Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Abstract

Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices, such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress (e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes (crop, tree, soil, water and road) with the support vector machines (SVMs) algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands (red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information (MI), and full bands of on board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.

References

  1. [1]

    C. J. Tucker. Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment, vol. 10, no. 1, pp. 23–32, 1980. DOI: 10.1016/0034-4257(80)90096-6.

  2. [2]

    J. Penuelas, I. Filella, C. Biel, L. Serrano, R. Save. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, vol. 14, no. 10, pp. 1887–1905, 1993. DOI: 10.1080/01431169308954010.

  3. [3]

    M. J. Hill. Vegetation index suites as indicators of vegetation state in grassland and savanna: an analysis with simulated Sentinel 2 data for a North American transect. Remote Sensing of Environment, vol. 137, pp. 94–111, 2013. DOI: 10.1016/j.rse.2013.06.004.

  4. [4]

    W. Rouse, R. H. Haas, J. A. Schell, D. W. Deering. Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the 3rd ERTS Symposium, Washington DC, USA, pp. 309–317, 1974.

  5. [5]

    Y. Gao, J. P. Waler, M. Allahmoradi, A. Monerris, D. Ryu, T. J. Jackson. Optical sensing of vegetation water content: a synthesis study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 4, pp. 1456–1464, 2015. DOI: 10.1109/JSTARS.2015.2398034.

  6. [6]

    B. C. Gao. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, vol. 58, no. 3, pp. 257–266, 1996. DOI: 10.1016/S0034-4257(96)00067-3.

  7. [7]

    T. J. Jackson, D. Y. Chen, M. Cosh, F. Q. Li, M. Anderson, C. Walthall, P. Doriaswamy, E. R. Hunta. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, vol. 92, no. 4, pp. 475–482, 2004. DOI: 10.1016/j.rse.2003.10.021.

  8. [8]

    D. Y. Chen, J. F. Huang, T. J. Jackson. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-wave and short-wave infrared bands. Remote Sensing of Environment, vol. 98, no. 2–3, pp. 225–236, 2005. DOI: 10.1016/j.rse.2005.07.008.

  9. [9]

    D. A. Roberts, R. O. Green, J. B. Adams. Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sensing of Environment, vol. 62, no. 3, pp. 223–240, 1997. DOI: 10.1016/S0034-4257(97)00092-8.

  10. [10]

    M. T. Yilmaz, E. R. Hunt, T. J. Jackson. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sensing of Environment, vol. 112, no. 5, pp. 2514–2522, 2008. DOI: 10.1016/j.rse.2007.11.014.

  11. [11]

    P. Martimor, O. Arino, M. Berger, R. Biasutti, B. Carnicero, U. Del Bello, V. Fernandez, F. Gascon, B. Greco, P. Silvestrin, F. Spoto, O. Sy. Sentinel-2 optical high resolution mission for GMES operational services. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium, IEEE, Barcelona, Spain, pp. 2677–2680, 2007. DOI: 10.1109/IGARSS.2007.4423394.

  12. [12]

    M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort, A. Meygret, F. Spoto, O. Sy, F. Marchese, P. Bargellini. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, vol. 120, pp. 25–26, 2012. DOI: 10.1016/j.rse.2011.11.026.

  13. [13]

    J. Y. Su, D. W. Yi, C. J. Liu, L. Guo, W. H. Chen. Dimension reduction aided hyperspectral image classification with a small-sized training dataset: experimental comparisons. Sensors, vol. 17, no. 12, Article number 2726, 2017. DOI: 10.3390/s17122726.

  14. [14]

    T. X. Zhang, J. Y. Su, C. J. Liu, W. H. Chen, H. Liu, G. Liu. Band selection in Sentinel-2 satellite for agriculture applications. In Proceedings of the 23rd International Conference Automation and Computing, Huddersfield, UK, pp. 1–6, 2017.

  15. [15]

    Z. W. Gao, C. Cecati, S. X. Ding. A Survey of fault diagnosis and fault-tolerant techniques-Part II: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp. 3768–3774, 2015. DOI: 10.1109/TIE.2015.2419013.

  16. [16]

    P. Bromova, P. Skoda, J. Vazny. Classification of spectra of emission line stars using machine learning techniques. International Journal of Automation and Computing, vol. 11, no. 3, pp. 265–273, 2014. DOI: 10.1007/s11633-014-0789-2.

  17. [17]

    M. Pesaresi, C. Corbane, A. Julea, A. J. Florczyk, V. Syrris, P. Soille. Assessment of the added-value of sentinel-2 for detecting built-up areas. Remote Sensing, vol. 8, no. 4, Article number 299, 2016. DOI: 10.3390/rs8040299. DOI: 10.3390/rs8040299.

  18. [18]

    E. Mandanici, G. Bitelli. Preliminary comparison of sentinel-2 and Landsat 8 imagery for a combined use. Remote Sensing, vol. 8, no. 12, Article number 1014, 2016. DOI: 10.3390/rs8121014.

  19. [19]

    H. van der Werff, F. van der Meer. Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing. Remote Sensing, vol. 8, no. 11, Article number 883, 2016. DOI: 10.3390/rs8110883.

  20. [20]

    R. Richter. Atmospheric/Topographic Correction for Satellite Imagery (ATCOR-2/3 User Guide). ATCOR-2/3 User Guide, Version 6.3, pp. 1–71, 2007.

  21. [21]

    C. Emde, R. Buras-Schnell, A. Kylling, B. Mayer, J. Gasteiger, U. Hamann, J. Kylling, B. Richter, C. Pause, T. Dowling, L. Bugliaro. The libRadtran software package for radiative transfer calculations (version 2.0.1). Geoscientific Model Development, vol. 9, no. 5, pp. 1647–1672, 2016. DOI: 10.5194/gmd-9-1647-2016.

  22. [22]

    D. Koller, M. Sahami. Toward Optimal Feature Selection. Technical Report, Stanford InfoLab, Stanford Unversity, USA, 1996.

  23. [23]

    Q. Q. Gu, Z. H. Li, J. W. Han. Generalized fisher score for feature selection. https://doi.org/arxiv.org/abs/1202.3725, 2012.

  24. [24]

    H. C. Peng, F. H. Long, C. Ding. Feature selection based on mutual information criteria of max-dependency, maxrelevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. DOI: 10.1109/TPAMI.2005.159.

  25. [25]

    B. Zhao, J. S. Feng, X. Wu, S. C. Yan. A survey on deep learning-based fine-grained object classification and semantic segmentation. International Journal of Automation and Computing, vol. 14, no. 2, pp. 119–135, 2017. DOI: 10.1007/s11633-017-1053-3.

  26. [26]

    B. E. Boser, I. M. Guyon, V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM, Pittsburgh, USA, pp. 144–152, 1992. DOI: 10.1145/130385.130401.

  27. [27]

    V. N. Vapnik. Statistical Learning Theory, New York, USA: Wiley, 1998.

  28. [28]

    C. M. Gevaert, C. Persello, G. Vosselman. Optimizing multiple kernel learning for the classification of UAV data. Remote Sensing, vol. 8, no. 12, Article number 1025, 2016. DOI: 10.3390/rs8121025.

  29. [29]

    M. Goudjil, M. Koudil, M. Bedda, N. Ghoggali. A novel active learning method using SVM for text classification. International Journal of Automation and Computing, vol. 15, no. 3, pp. 290–298, 2018. DOI: 10.1007/s11633-015-0912-z.

  30. [30]

    Y. H. Yi, D. W. Yang, D. Y. Chen, J. F. Huang. Retrieving crop physiological parameters and assessing water deficiency using MODIS data during the winter wheat growing period. Canadian Journal of Remote Sensing, vol. 33, no. 3, pp. 189–202, 2007. DOI: 10.5589/m07-025.

  31. [31]

    C. Slave. Analysis of agricultural areas using satellite images. International Journal of Academic Research in Environment and Geography, vol. 1, no. 2, pp. 26–32, 2014.

Download references

Acknowledgements

This work was supported by Science and Technology Facilities Council (STFC) under Newton fund (No. ST/N006852/1). Tian-Xiang Zhang would also like to thank Chinese Scholarship Council (CSC) for supporting his study in the UK.

Author information

Correspondence to Jin-Ya Su.

Additional information

Recommended by Associate Editor Jie Zhang

Tian-Xiang Zhang received the B. Eng. degree in flight vehicles design and engineering from Beijing Institue of Technology, China in 2015, and the M. Sc. degree in aerospace engineering from University of Manchester, UK in 2016. In January of 2017, he joined the Loughborough University Center for Autonomous System (LUCAS) Lab as a Ph. D. degree candidate in remote sensing applications for precision agriculture under the supervision of Dr. Cun-Jia Liu and Prof. Wen-Hua Chen, Department of Aeronautical and Automotive Engineering, Loughborough University, UK. He was fully funded by Chinese Scholarship Council (CSC) and now he is a member of the group joining the Newton Fund UK-China Agri-Tech Network Plus by Rothamsted Research on behalf of Science and Technology Facilities Council (STFC) and an IEEE student member.

His research interest is remote sensing applications for precision agriculture, including satellite and UAV image analysis, data assimilation adopting different methods for agricultural variables prediction and water stress analysis.

Jin-Ya Su received the B. Sc. degree in applied mathematics at School of Mathematics and Statistics, Shandong University, China in 2011, the Ph. D. degree in fault diagnosis at Department of Aeronautical and Automotive Engineering, Loughborough University, UK in 2016. Since 2015, he has been a research associate in Centre for Autonomous Systems, Loughborough University, UK. He received the Best Student Paper Award in the 19th International Conference on Automation and Computing (2013), the IEEE-IES Student Paper Travel Award in the 17th International Conference on Industrial Technology (2016), and the Annual ICI Prize from the Institute of Measurement and Control in 2016. In 2015, he received the prestigious Chinese Government Award for Outstanding Self-financed Students Abroad.

His research interests include Kalman filter, machine learning and their applications to autonomous systems such as intelligent vehicle, agricultural information system.

Cun-Jia Liu received the B. Eng. and M. Sc. degrees in guidance, navigation, and control from Beihang University, China in 2005 and 2008, respectively, and the Ph. D. degree in autonomous vehicle control from Loughborough University, UK in 2011. He has been a lecturer of flight dynamics and control with Loughborough University, since 2013.

His research interests include optimization-based control, disturbance-observer-based control, Bayesian information fusion, and their applications to autonomous vehicles for flight control, path planning, decision making, and situation awareness.

Wen-Hua Chen received the M. Sc. and Ph. D. degrees in control engineering from Northeast University, China in 1989 and 1991, respectively. From 1991 to 1996, he was with Department of Automatic Control, Nanjing University of Aeronautics and Astronautics, China. From 1997 to 2000, he was a researcher and then a lecturer of control engineering with the Centre for Systems and Control, University of Glasgow, UK. In 2000, he became a lecturer with Department of Aeronautical and Automotive Engineering, Loughborough University, UK, where he was appointed as a professor in 2012. He is a chartered engineer (CEng) in the UK, a fellow of Institute of Electrical and Electronics Engineering (FIEEE), a fellow of the Institution of Engineering and Technology (FIET) and a fellow of the Institution of Mechanical Engineers (FIMechE).

His research interests include development of advanced control strategies, such as non-linear model predictive control and disturbance-observer-based control, and their applications in aerospace and automotive engineering, and development of unmanned autonomous intelligent systems.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://doi.org/creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, T., Su, J., Liu, C. et al. Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture. Int. J. Autom. Comput. 16, 16–26 (2019). https://doi.org/10.1007/s11633-018-1143-x

Download citation

Keywords

  • Sentinel-2A
  • remote sensing
  • image classification
  • supervised learning
  • precision agriculture