سابِق|تال

Winter wheat planting area extraction over wide area using vegetation red edge information of multi-temporal Sentinel-2 images

  • role： First author第一作者
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100036, China

  • Email：1004490281@qq.com
  • Introduction：田欣媛，研究方向为遥感信息处理与分析。E-mail： 1004490281@qq.com
TIAN Xinyuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100036, China

  • Email：yhzhang@casm.ac.cn
  • Introduction：张永红，研究方向为遥感信息提取与分析以及SAＲ干涉测量形变监测。E-mail： yhzhang@casm.ac.cn
ZHANG Yonghong1*,  
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100036, China

    College of Geodesy and Geomatics, Shangdong University of Science and Technology, Qingdao 266590, China

LIU Rui12,  
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100036, China

WEI Jujie1

ملخص

As the main grain crop in China, winter wheat planting area has great inter-annual variations. Remote Sensing (RS) technology is the most effective method to obtain the planting area data of winter wheat over wide areas. The Normalized Difference Vegetation Index (NDVI) parameter derived from multi-temporal RS images with medium and low resolution, such as MODIS imagery, is frequently used for such purpose. But the accuracy of obtained winter wheat planting area is usually unsatisfactory due to the limited spatial resolution of the used RS images. Sentinel-2A and Sentine-2B, which were launched in 2015 and 2017 respectively, are currently the only spaceborne sensors capable of acquiring images in three red-edge spectral bands with meter-level spatial resolution. Red-edge bands have great potential in discriminating vegetation types and evaluating vegetation health condition, because these bands are sensitive to the chlorophyll and the nitrogen contents of vegetation. However, there are few studies on winter wheat extraction over wide area using the red-edge bands of Sentinel-2 data. In this paper, a wide-area winter wheat extraction method based on the Red-Edge Position Index (REPI) and the NDVI data derived from multi-temporal Sentinel-2 images is proposed, which is then applied to extract the winter wheat planting area in the Beijing-Tianjin-Hebei region for the year of 2020. Firstly, the temporal REPI and NDVI are extracted from Sentinel-2 data. Secondly, the key temporal phases for discriminating winter wheat are studied, and the decision rules based on temporal REPI and NDVI features for winter wheat discrimination are constructed. Finally, the winter wheat planting area in the Beijin-Tianjin-Hebei region in 2020 is extracted using the constructed decision rules. The extracted winter wheat area is validated by two kinds of reference data. It shows an error of -2.57% when compared with the winter wheat planting area data published by the National Bureau of Statistics. Additionally, the overall accuracy of the extracted winter wheat area is 94.24% and the Kappa coefficient is 0.88 in comparison with the visual interpretation results of Google Earth high-resolution images. The conclusions are: (1) REPI changes with the change of chlorophyll concentration during the growth period of winter wheat, and gradually increases with the growth of winter wheat seedlings after sowing period, until reaching the first peak (about 722 nm) at the emergence stage (November). During the overwintering period, it falls below 715 nm. The second peak is reached at heading stage (April-May), with the corresponding value greater than 730 nm. The REPI value in the mature stage drops below 720 nm. Especially, the REPI at heading stage provides excellent separability between winter wheat and woodland, while NDVI has a serious commission error when distinguishing winter wheat from woodland. (2) It can not achieve satisfactory result when either parameter (REPI or NDVI) of single date is used to extract winter wheat over wide areas. However, the decision tree classifier constructed based on the combination of REPI and NDVI parameters derived from multi-temporal Sentinel-2 data can extract winter wheat area with very high accuracy over wide areas, because other land cover types can be well excluded by the specific features presented by either REPI or NDVI of the key temporal phases, thus the effect of “different cover types with similar pectrum” associate with wide-area classification can be largely mitigated. This research demonstrates the significance of the red-edge bands of Sentinel-2 in discriminating winter wheat of wide-area.

مفهوم

winter wheat;area extraction;wide-area;Sentinel-2;REPI;multi-temporal images

References

  1. 1.
    Bi K Y, Niu Z, Huang N, Kang J and Pei J. 2017. Identifying vegetation with decision tree model based on object-oriented method using multi-temporal Sentinel-2A images. Geography and Geo-Information Science, 33(5): 16-20, 27
  2. 2.
    Deng S B. 2014. Remote Sensing Image Processing Method of ENVI(2nd). Beijing: Higher Education Press:136.
  3. 3.
    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 and Bargellini P. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120(none): 25-36
  4. 4.
    Du J B, Zhang J, Wang Z M, Mao D H, Zhang M and Wu B F. 2019. Crop mapping based on Sentinel-2A NDVI time series using object-oriented classification and decision tree model. Journal of Geo-Information Science, 21(5): 740-751
  5. 5.
    Frampton W J, Dash J, Watmough G and Milton E J. 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82(none): 83-92
  6. 6.
    Guo Y K, Liu Y L, Xu M and Zhang X J. 2021. Modeling and analysis of red edge index estimated by leaf area index in road vegetation. Science of Surveying and Mapping, 46(1): 93-98
  7. 7.
    Guo Y L, Li L T, Chen W Q, Cui J Q and Wang Y L. 2020. Research on the estimation of winter wheat chlorophyll content based on red edge spectral and XGBoost algorithm. Infrared, 41(11): 33-43
  8. 8.
    Guo Y S, Liu Q S, Liu G H and Huang C. 2017. Extraction of main crops in yellow river delta based on MODIS NDVI time series. Journal of Natural Resources, 32(10): 1808-1818
  9. 9.
    Guyot G and Baret F. 1988. Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux. Spectral Signatures of Objects in Remote Sensing, 287(287): 279-286.[]
  10. 10.
    Hu Q, Wu W B, Song Q, Yu Q Y, Yang P and Tang H J. 2015. Recent progresses in research of crop patterns mapping by using remote sensing. Scientia Agricultura Sinica, 48(10): 1900-1914
  11. 11.
    Hunt M L, Blackburn G A, Carrasco L, Redhead J W and Rowland C S. 2019. High resolution wheat yield mapping using Sentinel-2. Remote Sensing of Environment, 233(none): 111410-111425
  12. 12.
    Koukoulas S and Blackburn G A. 2001. Introducing new indices for accuracy evaluation of classified images representing semi-natural woodland environments. Photogrammetric Engineering & Remote Sensing, 67(4): 499-510
  13. 13.
    Liang J, Zheng Z W, Xia S T, Zhang X T and Tang Y Y. 2020. Crop recognition and evaluation using red edge features of GF-6 satellite. Journal of Remote Sensing, 24(10): 1168-1179
  14. 14.
    Main-Knorn M, Pflug B, Debaecker V and Louis J. 2015. Calibration and validation plan for the L2A processor and products of the Sentinel-2 mission. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7-W3: 1249-1255
  15. 15.
    Majasalmi T and Rautiainen M. 2016. The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study. Remote Sensing Letters, 7(5): 427-436
  16. 16.
    Massey R, Sankey T T, Congalton R G, Yadav K, Thenkabail P S, Ozdogan M and Sánchez Meador A J. 2017. MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types. Remote Sensing of Environment, 198(none): 490-503
  17. 17.
    Nasrallah A, Baghdadi N, Mhawej M, Faour G, Darwish T, Belhouchette H and Darwich S. 2018. A novel approach for mapping wheat areas using high resolution Sentinel-2 images. Sensors, 18(7): 2089
  18. 18.
    Saini R and Ghosh S K. 2018. Exploring capabilities of Sentinel-2 for vegetation mapping using random forest. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3: 1499-1502
  19. 19.
    Tavakoli H, Mohtasebi S S, Alimardani R and Gebbers R. 2014. Evaluation of different sensing approaches concerning to nondestructive estimation of Leaf Area Index (LAI) for winter wheat. International Journal on Smart Sensing and Intelligent Systems, 7(1): 337-359
  20. 20.
    Tian H F, Pei J, Huang J X, Li X C, Wang J, Zhou B Y, Qin Y C and Wang L. 2020. Garlic and winter wheat identification based on active and passive satellite imagery and the Google Earth engine in Northern China. Remote Sensing, 12(21): 3539-3556
  21. 21.
    Tian Y, Chen Z Q, Hui F M, Cheng X and Ouyang L X. 2019. ESA Sentinel-2A/B satellite: characteristics and applications. Journal of Beijing Normal University (Natural Science), 55(1): 57-65
  22. 22.
    Wang J, Liu L, Li C, Xiang T F, Zheng S L, Ding S B and Liu P J. 2017. The study for formation mechanism of spectral red edge of green vegetation. Spectroscopy and Spectral Analysis, 37(12): 3940-3946
  23. 23.
    Wang L M, Liu J, Yang F G, Yang L B, Yao B M and Wang X L. 2018. Acguisition of winter wheat area in the Beijing-Tianjin-Hebei region with GF-1 satellite data. Acta Agronomica Sinica, 44(5): 762-773
  24. 24.
    Wang R, Feng M C, Yang W D and Zhang M J. 2019. Remote sensing monitoring of winter wheat planting area in county based on Sentinel-2A imagery. Journal of Shanxi Agricultural Sciences, 47(5): 854-860
  25. 25.
    Wei M F, Qiao B J, Zhao J H and Zuo X Y. 2020. The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images. International Journal of Parallel, Emergent and Distributed Systems, 35(3): 297-308
  26. 26.
    Wu B F, Fan J L, Tian Y C, Li Q Z, Zhang L, Liu Z L, Zhang G L, He L H, Huang J L, Jiang X B, Yan C Z, Xu A and Zhang W Q. 2004. A method for crop planting structure inventory and its application. Journal of Remote Sensing, 8(6): 618-627
  27. 27.
    Wu F H, Lang T T, Jiang D, Ding F Y and Zhang Z P. 2019. Study on rapid extraction technology of winter wheat planting area in Beijing-Tianjin-Hebei region. Jiangsu Agricultural Sciences, 47(16): 224-229
  28. 28.
    Xiao L J, Yang W D, Feng M C, Sun H and Wang C. 2020. Effects of drought stress on canopy spectra and red edge parameters of winter wheat. Journal of Shanxi Agricultural University (Natural Science Edition), 40(5): 92-98
  29. 29.
    Yang Y J, Zhan Y L, Tian Q J, Gu X F, Yu T and Wang L. 2015. Crop classification based on GF-1/WFV NDVI time series. Transactions of the Chinese Society of Agricultural Engineering, 31(24):155-161.
  30. 30.
    Yue Z G. 2015. European sentinel-2a satellite is about to sstrut it’s stuff—The brief introduction of Copernicus earth Programme (Part 1). Infrared, 36(8): 34-48
  31. 31.
    Zhang J S, Zhao G Z, Hong Y T, Sun Z H and Duan Y M. 2020. Spatial extraction of winter wheat in Hebei in growing season using pixel-wise phenological curve. Transactions of the Chinese Society of Agricultural Engineering, 36(23): 193-200
  32. 32.
    Zhang S, Zhang J H, Bai Y and Yao F M. 2018. Extracting winter wheat area in Huanghuaihai Plain using MODIS-EVI data and phenology difference avoiding threshold. Transactions of the Chinese Society of Agricultural Engineering, 34(11): 150-158
  33. 33.
    Zhao C J, Wang Z J, Wang J H and Huang W J. 2012. Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra. International Journal of Remote Sensing, 33(11): 3472-3491
  34. 34.
    Zhao Y S. 2013. The Principle and Method of Analysis of Remote Sensing Application(2nd). Beijing: Science Press: 34, 360-362
  35. 35.
    Zhen X J, Zhang X H, Wu M G, Fu X Y, He Y and Hong C Q. 2019. Identification of winter wheat based on Sentinel-2A NDVI times series data. Jiangsu Agricultural Sciences, 47(16): 239-245
  36. 36.
    Zou H Y and Zheng H P. 2010. The effect and method of quantitative analysis of “Red Edge”of vegetation. Remote Sensing Information, 5(4):112-116

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website