Infrared and Laser Engineering, Volume. 50, Issue 5, 20200318(2021)

Crop classification of modern agricultural park based on time-series Sentinel-2 images

Dongyan Zhang1, Zhen Dai1,2, Xingang Xu2、*, Guijun Yang2, Yang Meng2, Haikuan Feng2, Qi Hong1, and Fei Jiang1,3
Author Affiliations
  • 1National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
  • 2Beijing Agricultural Information Technology Research Center, Beijing 100097, China
  • 3School of Information Engineering, Suzhou University, Suzhou 234000, China
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    References(23)

    [1] Chunjiang Zhao. Advances of research and application in remote sensing for agriculture. Transactions of the Chinese Society for Agricultural Machinery, 45, 277-293(2014).

    [2] Zijuan Zhao, Dong Liu, Zhongqiao Hang. Research status and prospects of crop remote sensing recognition methods. Jiangsu Agricultural Sciences, 47, 45-51(2019).

    [3] Chenghai Yang, H Everitt James, Murden Dale. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 75, 347-354(2010).

    [4] Wenming Shen, Wenjie Wang, Haijiang Luo, et al. Classification methods of remote sensing image based on decision tree technologies. Remote Sensing Technology and Application, 22, 333-338(2007).

    [5] Jing Tian, Juanle Wang, Yifan Li, et al. Land cover classification in mongolian plateau based on decision tree method: A case study in Tov Province, Mongolia. Journal of Geo-information Science, 16, 460-469(2014).

    [6] M A Friedl, C E Brodley, A H Strahler. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience & Remote Sensing, 37, 969-977(1999).

    [7] Xingang Xu, Qiangzi Li, Wancun Zhou, et al. Classification application of quick bird imagery to obtain crop planting area. Remote Sensing Technology and Application, 23, 17-23(2008).

    [8] Kun Tan, Peijun Du. Hyperspectral remote sensing images classification based on support vector machine. Journal of Infrared and Millimeter Waves, 27, 123-128(2008).

    [9] Xiaohe Gu, Lijian Han, Jinshui Zhang, et al. Monitoring of paddy rice plant area based on similar index by multi-resolution remote sensing data. Scientia Agricultura Sinica, 41, 978-985(2008).

    [10] Chen Pan, Peijun Du, Hairong Zhang. Decision tree classification and application in remote sensing image processing. Science of Surveying and Mapping, 33, 208-211(2008).

    [11] L Brfiman. Random forests. Machine Learning, 45, 5-32(2001).

    [12] Cunjun Li, Jihua Wang, Liangyun Liu, et al. Land cover mapping of winter wheat and clover using muti-temporal Landsat NIR band in a growing season. Transactions of the Chinese Society of Agricultural Engineering, 21, 96-101(2005).

    [13] Xinchuan Li, Xingang Xu, Jihua Wang, et al. Crop classification recognition based on time-series images from HJ satellite. Transactions of the Chinese Society of Agricultural Engineering, 29, 169-176(2013).

    [14] Pengfei Wei, Xingang Xu, Guijun Yang, et al. Remote sensing classification of crops based on the change characteristics of multi-phase vegetation index. Journal of Agricultural Science and Technology, 21, 54-61(2019).

    [15] Qingyun Xu, Guijun Yang, Huiling Long, et al. Crop information identification based on MODIS NDVI time-series data. Transactions of the Chinese Society of Agricultural Engineering, 30, 134-144(2014).

    [16] Qin An, Shengbo Chen, Shichao Sun, et al. Study on corn yield estimation based on multi-temporal MODIS-RVI. Geospatial Information, 16, 14-16, 8(2018).

    [17] Xia Zhang, Quanjun Jiao, Bing Zhang, et al. Preliminary study on cropping pattern mapping using MODIS_EVI image time series. Transactions of the Chinese Society of Agricultural Engineering, 24, 161-165(2008).

    [18] Yaozhong Pan, Le Li, Jinshui Zhang, et al. Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sensing of Environment, 119, 232-242(2012).

    [19] Limin Wang, Jia Liu, Fugui Yang, et al. Rice recognition ability basing on GF-1 multi-temporal phases combined with near infrared data. Transactions of the Chinese Society of Agricultural Engineering, 33, 196-202(2017).

    [20] Yi Liu, Peijun Du, Hui Zheng, et al. Classification of China small satellite remote sensing image based on random forests. Science of Surveying and Mapping, 37, 194-196(2012).

    [21] A L Balogun, S T Yekeen, B Pradhan, et al. Spatio-temporal analysis of oil spill impact and recovery pattern of coastal vegetation and wetland using multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models. Remote Sensing, 12, 1225-1225(2020).

    [22] Haiyang Yu, Gengxing Zhao, Chunyan Chang, et al. Random forest classifier in remote sensing information extraction: A review of applications and future development. Remote Sensing Information, 34, 8-14(2019).

    [23] van der Linden Sebastian, Rabe Andreas, Held Matthias, et al. The en MAP-Box—A toolbox and application programming interface for en MAP data processing. Remote Sensing, 7, 11249-11266(2015).

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    Dongyan Zhang, Zhen Dai, Xingang Xu, Guijun Yang, Yang Meng, Haikuan Feng, Qi Hong, Fei Jiang. Crop classification of modern agricultural park based on time-series Sentinel-2 images[J]. Infrared and Laser Engineering, 2021, 50(5): 20200318

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    Paper Information

    Category: Spectroscopy

    Received: Dec. 7, 2020

    Accepted: --

    Published Online: Aug. 13, 2021

    The Author Email: Xu Xingang (xxgpaper@126.com)

    DOI:10.3788/IRLA20200318

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