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Original Articles

Crop classification using crop knowledge of the previous-year: Case study in Southwest Kansas, USA

, , , , &
Pages 1061-1077 | Received 23 May 2016, Accepted 26 Sep 2016, Published online: 17 Feb 2017

References

  • Breiman L. (2001)—Random forests. Machine Learning, 45 (1): 5–32. doi: http://dx.doi.org/10.1023/a:1010933404324.
  • Breiman L., Cutler A., Liaw A., Wiener M. (2015)—Breiman and Cutler's random forests for classification and regression. Version 4.6-12. Available online at: https://cran.r-project.org/web/packages/randomForest/randomForest.pdf.
  • Brown J.C., Kastens J.H., Coutinho A.C., Victoria D.D., Bishop C.R. (2013)—Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sensing of Environment, 130: 39–50. doi: http://dx.doi.org/10.1016/j.rse.2012.11.009.
  • Congalton R.G. (1991)—A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37 (1): 35–46. doi: http://dx.doi.org/10.1016/0034-4257(91)90048-B.
  • CRESDA (2015)—Gao Fen (GF-1) Satellite. Available online at: http://www.cresda.com/n16/n1130/n188-475/188494.html.
  • Drusch M., Del Bello U., Carlier S., Colin O., Fernandez V., Gascon F., Hoersch B., Isola C., Laberinti P., Martimort P., Meygret A., Spoto F., Sy O., Marchese F., Bargellini P. (2012)—Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120: 25–36. doi: http://dx.doi.org/10.1016/j.rse.2011.11.026.
  • Gallego J., Craig M., Michaelsen J., Bossyns B., Fritz S. (2008)—Best practices for crop area estimation with Remote Sensing. GEOSS Community of Practice Ag 0703a, European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen.
  • Hao P.Y., Zhan Y.L., Wang L., Niu Z., Shakir M. (2015)—Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA. Remote Sensing, 7 (5):5347-5369. doi: http://dx.doi.org/10.3390/rs70505347.
  • Hao P.Y., Niu Z., Wang L., Wang X., Wang C. (2012)—Multi-source automatic crop pattern mapping based on historical vegetation index profiles. Transactions of the Chinese Society of Agricultural Engineering, 28 (23): 123–131.
  • Hao P.Y., Wang L., Zhan Y., Z Niu Z. (2016)—Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China. Isprs International Journal of Geo-Information, 5 (5): 67.
  • Hao P.Y., Wang L., Niu Z., Aablikim A., Huang N., Xu S., Chen F. (2014)—The Potential of Time Series Merged from Landsat-5 TM and HJ-1 CCD for Crop Classification: A Case Study for Bole and Manas Counties in Xinjiang, China. Remote Sensing, 6 (8): 7610–7631. doi: http://dx.doi.org/10.3390/rs6087610.
  • Huang W.J., Huang J.F., Wang X.Z., Wang F.M., Shi J.J. (2013)—Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra. Sensors, 13 (12): 16023–16050.
  • Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. (2002)—Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83 (1–2): 195–213. doi: http://dx.doi.org/10.1016/S0034-4257(02)00096-2.
  • Irons J.R., Dwyer J.L., Barsi J.A. (2012)—The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment, 122: 11–21. doi: http://dx.doi.org/10.1016/j.rse.2011.08.026.
  • Lhermitte S., Verbesselt J., Verstraeten W. W., Coppin P. (2011)—A comparison of time series similarity measures for classification and change detection of ecosystem dynamics. Remote Sensing of Environment, 115 (12): 3129–52. doi: http://dx.doi.org/10.1016/j.rse.2011.06.020.
  • Loosvelt L., Peters J., Skriver H., De Baets B., Verhoest N.E.C. (2012)—Impact of Reducing Polarimetric SAR Input on the Uncertainty of Crop Classifications Based on the Random Forests Algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50 (10): 4185–4200. doi: http://dx.doi.org/10.1109/tgrs.2012.2189012.
  • Low F., Michel U., Dech S., Conrad C. (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: http://dx.doi.org/10.1016/j.isprsjprs.2013.08.007.
  • LP_DAAC (2015)—Vegetation Indices 16-Day L3 Global 250m. Available online at: https://1pdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1. (Accessed June, 23th).
  • Muhammad S., Niu Z., Wang L., Aablikim A., Hao P.Y., Wang C.Y. (2015a)—Crop Classification Based on Time Series MODIS EVI and Ground Observation for Three Adjoining Years in Xinjiang. Spectroscopy and Spectral Analysis, 35 (5): 1345–1350. doi: http://dx.doi.org/10.3964/j.issn.1000-0593(2015)05-1345-06.
  • Muhammad S., Zhan Y., Niu Z., Li Wang, Hao P.Y. (2015b)—Analyzing the Sensitivity of Crops Classification Accuracy Based on MODIS EVI Time Series and History Ground Reference Data. Canadian Journal of Remote Sensing, 41 (6): 536–546. doi: http://dx.doi.org/10.1080/07038992.2015.1112727.
  • Murakami T., Ogawa S., Ishitsuka N., Kumagai K., Saito G. (2001)—Crop discrimination with multitemporal SPOT/HRVdata in the Saga Plains, Japan. International Journal of Remote Sensing, 22 (7): 1335–1348. doi: http://dx.doi.org/10.1080/01431160151144378.
  • NASA (2015)—MODIS: Moderate Resolution Imaging Spectroradiometer. Available online at: http://nsidc.org/data/modis/ (Accessed December, 31, 2015).
  • USDA (2014)—CropScape—Cropland Data Layer. Washington, D.C. Available online at: http://nassgeodata.gmu.edu/CropScape/ (Accessed 02 December).
  • Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Harlan J.C. (1974)—Monitoring the vernal advancements and retrogradation of natural vegetation. In: 1–137. NASA/GSFC.
  • USDA (2015)—National Agricultural Statistics Service, 2013 Kansas Cropland Data Layer. Available online at: http://www.nass.usda.gov/Research_and_Science/Cropland/metadata/metadata_ks14.htm. (Accessed December 11).
  • USDA (2016)—Crop Progress and Condition. Available online at: https://www.nass.usda.gov/Charts_and_Maps/Crop_Progress_&_Condition/ (Accessed 2016/7/29).
  • Wardlow B.D., Egbert S.L., Kastens J.H. (2007)—Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108 (3): 290–310. doi: http://dx.doi.org/10.1016/j.rse.2006.11.021.
  • Wardlow B., Stephen D., Egbert 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, 112 (3): 1096–116. doi: http://dx.doi.org/10.1016/j.rse.2007.07.019.
  • Zhang J.H., Feng L.L., Yao F.M. (2014)—Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information. ISPRS Journal of Photogrammetry and Remote Sensing, 94: 102–113. doi: http://dx.doi.org/10.1016/j.isprsjprs.2014.04.023.
  • Zhang X., Friedl M.A., Schaaf C.B. (2009)—Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. International Journal of Remote Sensing, 30 (8): 2061–2074. doi: http://dx.doi.org/10.1080/01431160802549237.
  • Zhong L.H., Gong P., Biging G.S. (2012a)—Phenology-based Crop Classification Algorithm and its Implications on Agricultural Water Use Assessments in California's Central Valley. Photogrammetric Engineering and Remote Sensing, 78 (8): 799–813.
  • Zhong Y., Zhang L. (2012b)—An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 50 (3): 894–909. doi: http://dx.doi.org/10.1109/tgrs.2011.2162589.