res. 2008, 113, 1-12.
60. Vermote, E.F.; Vermeulen, A. Athmospheric Correction Algorithm: Spectral Reflectances (MOD09), MODIS Algorithm Theoretical Basis Document Version 4; NASA: College Park, MD, USA, 1999.
61. Land Processes Distributed Active Archiving Center Data Pool. Available online: http://e4ftl01.cr.usgs.gov/(accessed on 1 June 2015).
62. Chen, J.; Jönsson, P.; Tamura, M.; Gu, Z.; Matsushita, B.; Eklundh, L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens. Environ. 2004, 91, 332-344.
63. Yan, H.; Xiao, X.; Huang, H.; Liu, J.; Chen, J.; Bai, X. Multiple cropping intensity in China derived from agro-meteorological observations and MODIS data. Chin. Geogr. Sci. 2014, 24, 205-219.
64. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer Series in Statistics; Springer New York: New York, NY, USA, 2009.
65. Hijmans, R.; Kapoor, J.; Wieczorek, J.; Garcia, N.; Maunahan, A.; Rala, A.; Mandel, A. Global Administrative Areas, Version 2.0. Available online: http://www.gadm.org/(accessed on 1 May 2015).
66. Baldeck, C.A.; Asner, G.P. Single-species detection with airborne imaging spectroscopy data: A comparison of support vector techniques. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 2501-2512.
67. Drake, J.M.; Randin, C.; Guisan, A. Modelling ecological niches with support vector machines. J. Appl. Ecol. 2006, 43, 424-432.
68. Baldi, P.; Brunak, S.; Chauvin, Y.; Andersen, C.A.F.; Nielsen, H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16, 412-424.
69. Olofsson, P.; Foody, G.M.; Herold, M.; Stehman, S.V.; Woodcock, C.E.; Wulder, M.A. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 2014, 148, 42-57.
70. Powers, D. Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. J. Mach. Learn. Technol. 2011, 2, 37-63.
71. Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 1960, 20, 37-46.
72. Ottinger, M.; Clauss, K.; Kuenzer, C. Aquaculture: Relevance, distribution, impacts and spatial assessments-A review. Ocean Coast. Manag. 2016, 119, 244-266.
73. Liu, J.; Kuang, W.; Zhang, Z.; Xu, X.; Qin, Y.; Ning, J.; Zhou, W.; Zhang, S.; Li, R.; Yan, C.; et al. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J. Geogr. Sci. 2014, 24, 195-210.
74. Okamoto, K.; Kawashima, H. Estimating total area of paddy fields in Heilongjiang, China, around 2000 using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus data. Remote Sens. Lett. 2016, 7, 533-540.
75. Python Software Foundation Python 2.7.9. Available online: https://www.python.org/downloads/release/python-279/(accessed on 12 May 2015).
76. Open Source Geospatial Foundation. GDAL-Geospatial Data Abstraction Library; Version 1.11. Available online: http://gdal.osgeo.org (accessed on 1 February 2015).
77. Van der Walt, S.; Colbert, S.C.; Varoquaux, G. The NumPy Array: A structure for efficient numerical computation. Comput. Sci. Eng. 2011, 13, 22-30.
78. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825-2830.
79. McKinney, W. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, Austin, TX, USA, 28 June-3 July 2010; pp. 51-56.
80. Jones, E.; Oliphant, T.; Peterson, P. SciPy: Open Source Scientific Tools for Python; Version: 0.16.0. Available online: http://www.scipy.org/(accessed on 2 June 2015).