Employing the Convolutional LSTM Network in Crop Classification using NDVI Time Series

Document Type : Original Article


1 Ph.D. Student in Remote Sensing Center, Shahid Beheshti University

2 Assistant Prof. of Remote Sensing Center, Faculty of Earth Sciences, Shahid Beheshti University

3 Prof. of Remote Sensing Center, Faculty of Earth Sciences, Shahid Beheshti University

4 Associate Prof. of Remote Sensing Center, Faculty of Earth Sciences, Shahid Beheshti University


Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy and greenness during the growing season is one of the most prominent characteristics of vegetation, including agricultural products, which can be monitored by using time series of vegetation indices that have useful information about the sequence of phenological features of crops. The use of deep learning methods with the ability of learning sequential information obtained from these time series can be useful in crop mapping and reducing dependence on ground data. The LSTM network is one of the types of RNNs in sequential data analysis that has the ability to learn long-term sequences of time-series information. Therefore, in this study, after extracting the NDVI time-series of 9 different dates from Sentinel-2 satellite images for a region located in Moghan plain, with ground labeled data related to the type of crops cultivated, we trained a convolutional LSTM network. Then we used this trained network to classify agricultural products in another region of the plain as a test site, and achieved an overall accuracy of 82% and a kappa coefficient of 0.8. Increasing the number of ground samples and selecting the exact boundary of crops, can increase the efficiency of the method used.


Adams, M.L., Philpot, W.D. and Norvell, W.A., 1999. Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing, 20(18), pp.3663-3675.
Ashourloo, D., Shahrabi, H.S., Azadbakht, M., Rad, A.M., Aghighi, H. & Radiom, S., 2020, A Novel Method for Automatic Potato Mapping Using Time Series of Sentinel-2 Images, Computers and Electronics in Agriculture, 175, P. 105583.
Bannari, A., Morin, D., Bonn, F. & Huete, A., 1995, A Review of Vegetation Indices, Remote Sensing Reviews, 13, PP. 95-120.
Bargiel, D., 2017, A New Method for Crop Classification Combining Time Series of Radar Images and Crop Phenology Information, Remote Sensing of Environment, 198, PP. 369-383.
Bengio, Y., Simard, P. & Frasconi, P., 1994, Learning Long-Term Dependencies with Gradient Descent is Difficult, IEEE Transactions on Neural Networks, 5, PP. 157-166.
Foerster, S., Kaden, K., Foerster, M. & Itzerott, S., 2012, Crop Type Mapping Using Spectral–Temporal Profiles and Phenological Information, Computers and Electronics in Agriculture, 89, PP. 30-40.
Gadiraju, K.K. & Vatsavai, R.R., 2020, Comparative Analysis of Deep Transfer Learning Performance on Crop Classification, In, Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (PP. 1-8).
Gitelson, A.A., Viña, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G. and Leavitt, B., 2003. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical research letters, 30(5).
Hatfield, J.L. & Prueger, J.H., 2010, Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices, Remote Sensing, 2, PP. 562-578.
Hochreiter, S. & Schmidhuber, J., 1997, Long Short-Term Memory, Neural Computation, 9, PP. 1735-1780.
Huete, A. & Tucker, C., 1991, Investigation of Soil Influences in AVHRR Red and Near-Infrared Vegetation Index Imagery, International Journal of Remote Sensing, 12, PP. 1223-1242.
Huete, A., Justice, C. & Liu, H., 1994, Development of Vegetation and Soil Indices for MODIS-EOS, Remote Sensing of Environment, 49, PP. 224-234.
Jia, X., Khandelwal, A., Carlson, K.M., Gerber, J.S., West, P.C., Samberg, L.H. & Kumar, V., 2020, Automated Plantation Mapping in Southeast Asia Using Modis Data and Imperfect Visual Annotations, Remote Sensing, 12, P. 636.
Krizhevsky, A., Sutskever, I. & Hinton, G.E., 2017, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM, 60, PP. 84-90.
Lyu, H., Lu, H. & Mou, L., 2016, Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection, Remote Sensing, 8, P. 506.
Mou, L. & Zhu, X.X., 2018, RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images, arXiv preprint arXiv:1805.02091.
Peña-Barragán, J.M., Ngugi, M.K., Plant, R.E. & Six, J., 2011, Object-Based Crop Identifi-cation Using Multiple Vegetation Indices, Textural Features and Crop Phenology, Remote Sensing of Environment, 115, PP. 1301-1316.
Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. and Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote sensing of environment, 48(2), pp.119-126.
Richardson, A.J. and Wiegand, C.L., 1977. Distinguishing vegetation from soil background information. Photogrammetric engineering and remote sensing, 43(12), pp.1541-1552.
Rogan, J., Franklin, J. & Roberts, D.A., 2002, A Comparison of Methods for Monitoring Multitemporal Vegetation Change Using Thematic Mapper Imagery, Remote Sensing of Environment, 80, PP. 143-156.
Rußwurm, M., & Korner, M., 2017, Temporal Vegetation Modelling Using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-Spectral Satellite Images, In, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (PP. 11-19).
Rußwurm, M. and Körner, M., 2018. Convolutional LSTMs for cloud-robust segmentation of remote sensing imagery. arXiv preprint arXiv:1811.02471.
Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A. & Skakun, S., 2017, Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping, frontiers in Earth Science, 5, 17.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A., 2015, Going Deeper with Convolutions, In, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (PP. 1-9).
Tucker, C.J. and Sellers, P.J., 1986. Satellite remote sensing of primary production. International journal of remote sensing, 7(11), pp.1395-1416.
Xie, Y., Sha, Z. & Yu, M., 2008, Remote Sensing Imagery in Vegetation Mapping: A Review, Journal of Plant Ecology, 1, PP. 9-23.
Zhong, L., Hu, L. & Zhou, H., 2019, Deep Learning Based Multi-Temporal Crop Classification, Remote Sensing of Environment, 221, PP. 430-443.