Evaluation Of Machine Learning Classification Methods For Rice Detection Using Earth Observation Data: Case Of Senegal

  • Fama Mbengue Laboratoire de Télédétection Appliquée-LTA, Institut des Sciences de la Terre,Université Cheikh Anta DIOP (UCAD) de Dakar, Sénégal Département de Physique, Université Cheikh Anta DIOP (UCAD) de Dakar, Sénégal
  • Gayane Faye Laboratoire de Télédétection Appliquée-LTA Institut des Sciences de la Terre,Université Cheikh Anta DIOP (UCAD) de Dakar, Sénégal
  • Kharouna Talla Département de Physique, Université Cheikh Anta DIOP (UCAD) de Dakar
  • Mamadou Adama Sarr Université Gaston Berger (UGB) de Saint-Louis, Sénégal
  • André Ferrari Université cote d’Azur, OCA, UMR Lagrange, France
  • Modou Mbaye Laboratoire de Télédétection Appliquée-LTA, Institut des Sciences de la Terre,Université Cheikh Anta DIOP (UCAD) de Dakar, Sénégal
  • Mamadou Semina Dramé Laboratoire de Physique de l’atmosphère et de l’océan-Simeon Fongang, Université Cheikh Anta Diop De Dakar, Senegal
  • Papa Sagne Laboratoire de Biostratigraphie-Sédimentologie, Département de Géologie, Université Cheikh Anta Diop de Dakar, Sénégal Laboratoire de Télédétection Appliquée, Institut des Sciences de la Terre, Université Cheikh Anta Diop de Dakar, Sénégal
Keywords: Rice agriculture, Machine Learning, Google Earth Engine


Agriculture is considered one of the most vulnerable sectors to climate change. In addition to rainfed agriculture, irrigated crops such as rice have been developed in recent decades along the Senegal River. This new crop requires reliable information and monitoring systems. Remote sensing data have proven to be very useful for mapping and monitoring rice fields. In this study, a rice classification system based on machine learning to recognize and categorize rice is proposed. Physical interpretations of rice with other land cover classes in relation to the spectral signature should identify the optimal periods for mapping rice plots using three machine learning methods including Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART). The database is composed of field data collected by GPS and high spatial resolution (10 to 30 m) satellite data acquired between January and May 2018. The analysis of the spectral signature of different land cover show that the ability to differentiate rice from other classes depends on the level of rice development. The results show the efficiency of the three classification algorithms with overall accuracies and Kappa coefficients for SVM (96.2%, 94.5%), for CART (97.6%, 96.5%) and for RF (98% 97.1%) respectively. Unmixing analysis was made to verify the classification and compare the accuracy of these three algorithms according to their performance.


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1. ANDS (2020). Situation economique et sociale regional de Saint-Louis 2017-2018, ANDS
2. Azzari, G. & Lobell, D.B. (2017). Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring. Remote Sensing of Environment 202, 64–74.
3. Belder, P., Bouman, B.A.M., Cabangon, R., Guoan, L., Quilang, E.J.P., Yuanhua, L., Spiertz, J.H.J., & Tuong, T.P. (2004). Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia. Agricultural water management 65, 193–210.
4. Ben-Hur, A. & Weston, J. (2010). A user’s guide to support vector machines, in: Data Mining Techniques for the Life Sciences. Springer, pp. 223–239.
5. Boschetti, M., Nutini, F., Manfron, G., Brivio, P.A., & Nelson, A. (2014). Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems. PLoS ONE 9, e88741. https://doi.org/10.1371/journal.pone.0088741
6. Bouman, B.A.M., Humphreys, E., Tuong, T.P., & Barker, R. (2007). Rice and water. Advances in agronomy 92, 187–237.
7. Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Belmont, CA: Wadsworth. International Group 432, 151–166.
8. Chang, C.-C. & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST) 2, 1–27.
9. Cortes, C. & Vapnik, V. (1995). Support-vector networks Machine learning (pp. 237–297), Vol. 20. Boston, MA: Kluwer Academic Publisher.
10. Ferrant, S., Selles, A., Le Page, M., Herrault, P.-A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., Saqalli, M., Dewandel, B., Caballero, Y., Ahmed, S., Maréchal, J.-C., & Kerr, Y. (2017). Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India. Remote Sensing 9, 1119. https://doi.org/10.3390/rs9111119
11. Foody, G.M. & Mathur, A. (2004). Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment 93, 107–117. https://doi.org/10.1016/j.rse.2004.06.017
12. Foody, G.M. & Mathur, A. (2006). The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM. Remote sensing of environment 103, 179–189.
13. Gao, B. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58, 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
14. Genuer, R. & Poggi, J.-M. (2017). Arbres CART et For\^ets al\’eatoires, Importance et s\’election de variables. arXiv:1610.08203 [math, stat].
15. Gumma, M.K., Thenkabail, P.S., Maunahan, A., Islam, S., & Nelson, A. (2014). Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500m data for the year 2010.ISPRS Journal of Photogrammetry and RemoteSensing 91,98–113. https://doi.org/10.1016/j.isprsjprs.2014.02.007
16. Hong Son, N. & Thai-Nghe, N. (2019). Deep Learning for Rice Quality Classification, in: 2019 International Conference on Advanced Computing and Applications (ACOMP). Presented at the 2019 International Conference on Advanced Computing and Applications (ACOMP), IEEE, NhaTrang, Vietnam, pp.92–96. https://doi.org/10.1109/ACOMP.2019.00021
17. Huang, J., Li, Y., Fu, C., Chen, F., Fu, Q., Dai, A., Shinoda, M., Ma, Z., Guo, W., & Li, Z. (2017). Dryland climate change: Recent progress and challenges. Reviews of Geophysics 55, 719–778.
18. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
19. Jay L. Maclean & David Charles Dawe (2002). Rice Almanac: Source Book for the Most Important Economic Activity on Earth - Google Livres [WWW Document].
20. Jha, J. & Ragha, L. (2013). Intrusion detection system using support vector machine. International Journal of Applied Information Systems (IJAIS) 3, 25–30.
21. Kshirsagar, K.G. & Pandey, S. (1995). Diversity of rice cultivars in a rainfed village in the Orissa state of India. Using diversity: enhancing and maintaining resources on-farm. International Development Research Center (IDRC), Regional Office for South Asia, India. p 55–65.
22. Kuenzer, C. & Knauer, K. (2013). Remote sensing of rice crop areas. International Journal of Remote Sensing 34, 2101–2139. https://doi.org/10.1080/01431161.2012.738946
23. Lacharme, M. (2001). Données morphologiques et cycle de la plante 22
24. Lancashire, P.D., Bleiholder, H., Boom, T.V.D., Langelüddeke, P., Stauss, R., Weber, E., & Witzenberger, A. (1991). A uniform decimal code for growth stages of crops and weeds. Ann Applied Biology 119, 561–601. https://doi.org/10.1111/j.1744-7348.1991.tb04895.x
25. Le Toan, T., Ribbes, F., Li-Fang Wang, Floury, N., Kung-Hau Ding, Jin Au Kong, Fujita, M., & Kurosu, T. (1997). Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Trans. Geosci. Remote Sensing 35, 41–56. https://doi.org/10.1109/36.551933
26. Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random forest, in: International Conference on Information Computing and Applications. Springer, pp. 246–252.
27. Mendez Del Villar, P., Bauer, J.M., Maiga, A., & Ibrahim, L. (2011). Rice crisis, market developments and food security in West Africa. Ministry of Foreign and European Affairs, West Africa, 61.
28. Michel, P. & Sall, M. Dynamique des paysages et aménagement de la vallée alluviale du Sénégal 21.
29. Nguyen, X.T. (2016). Modélisation de l’impact des rizières et de l’irrigation sur le régime hydrologique de la rivière Cong au Vietnam. (PhD Thesis). Université du Québec, Institut national de la recherche scientifique.
30. Parente, R., Rong, K., Geleilate, J.-M.G., & Misati, E. (2019). Adapting and sustaining operations in weak institutional environments: A business ecosystem assessment of a Chinese MNE in Central Africa. Journal of International Business Studies 50, 275–291.
31. Radanielina, T., Ramanantsoanirina, A., Raboin, L.-M., & Ahmadi, N. (2013). Determinants of rice varietal diversity in the region of Vakinankaratra (Madagascar). Cahiers Agricultures 22, 442–449. https://doi.org/10.1684/agr.2013.0648
32. Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J.P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing 67, 93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002
33. Rouse Jr, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium: Volume 1; Technical presentations, section B, SC Freden, EP Mercanti, and MA Becker, Eds., NASA Special Publ. NASA-SP-351-VOL-1-SECT-B, A 20, 309–317.
34. Schiilkop, P.B., Burgest, C., & Vapnik, V. (1995). Extracting support data for a given task, in: Proceedings, First International Conference on Knowledge Discovery & Data Mining. AAAI Press, Menlo Park, CA. pp. 252–257.
35. Singh, V.P., Tuong, T.P., & Kam, S.P. (2000). Characterising rainfed rice environments: an overview of the biophysical aspects. Characterising and understanding rainfed environments. Los Baños, Philippines, IRRI 3–32.
36. Steinberg, D., Golovnya, M., & Polosukhin, I. (2012). Text Mining Using STMTM, CART®, and TreeNet® from Salford Systems: Analysis of 16,000 iPod Auctions on eBay, in: Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications. Academic Press, pp. 413–416.
37. Teluguntla, P., Thenkabail, P.S., Oliphant, A., Xiong, J., Gumma, M.K., Congalton, R.G., Yadav, K., & Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing 144, 325–340.
38. Torbick, N., Chowdhury, D., Salas, W., & Qi, J. (2017). Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2. Remote Sensing 9, 119. https://doi.org/10.3390/rs9020119
39. Touré, M.A. (2018). “Climate variability and ecosystem dynamics in the Senegal River Delta from the 1950 to the 2010.”
40. Tuong, T.P., BAM, B., & Mortimer, M. (2005). More Rice, Less Water—Integrated Approaches for Increasing Water Productivity in Irrigated Rice-Based Systems in Asia—. Plant Production Science 8, 231–241.
41. Vapnik, V.N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks 10, 988–999.
42. Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J.-F., & Ceschia, E. (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment 199, 415–426. https://doi.org/10.1016/j.rse.2017.07.015
43. Wassmann, R., Hien, N.X., Hoanh, C.T., & Tuong, T.P. (2004). Sea level rise affecting the Vietnamese Mekong Delta: water elevation in the flood season and implications for rice production. Climatic change 66, 89–107.
44. Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Salas, W., & Moore, B. (2006). Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment 100,95–113. https://doi.org/10.1016/j.rse.2005.10.004
45. Yang, C.-M., Cheng, C.-H., & Chen, R.K. (2007). Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop science 47, 329–335.
46. Yoshida, S., Forno, D.A., Cock, J.H. (1971). Laboratory manual for physiological studies of rice. Laboratory manual for physiological studies of rice.
47. Zhang, H., Li, Y., Zhu, J.-K. (2018). Developing naturally stress-resistant crops for a sustainable agriculture. Nature plants 4, 989–996.
How to Cite
Mbengue, F., Faye, G., Talla, K., Adama Sarr, M., Ferrari, A., Mbaye, M., Semina Dramé, M., & Sagne, P. (2022). Evaluation Of Machine Learning Classification Methods For Rice Detection Using Earth Observation Data: Case Of Senegal. European Scientific Journal, ESJ, 18(17), 214. https://doi.org/10.19044/esj.2022.v18n17p214
ESJ Natural/Life/Medical Sciences