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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 41, 2015 - Issue 6
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Original Articles

Analyzing the Sensitivity of Crops Classification Accuracy Based on MODIS EVI Time Series and History Ground Reference Data

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Pages 536-546 | Received 26 Jan 2015, Accepted 08 Oct 2015, Published online: 18 Dec 2015

REFERENCES

  • Badhwar, G.D. 1984. “Automatic corn–soybean classification using landsat MSS data. I. Near-harvest crop proportion estimation.” Remote Sensing of Environment, Vol. 14(No. 1–3): pp. 15–29
  • Boryan, C., Yang, Z., Mueller, R., and Craig, M. 2011. “Monitoring U.S. agriculture: The U.S. Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program.” Geocarto International, Vol. 26(No. 5): pp. 341–358. doi: 10.1080/10106049.2011.562309.
  • Brown, J.C., Kastens, J.H., Coutinho, A.C., Victoria, D.d.C., and Bishop, C.R. 2013. “Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data.” Remote Sensing of Environment, Vol. 130: pp. 39–50. doi: 10.1016/j.rse.2012.11.009.
  • Campbell, J.B. 2002. Introduction to Remote Sensing. New York, NY: CRC Press.
  • Carpenter, G.A., Gjaja, M.N., Gopal, S., and Woodcock, C.E. 1997. “ART neural networks for remote sensing: Vegetation classification from Landsat TM and terrain data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 35(No. 2): pp. 308–325. doi: 10.1109/36.563271.
  • Clark, M.L., Aide, T.M., Grau, H.R., and Riner, G. 2010. “A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America.” Remote Sensing of Environment, Vol. 114(No. 11): pp. 2816–2832. doi: 10.1016/j.rse.2010.07.001.
  • de Bie, C.A.J.M., Khan, M.R., Smakhtin, V.U., Venus, V., Weir, M.J.C., and Smaling, E.M.A. 2011. “Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping.” International Journal of Remote Sensing, Vol. 32(No. 21): pp. 6673–6693. doi: 10.1080/01431161.2010.512939.
  • Hansen, M.C., Egorov, A., Potapov, P.V., Stehman, S.V., Tyukavina, A., Turubanova, S.A., Roy, D.P., Goetz, S.J., Loveland, T.R., Ju, J., Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T. 2014. “Monitoring conterminous United States (CONUS) land cover change with web-enabled Landsat data (WELD).” Remote Sensing of Environment, Vol. 140:pp. 466–484.
  • Hoerling, M., Eischeid, J., Kumar, A., Leung, R., Mariotti, A., Mo, K., Schubert, S., and Seager, R. 2014. “Causes and predictability of the 2012 Great Plains drought.” Bulletin of the American Meteorological Society, Vol. 95(No. 2): pp. 269–282.
  • Howard, D.M., Wylie, B.K., and Tieszen, L.L. 2012. “Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA.” International Journal of Remote Sensing, Vol. 33(No. 19): pp. 6094–6108. doi: 10.1080/01431161.2012.680617.
  • Jensen, J.R. 2007. Remote Sensing of the Environment: An Earth Resource Perspective (2nd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
  • Johnson, D.M. 2014. “An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States.” Remote Sensing of Environment, Vol. 141: pp. 116–128.
  • Jonsson, P., and Eklundh, L. 2002. “Seasonality extraction by function fitting to time-series of satellite sensor data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 40(No. 8): pp. 1824–1832. doi: 10.1109/TGRS.2002.802519.
  • Jonsson, P., and Eklundh, L. 2004. “TIMESAT—a program for analyzing time-series of satellite sensor data.” Computers & Geosciences, Vol. 30(No. 8): pp. 833–845. doi: 10.1016/j.cageo.2004.05.006.
  • Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon P.J., and Goetz, A.F.H. 1993. “The spectral image processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data.” Remote Sensing of Environment, Vol. 44(No. 2–3): pp. 145–163.
  • Kuenzer, C., and Knauer, K. 2012. “Remote sensing of rice crop areas.” International Journal of Remote Sensing, Vol. 34(No. 6): pp. 2101–2139. doi: 10.1080/01431161.2012.738946.
  • Morton, D.C., DeFries, R.S., Shimabukuro, Y.E., Anderson, L.O., Del Bon Espírito-Santo, F., Hansen, M., and Carroll, M. 2005. “Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data.” Earth Interactions, Vol. 9(No. 8): pp. 1–22. doi: 10.1175/EI139.1.
  • Ratle, F., Camps-Valls, G., and Weston, J. 2010. “Semisupervised neural networks for efficient hyperspectral image classification.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48(No. 5): 2271–2282. doi: 10.1109/TGRS.2009.2037898.
  • Rumelhart, D.E., McClelland, J.L., and Group, P.R. 1995. Parallel Distributed Processing. Cambridge, MA: MIT Press.
  • Sakamoto, T., Gitelson, A.A., and Arkebauer, T.J. 2013. “MODIS-based corn grain yield estimation model incorporating crop phenology information.” Remote Sensing of Environment, Vol. 131: pp. 215–231. doi: 10.1016/j.rse.2012.12.017.
  • Sesnie, S.E., Dickson, B.G., Rosenstock, S.S., and Rundall, J.M. 2011. “A comparison of Landsat TM and MODIS vegetation indices for estimating forage phenology in desert bighorn sheep (Ovis canadensis nelsoni) habitat in the Sonoran Desert, USA.” International Journal of Remote Sensing, Vol. 33(No. 1): pp. 276–286. doi: 10.1080/01431161.2011.592865.
  • Shakir, M., Zheng, N., Li, W., Aablikim, A., Peng-yu, H., and Chang-yao, W. 2015. “Crop classification based on time series MODIS EVI and ground observation for three adjoining years in Xinjiang.” Spectroscopy and Spectral Analysis, Vol. 35(No.5): pp. 1345–1350. doi: 10.3964/j.issn.1000-0593(2015)05-1345-06.
  • Shao, Y., Lunetta, R.S., Ediriwickrema, J., and Iiames, J. 2010. “Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data.” Photogrammetric Engineering & Remote Sensing, Vol. 76(No. 1): pp. 73–84. doi: 10.14358/PERS.76.1.73.
  • Tooke, T.R., Coops, N.C., Goodwin, N.R., and Voogt, J.A. 2009. “Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications.” Remote Sensing of Environment, Vol. 113(No. 2): pp. 398–407.
  • U.S. Department of Agriculture, National Agricultural Statistics Service. 2009. National Agricultural Statistics Service Cropland Data Layer. Published crop-specific data layer, last modified June 26, 2014, http://nassgeodata.gmu.edu/CropScape/.
  • Wardlow, B.D., and Egbert, S.L. 2008. “Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains.” Remote Sensing of Environment, Vol. 112(No. 3): pp. 1096–1116. doi: 10.1016/j.rse.2007.07.019.
  • Wardlow, B., Egbert, S., and Kastens, J. 2007. “Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains.” Remote Sensing of Environment, Vol. 108(No. 3): pp. 290–310. doi: 10.1016/j.rse.2006.11.021.
  • Xian, G., Homer, C., and Fry, J. 2009. “Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods.” Remote Sensing of Environment, Vol. 113(No. 6): pp. 1133–1147.
  • Xiaoping, L., Xia, L., Lin, L., Jinqiang H., and Bin, A. 2008. “An innovative method to classify remote-sensing images using ant colony optimization.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 46(No. 12): pp. 4198–4208. doi: 10.1109/TGRS.2008.2001754.
  • Yanfei, Z., and Liangpei, Z. 2012. “An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 50(No. 3): pp. 894–909. doi: 10.1109/TGRS.2011.2162589.
  • Yanfei, Z., Liangpei, Z., Jianya, G., and Pingxiang, L. 2007. “A supervised artificial immune classifier for remote-sensing imagery.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 45(No. 12): pp. 3957–3966. doi: 10.1109/TGRS.2007.907739.
  • Zhang, L., Zhong, Y., Huang, B., and Li, P. 2007. “A resource limited artificial immune system algorithm for supervised classification of multi/hyper‐spectral remote sensing imagery.” International Journal of Remote Sensing, Vol. 28(No. 7): pp. 1665–1686. doi: 10.1080/01431160600675903.
  • Zhong, L., Gong, P., and Biging, G.S. 2012. “Phenology-based crop classification algorithm and its implications on agricultural water use assessments in California's Central Valley.” Photogrammetric Engineering & Remote Sensing, Vol. 78(No.8): pp. 799–813. doi: 10.14358/PERS.78.8.799.
  • Zhong, L., Gong, P., and Biging, G.S. 2014. “Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery.” Remote Sensing of Environment, Vol. 140: pp. 1–13.
  • Zhong, L., Hawkins, T., Biging, G., and Gong, P. 2011. “A phenology-based approach to map crop types in the San Joaquin Valley, California.” International Journal of Remote Sensing, Vol. 32(No. 22): pp. 7777–7804. doi: 10.1080/01431161.2010.527397.

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