Dwi Wahyu Triscowati, Bagus Sartono, Anang Kurnia, Dede Dirgahayu, Arie Wahyu Wijayanto


Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.


rice-plant classification; temporal autocorrelation; temporal features engineering; random forest; Landsat-8

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Azar, R., Villa, P., Stroppiana, D., Crema, A., Boschetti, M., & Brivio, P. A. (2016). Assessing In-Season Crop Classification Performance Using Satellite Data: A Test Case In Northern Italy. European Journal of Remote Sensing, 49, 361–380. doi: 10.5721/EuJRS20164920

Belgiu, M. & Dragut, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. doi:10.1016/j.isprsjprs.2016.01.011

Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. Retrieved from

Dirgahayu, D., Noviar, H., & Anwar, S. (2014). Model pertumbuhan tanaman padi di pulau Sumatera menggunakan data EVI MODIS multitemporal. Deteksi Param. Geobiofisik Dan Diseminasi Penginderaan Jauh (7):333–343.Dirgahayu, D., Parsa, I. M., Silvia, Hartini, S., Budoyo, S., Indriawan, K., & Ernawati (2015). Litbang Pemanfaatan Data Penginderaan Jauh Untuk Pemantauan Pertumbuhan Tanaman Padi Di Lahan Sawah: Studi Kasus Pulau Kalimantan [R & D utilization of remote sensing data for monitoring rice plant growth in rice fields: Case study of Kalimantan Island]. Jakarta: Pusfatja LAPAN.

Ghimire, B., Rogan, J., Galiano, V. R., Panday, P., & Neeti, N. (2012). An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience & Remote Sensing, 49(5), 623–643. doi: 10.2747/1548-1603.49.5.623

Gregorutti, B., Michel, B., & Saint-Pierre, P., (2017). Correlation and variable importance in random forests. Statistics and Computing, 27(3), 659–678. doi: 10.1007/s11222-016-9646-1

Guan, X., Huang, C., Liu, G., Meng, X., & Liu, G. (2016). Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sensing, 8(19), 1–25. doi: 10.3390/rs8010019

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (data mining, inference, and prediction). New York: Springer.

Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geosci. Remote Sens. Lett. 14(5):778–782. doi:10.1109/LGRS.2017.2681128.

Millard, K. & Richardson, M. (2015). On the importance of training data sample selection in random forest image classification: A case study in peatland ecosystem mapping. Remote Sensing, 7, 8489–8515.

Mishra, N. B., Crews, K. A., Miller, J. A., & Meyer, T. (2015). Mapping vegetation morphology types in southern Africa savanna using using MODIS time-series metrics: A case study of Central Kalahari, Botswana. Land, 4, 197–215. doi: 10.3390/land4010197

Parsa, I. M., Dirgahayu, D., Manalu, J.,

Carolita, I., & Harsanugraha, K. W. (2017). Uji Model Fase Pertumbuhan Padi Berbasis Citra Modis Multiwaktu Di Pulau Lombok [Test model of multi-time modular image-based rice growthphase in Lombok Island]. Jurnal Penginderaan Jauh 14(1), 51–64.

Qiu, B., Lu, D., Tang, Z., Chen, C., & Zou, F. (2017). Automatic and adaptive paddy rice mapping using Landsat images: Case study in Songnen Plain in Northeast China. Science of The Total Environment. 598:581–592. doi:10.1016/j.scitotenv.2017.03.221.

Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Krishna Gumma, M., Congalton, R. G., … 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(2), 325–340. doi: 10.1016/j.isprsjprs.2018.07.017

Tong, X., Xia, G., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2018). Learning transferable deep models for land-use classification with high-resolution remote sensing images. NASA Astrophysics Data System (Subject Computer Vision and Pattern Recognition) 7, 1–25.

You, J., Li, X., Low, M., Lobell, D., & Ermon, S. (2017). Deep Gaussian process for crop yield prediction based on remote sensing data. 31st AAAI Conference on Artificial Intelligence, 4559–4565. Zhang, C., Zhang, H., & Zhang, L. (2018). An automated paddy rice extent extraction with time stacks of Sentinel data : A case study in Jianghan Plain, Hubei, China. 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) Agustus 2018, 1–6. doi: 10.1109/Agro-Geoinformatics.2018.8476119


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