The Use of Multi-temporal Spectral Information to Improve the Classification of Agricultural Crops in Landscapes

  • Ralf WielandEmail author
  • Pablo Rosso
Part of the Innovations in Landscape Research book series (ILR)


Machine learning opens up a wide range of possibilities for crop classification and mapping using satellite data. With the shortening of their revisit cycles, satellites are now able to provide an increasing amount of data with valuable temporal information. We propose a machine learning approach to efficiently analyze multi-temporal data for crop identification and monitoring. This methodology utilizes a Bayesian approach to gradually improve classification accuracy as the temporal resolution increases. Two multispectral satellite configurations were simulated with hyperspectral data and analyzed with a support vector machine approach and a deep learning algorithm. Results showed that both approaches are able to efficiently process information as time progresses and rapidly achieve very high accuracies. The deep learning algorithm has the advantage that the dynamic component, time, is accounted for automatically, without the need of being actively incorporated by the analyst.


Hyperspectral data Temporal data Machine learning Support Vector Machine Deep learning LSTM Remote sensing 



This work was supported by the Federal Ministry of Food (BMELV) and Agriculture and the Ministry of Science, Research and Culture (MWKF) of the State of Brandenburg. Furthermore, I would like to thank our former colleague of the ZALF: Bernd Zbell, who did the data sampling and the preparation of the Excel-tables. My special thanks to the Python community which developed the used software and made it as Free and Open Source Software available.


  1. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow IJ, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray DG, Olah C, Schuster M, Shlens J, Steiner B, Sutskever Il, Talwar K, Tucker PA, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems. CoRR, Volume abs/1603.04467.
  2. Becerra-García RA, García-Bermúdez RV, Joya-Caparrós G, Fernández-Higuera A, Velázquez-Rodríguez C, Velázquez-Mariño M, Cuevas-Beltrán FR, García-Lagos F, Rodráguez-Labrada R (2017) Data mining process for identification of non-spontaneous saccadic movements in clinical electrooculography. Neurocomputing 250:28–36Google Scholar
  3. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: KDD 16 proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, USA, August 13–17, 2016. ACM DL, Pages 785–794Google Scholar
  4. Collet F, others (2015) Keras.
  5. Gilbertson J (2017) Machine learning for object-based crop classification using multi-temporal Landsat-8 imagery. MSc. thesis, Stellenbosch University, 102 ppGoogle Scholar
  6. Gautam RS, Singh D, Mittal A, Sajin P (2008) Application of SVM on satellite images to detect hotspots in Jharia coal field region of India. Adv Space Res 41(11):1784–1792CrossRefGoogle Scholar
  7. Ghazaryan G, Dubovyk O, Löw F, Lavreniuk M, Kolotii A, Schellberg J, Kussul N (2018) A rule-based approach for crop identification using multitemporal and multisensor phenological metrics. Eur J Remote Sens 51(1):511–524CrossRefGoogle Scholar
  8. Griffiths P, Nendel C, Hostert P (2018) Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sens Environ, January 2019:135–151. (Open access)Google Scholar
  9. Hütt C, Waldhoff G (2018) Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata. Eur J Remote Sens 51:62–74Google Scholar
  10. Ji S, Zhang C, Xu A, Duan Y (2018) 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens 10(75):1–17Google Scholar
  11. Jin X, Song K, Du J, Liu H, Wen Z (2017) Comparison of different satellite bands and vegetation indices for estimation of soil organic matter based on simulated spectral configuration. Agric For Meteorol 244–245:57–71CrossRefGoogle Scholar
  12. Mirschel W, Wenkel K-O, Schultz A, Pommerening J, Verch G (2005) Dynamic ontogenesis model for winter rye and winter barley. Eur J Agron 23(2):123–135CrossRefGoogle Scholar
  13. Mura M, Bottalico F, Giannetti F, Bertani R, Giannini R, Mancini M, Orlandini S, Travaglini D, Chirici G (2018) Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems. Int J Appl Earth Obs Geoinf 66:126–134CrossRefGoogle Scholar
  14. Nendel C, Berg M, Kersebaum KC, Mirschel M, Specka X, Wegehenkel M, Wenkel KO, Wieland R (2011) The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecol Model 222(9):1614–1625CrossRefGoogle Scholar
  15. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  16. Qayyum A, Anwar SM, Awais M, Majid M (2017) Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20CrossRefGoogle Scholar
  17. Sharma A, Liu X, Yang X (2018) Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks. Neural Netw 105:346–355Google Scholar
  18. Vincenzi S, Zucchetta M, Franzoi P, Pellizzato M, Pranovi F, De Leo G, Torricelli P (2011) Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy. Ecol Model 222(8):1471–1478CrossRefGoogle Scholar
  19. Vuolo F, Neuwirth M, Immitzer M, Cl Atzberger, Ng W-T (2018) How much does multi-temporal Sentinel-2 data improve crop type classification? Int J Appl Earth Obs Geoinf 72:122–130CrossRefGoogle Scholar
  20. Weir AH, Bragg PL, Porter JP, Rayner JH (1984) A winter wheat crop simulation model without water or nutrient limitations. J Agric Sci Camb 102:371–382CrossRefGoogle Scholar
  21. Wu Y, Yuan M, Dong Sh, Lin L, Liu Y (2018) Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing 275:167–179CrossRefGoogle Scholar
  22. Xia W, Zhu W, Liao B, Chen M, Cai L, Huang L (2018) Novel architecture for long short-term memory used in question classification. Neurocomputing 299:20–31CrossRefGoogle Scholar
  23. Zhong L, Hu L, Gong P, Biging G (2016) Automated mapping of soybean and corn using phenology. ISPRS J Photogramm Remote Sens 119:151–164CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Leibniz Centre for Agricultural Landscape Research (ZALF)MünchebergGermany

Personalised recommendations