Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500m data for the year 2010

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 91, Issue , 2014, Pages 98-113

  Gumma, Murali Krishna ^a,b   Thenkabail, Prasad S ^c   Maunahan, Aileen ^a   Islam, Saidul ^a,d   Nelson, Andrew ^a  

^a INTERNATIONAL RICE RESEARCH INSTITUTE   (Philippines)

^b INTERNATIONAL CROPS RESEARCH INSTITUTE FOR THE SEMI ARID TROPICS ICRISAT   (India)

^c U S GEOLOGICAL SURVEY   (United States)

^d BANGLADESH RICE RESEARCH INSTITUTE   (Bangladesh)

Author keywords

Bangladesh; Cropping intensity; Field plot information; MODIS NDVI; Seasonal rice mapping; Spectral matching techniques

Indexed keywords

CROPS; ECONOMICS; FOOD SUPPLY; IMAGE RECONSTRUCTION; IRRIGATION; PHOTOMAPPING;

BANGLADESH; CROPPING INTENSITY; FIELD-PLOT INFORMATION; MODIS NDVI; RICE MAPPING; SPECTRAL MATCHING TECHNIQUES;

RADIOMETERS;

AGRICULTURAL LAND; AGRICULTURAL PRACTICE; AGRICULTURAL PRODUCTION; DATA SET; DECISION MAKING; FLOODING; FOOD SECURITY; IRRIGATION; MAPPING METHOD; MODIS; NATURAL RESOURCE; NDVI; PIXEL; RICE; SATELLITE IMAGERY;

BANGLADESH;

EID: 84896308273     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.02.007     Document Type: Article

References (39)

1. Allard, J., Kon, K., Morishima, Y., Kotzian, R., 2005. The crop protection industry's view on rice crop establishment in Asia and their impact on weed management techniques. In: Toriyama, K., Heong, K.L., Hardy, B. (Eds.), Rice is Life: Scientific Perspectives for the 21st Century. Proceedings of the World Rice Research Conference held in Tokyo and Tsukuba, Japan, 4-7 November, 2004. International Rice Research Institute, Los Baños, Philippines, and Japan International Research Center for Agricultural Sciences, Tsukuba, Japan, pp. 205-207.
2. Asaduzzaman, M., Ringler, C., Thurlow, J., Alam, S., 2010. Investing in crop agriculture in Bangladesh for higher growth and productivity, and adaption to climate change. Bangladesh Food Security Investment Forum, 26-27 May, 2010, Dhaka.
3. Badhwar G.D. Automatic corn-soybean classification using landsat MSS data. I. Near-harvest crop proportion estimation. Remote Sens. Environ. 1984, 14:15-29.
4. BBS, 2006. Bangladesh Bureau of Statistics and Ministry of Agriculture, The Government of the People's Republic of Bangladesh. Available at:. (accessed on 20.02.11). http://www.moa.gov.bd/statistics/bag.htm.
5. BBS, 2010. Hand Book of Agriculture Statistics, Bangladesh Bureau of Statistics. (accessed on 20.01.12). http://www.bbs.gov.bd/.
6. BBS, 2011. Hand Book of Agriculture Statistics, Bangladesh Bureau of Statistics. (accessed on 20.01.12). http://www.bbs.gov.bd/webtestapplication/userfiles/image/AgricultureCensus/.
7. Biggs T.W., Thenkabail P.S., Gumma M.K., Scott C.A., Parthasaradhi G.R., Turral H.N. Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India. Int. J. Remote Sens. 2006, 27:4245-4266.
8. 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., Xiao X., Schull M.A., Alankara R.D., Gunasinghe S., Mohideen S. 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.
9. 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.
10. Cihlar J., Xiao Q., Beaubien J., Fung K., Latifovic R. Classification by progressive generalization: a new automated methodology for remote sensing multichannel data. Int. J. Remote Sens. 1998, 19:2685-2704.
11. Cohen, J.E., 2004. IV. Comparing Long-Range Global Population Projections With Historical Experience. World population to 2300. (accessed on 15.07.12). http://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf.
12. Dheeravath V., Thenkabail P.S., Chandrakantha G., Noojipady P., Reddy G.P.O., Biradar C.M., Gumma M.K., Velpuri M. Irrigated areas of India derived using MODIS 500m time series for the years 2001-2003. ISPRS J. Photogramm. Remote Sens. 2010, 65:42-59.
13. FAO, 2013. Arable Land (% Of Land Area), On-line Statistical Database of the Food and Agriculture Organization of the United Nations. Available on line. (accessed on 21.01.13). http://data.worldbank.org/indicator/AG.LND.ARBL.ZS.
14. Frolking S., Yeluripati J.B., Douglas E. New district-level maps of rice cropping in India: a foundation for scientific input into policy assessment. Field Crops Res. 2006, 98:164-177.
15. Homayouni, S., Roux, M., 2003. Material Mapping from Hyperspectral Images using Spectral Matching in Urban Area. In: Landgrebe, P., (Ed.), IEEE Workshop in honour of Prof. Landgrebe, Washington DC, USA.
16. Gaur A., Biggs T.W., Gumma M.K., Parthasaradhi G.R., Turral H. Water scarcity effects on equitable water distribution and land use in Major Irrigation Project - a case study in India. J. Irrig. Drain. Eng. 2008, 134(1):26-35.
17. Goetz S.J., Varlyguin D., Smith A.J., Wright R.K., Prince S.D., Mazzacato M.E., Tringe J., Jantz C., Melchoir B. Application of multitemporal landsat data to map and monitor land cover and land use change in the Chesapeake Bay Watershed. Analysis of Multi-temporal Remote Sensing Images 2004, 223-232. World Scientific Publishers, Singapore. P. Smits, L. Bruzzone (Eds.).
18. Gumma M.K., Thenkabail P.S., Muralikrishna I.V., Velpuri M.N., Gangadhararao P.T., Dheeravath V., Biradar C.M., Acharya Nalan S., Gaur A. Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250m time-series data and spectral matching techniques, in the Krishna River basin (India). Int. J. Remote Sens. 2011, 32:3495-3520.
19. Gumma M.K., Thenkabail P.S., Nelson A. Mapping irrigated areas using MODIS 250 meter time-series data: a study on Krishna River Basin (India). Water 2011, 3:113-131.
20. Idso, C.D., 2011. Estimates of Global Food Production in the Year 2050: Will We Produce Enough to Adequately Feed the World?, pp. 1-43. (accessed on 15.07.12). http://www.co2science.org/education/reports/foodsecurity/.
21. Inoue Y., Sakaiya E., Zhu Y., Takahashi W. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sens. Environ. 2012, 126:210-221.
22. Islam A.S., Bala S.K., Haque M.A. Flood inundation map of Bangladesh using MODIS time-series images. J. Flood Risk Manage. 2010, 3:210-222.
23. Jensen J.R. Introductory Digital Image Processing: A Remote Sensing Perspective 2004, Prentice Hall, Upper Saddle River, NJ, p. 544. third ed.
24. Knight J.F., Lunetta R.L., Ediriwickrema J., Khorram S. Regional scale land-cover characterization using MODIS-NDVI 250m multi-temporal imagery: a phenology based approach. GISci. Remote Sens. 2006, 43:1-23.
25. Le Toan T., Ribbes F., Wang L.-F., Floury N., Ding K.-H., Kong J.A., Fujita M., Kurosu T. Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Trans. Geosci. Remote Sens. 1997, 35:41-56.
26. Leica, 2010. ERDAS Field Guide, vol. 4, October 2010.
27. Lobell D.B., Asner G.P., Ortiz-Monasterio J.I., Benning T.L. Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agric. Ecosyst. Environ. 2003, 94:205-220.
28. Nguyen T.T.H., De Bie C., Ali A., Smaling E., 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.
29. 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.
30. Shao Y., Fan X., Liu H., Xiao J., Ross S., Brisco B., Brown R., Staples G. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sens. Environ. 2001, 76:310-325.
31. 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.
32. Thenkabail P.S., Schull M., Turral H. Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sens. Environ. 2005, 95:317-341.
33. Thenkabail P.S., GangadharaRao P., Biggs T., Gumma M.K., Turral H. Spectral matching techniques to determine historical land use/land cover (LULC) and irrigated areas using time-series AVHRR pathfinder datasets in the Krishna River Basin, India. Photogramm. Eng. Remote Sens. 2007, 73:1029-1040.
34. Thenkabail P.S., Biradar C.M., Noojipady P., Dheeravath V., Li Y., Velpuri M., Gumma M., Gangalakunta O.R.P., Turral H., Cai X., Vithanage J., Schull M.A., Dutta R. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. Int. J. Remote Sens. 2009, 30:3679-3733.
35. Thiruvengadachari, S., Sakthivadivel, R., 1997. Satellite remote sensing for assessment of irrigation system performance: a case study in India. In: Research Report 9, International Irrigation Management Institute, Colombo, Sri Lanka.
36. Varlyguin, D., Wright, R., Goetz, S.J., Prince, S.D., 2001. Advances in land cover classification: a case study from the mid-Atlantic region. In: American Society for Photogrammetry and Remote Sensing, Proceedings, St. Louis, MO, p. 7. http://www.geog.umd.edu/resac.
37. Velpuri N.M., Thenkabail P.S., Gumma M.K., Biradar C.B., Noojipady P., Dheeravath V., Yuanjie L. Influence of resolution in irrigated area mapping and area estimations. Photogramm. Eng. Remote Sens. 2009, 75:1383-1395.
38. Vermote, E.F., Vermeulen, A., 1999. MODIS Algorithm Technical Background Document, Atmospheric Correction Algorithm: Spectral Reflectances (MOD09), NASA, Contract NAS5-96062.
39. WBP, 2012. Water Profile of Bangladesh, Food and Agriculture Organization. (accessed on 20.01.12). http://www.eoearth.org/article/Water_profile_of_Bangladesh.