The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images

References

• Wang K, Zhao J, Zhu G, et al. Early estimation of winter wheat planting area in Qingyang city by decision tree and pixel unmixing methods based on GF-1 satellite data. Remote Sens Technol Appl. 2018;33(1):158–167.
   Google Scholar
• Franch B, Vermote EF, Becker-Reshef I, et al. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR growing degree day information. Remote Sens Environ. 2015;161131–148. doi: 10.1016/j.rse.2015.02.014
   Web of Science ®Google Scholar
• Quan W, Wang Z. Researches on the extraction of winter wheat planting area using remote sensing method. Remote Sens Land Resour. 2013;25(4):8–15.
   Google Scholar
• Li W, Jiang N. Extraction of winter wheat planting area by object oriented classification method. J Triticeae Crop. 2012;32(4):000701–000705.
   Google Scholar
• Wang L, Guo Y, He J, et al. Classification method by fusion of decision free and SVM based on Sentinel-2A image. J Agr Mach. 2018;8:1–11.
   Google Scholar
• Fieuzal R, Baup F, Maraissicre C. Monitoring wheat and rapeseed by using synchronous optical and radar satellite data—from temporal signatures to crop parameters estimation. Adv Remote Sens. 2013;2(2):162–180. doi: 10.4236/ars.2013.22020
   Google Scholar
• Bi K, Niu Z, Huang N, et al. Identifying vegetation with decision tree model based on object-0riented method using multi-temporal Sentinel-2A images. Geogr Geo-Information Sci. 2017;33(5):16–20.
   Google Scholar
• Bolton DK, Friedl MA. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric For Meteorol. 2013;173(2):74–84. doi: 10.1016/j.agrformet.2013.01.007
   Google Scholar
• Zhou J, Li C, Shi L, et al. Crops distribution information extracted by remote sensing based on decision tree and object-oriented method. J Agr Mach. 2016;47(9):318–326.
   Google Scholar
• Ma Q. Study on crop acreage extraction based on rapid eye remote sensing image [D]; Shandong Normal University, 2012.
   Google Scholar
• Rapinel S, Clément B, Magnanon S, et al. Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image. J Environ Manage. 2014;144:236–246. doi: 10.1016/j.jenvman.2014.05.027
   PubMed Web of Science ®Google Scholar
• Immitzer M, Vuolo F, Atzberger C. First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens (Basel). 2016;8(3):166. doi: 10.3390/rs8030166
   Web of Science ®Google Scholar
• Fang CY, Wang L, Xu HO. A comparative study of different red edge indices for remote sensing detection of urban grassland health status. J Geo-InformaRion Sci. 2017;19(10):1382–1392.
   Google Scholar
• Delegido J, Verrelst J, Meza CM, et al. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur J Agron. 2013;46(46):42–52. doi: 10.1016/j.eja.2012.12.001
   Google Scholar
• Gallego FJ, Kussul N, Skakun S, et al. Efficiency assessment of using satellite data for crop area estimation in Ukraine. Int J Appl Earth Obs Geoinformation. 2014;29(1):22–30. doi: 10.1016/j.jag.2013.12.013
   Google Scholar
• Wu Haiyan, Lu Yuan. Extraction of Rice planting area based on ecognition object oriented classification. J Guangxi Teach Ed Univ; Natural Science Edition 2014;2:81–87.
   Google Scholar
• Kanke Y, Tuba AB, Dalen M, et al. Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Prec Agr. 2016;17(5):507–530. doi: 10.1007/s11119-016-9433-1
   Web of Science ®Google Scholar
• Fernndez-Manso A, Fernndez-Manso O, Quintano C. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int J Appl Earth Obs Geoinformation. 2016;50:170–175. doi: 10.1016/j.jag.2016.03.005
   Web of Science ®Google Scholar
• Majdar RS, Ghassemian H. A probabilistic SVM approach for hyperspectral image classification using spectral and texture features. Int J Remote Sens. 2017;38(15):4265–4284. doi: 10.1080/01431161.2017.1317941
   Web of Science ®Google Scholar
• Wang L, Zhao L, Liu D. A review on the application of SVM in hyperspectral image processing. J Harbin Eng Univ. 2018;39(6):973–983.
   Google Scholar
• Jin J, Zhu HY, Li ZX, et al. Comparison of accuracy of several supervised classification methods in ENVI [J]. Water Conserv Sci Technol Econ. 2014;20(1):146–148.
   Google Scholar