Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia

References

• Atkinson , P.M. and Lewis , P. 2000 . Geostatistical classification for remote sensing: an introduction . Computers and Geosciences , 26 : 361 – 371 .
   Web of Science ®Google Scholar
• Bachelet , D. 1995 . Rice paddy inventory in a few provinces of China using AVHRR data . Geocarto International , 10 : 23 – 38 .
   Google Scholar
• Baret , F. and Guyot , G. 1991 . Potentials and limits of vegetation indices for LAI and APAR assessment . Remote Sensing of Environment , 35 : 161 – 173 .
   Web of Science ®Google Scholar
• Boschetti , M. , Stroppiana , D. , Brivio , P.A. and Bocchi , S. 2009 . Multi-year monitoring of rice crop phenology through time series analysis of MODIS images . International Journal of Remote Sensing , 30 : 4643 – 4662 .
   Web of Science ®Google Scholar
• Bouvet , A. , Le Toan , T. and Lam-Dao , N. 2009 . Monitoring of the rice cropping system in the Mekong Delta using ENVISAT/ASAR dual polarization data . IEEE Transactions on Geoscience and Remote Sensing , 47 : 517 – 526 .
   Web of Science ®Google Scholar
• Congalton , R.G. and Green , K. 1999 . Assessing the Accuracy of Remotely Sensed Data: Principles and Practices , 43 – 70 . Boca Raton , FL : Lewis Publishers .
   Google Scholar
• Congalton , R.G. , Oderwald , R.G. and Mead , R.A. 1983 . Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques . Photogrammetric Engineering and Remote Sensing , 49 : 1671 – 1678 .
   Web of Science ®Google Scholar
• Fang , H. 1998 . Rice crop area estimation of an administrative division in China using remote sensing data . International Journal of Remote Sensing , 17 : 3411 – 3419 .
   Web of Science ®Google Scholar
• Fang , H. , Wu , B. , Liu , H. and Xuan , H. 1998 . Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year . International Journal of Remote Sensing , 3 : 521 – 525 .
   Web of Science ®Google Scholar
• Food Crops Agriculture Department . 2006 . Annual Report of Food Crops , 125 – 135 . Bali Province : Department Agriculture of Local Government .
   Google Scholar
• Gupta , R.K. 1993 . Comparative study of AVHRR ratio vegetation index and normalized difference vegetation index in district level agriculture monitoring . International Journal of Remote Sensing , 14 : 53 – 73 .
   Web of Science ®Google Scholar
• Huete , A. , Didan , K. , Miura , T. , Rodriguez , E.P. , Gao , X. and Ferreira , L.G. 2002 . Overview of the radiometric and biophysical performance of the MODIS vegetation indices . Remote Sensing of Environment , 83 : 195 – 213 .
   Web of Science ®Google Scholar
• Huete , A.R. 1988 . A soil-adjusted vegetation index (SAVI) . Remote Sensing of Environment , 25 : 295 – 309 .
   Web of Science ®Google Scholar
• Huete , A.R. and Escadafal , R. 1991 . Assessment of biophysical soil properties through spectral decomposition techniques . Remote Sensing of Environment , 35 : 149 – 159 .
   Web of Science ®Google Scholar
• Huete , A.R. and Warrick , A.W. 1990 . Assessment of vegetation and soil water regimes in partial canopies with optical remotely sensed data . Remote Sensing of Environment , 32 : 115 – 167 .
   Web of Science ®Google Scholar
• Hutchinson , C.F. 1982 . Techniques for combining Landsat and ancillary data for digital classification improvement . Photogrammetric Engineering and Remote Sensing , 8 : 123 – 130 .
   Google Scholar
• Jensen , J.R. 1986 . Introductory Digital Image Processing: A Remote Sensing Perspective , 205 – 220 . Upper Saddle River , NJ : Prentice-Hall .
   Google Scholar
• Khush , G.S. 2005 . What it will take to feed 5 billion rice consumers in 2030 . International Journal of Molecular Biology , 59 : 1 – 6 .
   Google Scholar
• Le Toan , T. , Ribbes , F. , Wang , L.F. , Floury , N. , Ding , K.H. , Kong , J.A. , Fujita , M. and Kurosu , T. 1997 . Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results . IEEE Transactions on Geoscience and Remote Sensing , 35 : 41 – 56 .
   Web of Science ®Google Scholar
• Liew , S.C. , Kam , S.P. , Tuong , T.P. , Chen , P. , Minh , V.Q. , Balababa , L. and Lim , H. 1998 . Application of multitemporal ERS-2 synthetic aperture radar in delineating rice cropping systems in the Mekong River Delta, Vietnam . IEEE Transactions on Geoscience and Remote Sensing , 36 : 1412 – 1420 .
   Web of Science ®Google Scholar
• Lillesand , T.M. and Kiefer , R.W. 2000 . Remote Sensing and Image Interpretation , 715 – 735 . New York : John Wiley & Sons .
   Google Scholar
• Mas , J.F. and Ramírez , I. 1996 . Comparison of land use classifications obtained by visual interpretation and digital processing . ITC Journal , 4 : 278 – 283 .
   Google Scholar
• Matthews , R.B. , Wassmann , R. , Knox , J.W. and Buendia , L.V. 2000 . Using a crop/soil simulation model and GIS techniques to assess methane emissions from rice fields in Asia: upscaling to national levels . Nutrient Cycling in Agroecosystems , 58 : 201 – 217 .
   Web of Science ®Google Scholar
• National Land Agency . 2008 . Land Use Map of Bali Province , 1 – 2 . Bali Province : Local Government of National Land Agency .
   Google Scholar
• Niel, T.G.V. and McVicar, T.R., 2001, Remote sensing of rice-based irrigated agriculture: a review, Rice CRC Technical Report P1105-01/01. www.ricecrc.org (http://www.ricecrc.org) (Accessed: 5 October 2009 ).
   Google Scholar
• Niel , T.G.V. , McVicar , T.R. , Fang , H. and Liang , S. 2003 . Calculating environmental moisture for per-field discrimination of rice crops . International Journal of Remote Sensing , 24 : 885 – 890 .
   Web of Science ®Google Scholar
• Okamoto , K. and Kawashima , H. 1999 . Estimation of rice-planted area in the tropical zone using a combination of optical and microwave satellite sensor data . International Journal of Remote Sensing , 5 : 1045 – 1048 .
   Web of Science ®Google Scholar
• Pan , X.Z. , Uchida , S. , Liang , Y. , Hirano , A. and Sun , B. 2010 . Discriminating different landuse types by using multitemporal NDXI in a rice planting area . International Journal of Remote Sensing , 31 : 585 – 596 .
   Web of Science ®Google Scholar
• Strahler, A.H., Boschetti, L., Foody, G.M., Friedl, M.A., Hansen, M.C., Herold, M., Mayaux, P., Morisette, J.T., Stehman, S.V. and Woodcock, C.E., 2006, Global land cover validation: recommendations for evaluation and accuracy assessment of global land cover maps. www.nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_25.pdf (http://www.nofc.cfs.nrcan.gc.ca/gofc-gold/Report Series/GOLD_25.pdf) (Accessed: 25 July 2009 ).
   Google Scholar
• Tingting , L. and Chuang , L. 2010 . Study on extraction of crop information using time-series MODIS data in the Chao Phraya Basin of Thailand . Advances in Space Research , 45 : 775 – 784 .
   Web of Science ®Google Scholar
• Xiao , X. , Boles , S. , Liu , J. , Zhuang , D. , Frolking , S. , Li , C. , Salas , W. and Moore , B. 2005 . Mapping paddy rice agriculture in southern China using multi-temporal MODIS images . Remote Sensing of Environment , 95 : 480 – 492 .
   Web of Science ®Google Scholar
• Xiao , X. , Braswell , B. , Zhang , Q. , Boles , S. , Frolking , S. and Moore , B. 2003 . Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia . Remote Sensing of Environment , 84 : 385 – 392 .
   Web of Science ®Google Scholar
• Xiao , X. , He , L. , Salas , W. , Li , C. , Moore , B. and Zhao , R. 2002 . Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields . International Journal of Remote Sensing , 23 : 3595 – 3604 .
   Web of Science ®Google Scholar
• Yang , C.M. , Liu , C.C. and Wang , Y.W. 2008 . Using Formosat-2 satellite data to estimate leaf area index of rice crop . Journal of Photogrammetry and Remote Sensing , 13 : 253 – 260 .
   Google Scholar