Monitoring Sugarcane Phenology Using Sentinel-1 SAR and Machine Learning in Central Lampung
DOI:
https://doi.org/10.24114/46hfx180Keywords:
Sugarcane, Random Forest, Support Vector Machine, SentinelAbstract
Sugarcane is a vital commodity in the national sugar industry, requiring accurate growth monitoring to support precision agriculture. In Indonesia, conventional monitoring methods remain limited in spatial and temporal coverage. This study aims to monitor sugarcane phenology using multitemporal Sentinel-1 Synthetic Aperture Radar and to develop a machine learning–based growth phase classification model. The study was conducted in Central Lampung during one complete growing season from November 2023 to December 2024. Time-series analysis of VV and VH backscatter coefficients and the Normalised Polarisation Ratio was applied to capture sugarcane growth dynamics. Growth phase classification models were developed using Random Forest (RF) and Support Vector Machine (SVM) and evaluated using the confusion matrix, overall accuracy, Kappa coefficient, coefficient of determination, and root mean square error. The results indicate that RF consistently outperformed SVM across all model configurations. The best performance was achieved using combined VV–VH polarization, yielding an R² of 0.929, RMSE of 0.295, and classification accuracy of 93%. In contrast, SVM models showed weak predictive performance with negative R² values and classification accuracy below 32%. These findings demonstrate that multitemporal Sentinel-1 SAR data combined with RF provide an effective approach for spatial and temporal monitoring of sugarcane phenology
References
Ahmed, M., Ahmad, S., Abbas, G., Hussain, S., & Hoogenboom, G. (2024). Sugarcane System. In M. Ahmed, S. Ahmad, G. Abbas, S. Hussain, & G. Hoogenboom (Eds), Cropping Systems Modeling Under Changing Climate (pp. 235–269). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0331-9_9
Amaliya, A., Supriono, A., Yanuarti, R., Aji, J. M. M., Ridjal, J. A., Soejono, D., & Ibanah, I. (2025). Proyeksi Produksi dan Konsumsi Gula Pasir di Indonesia 2022–2025 serta Implikasinya terhadap Target Swasembada Nasional. 5(1).
Baccari, N., Hamza, M. H., Slama, T., Sebei, A., Ouerghi, S., Elsheikh, R., Rebai, N., Hasanean, H., Almazroui, M., & Elhag, M. (2025). Assessment of Machine Learning Techniques in Mapping Land Use/Land Cover Changes in a Semi-Arid Environment. Earth Systems and Environment, 9(2), 519–539. https://doi.org/10.1007/s41748-024-00562-2
Baraha, S., & Sahoo, A. K. (2022). Restoration of speckle noise corrupted SAR images using regularization by denoising. Journal of Visual Communication and Image Representation, 86, 103546. https://doi.org/10.1016/j.jvcir.2022.103546
Cera, F. P., Danoedoro, P., Wicaksono, P., & Yasir, M. (2023). Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado. Jurnal Geografi, 15(2), 135–144. https://doi.org/10.24114/jg.v15i2.30795
Chauhan, V. K., Dahiya, K., & Sharma, A. (2019). Problem formulations and solvers in linear SVM: A review. Artificial Intelligence Review, 52(2), 803–855. https://doi.org/10.1007/s10462-018-9614-6
Den Besten, N., Steele Dunne, S., Mahmud, A., Jackson, D., Aouizerats, B., De Jeu, R., Burger, R., Houborg, R., McGlinchey, M., & Van Der Zaag, P. (2023). Understanding Sentinel-1 backscatter response to sugarcane yield variability and waterlogging. Remote Sensing of Environment, 290, 113555. https://doi.org/10.1016/j.rse.2023.113555
El Hajj, M., Bégué, A., Guillaume, S., & Martiné, J.-F. (2009). Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices—The case of sugarcane harvest on Reunion Island. Remote Sensing of Environment, 113(10), 2052–2061. https://doi.org/10.1016/j.rse.2009.04.009
Hakim, M. A., Kamal, M., & Arjasakusuma, S. (2022). Mapping Mangrove Surface Carbon Stocks Using Multisensor Imagery in Clungup Mangrove Conservation (Cmc) Malang Regency. Jurnal Geografi, 14(2), 192–201. https://doi.org/10.24114/jg.v14i2.33575
Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., & Hawkesford, M. (2016). High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. Remote Sensing, 8(12), 1031. https://doi.org/10.3390/rs8121031
Hong, Y., Que, X., Wang, Z., Ma, X., Wang, H., Salati, S., & Liu, J. (2024). Mangrove extraction from super-resolution images generated by deep learning models. Ecological Indicators, 159, 111714. https://doi.org/10.1016/j.ecolind.2024.111714
Hong, Y., Xie, T., Luo, L., Wang, M., Li, D., Zhang, Q., & Xu, T. (2025). Area extraction and growth monitoring of sugarcane from multi-source remote sensing images under a polarimetric SAR data compensation based on buildings. Geo-Spatial Information Science, 28(3), 831–848. https://doi.org/10.1080/10095020.2024.2381607
Jiang, H., Li, D., Jing, W., Xu, J., Huang, J., Yang, J., & Chen, S. (2019). Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China. Remote Sensing, 11(7), 861. https://doi.org/10.3390/rs11070861
Koch, E., Bruns, E., Chmielewski, F.-M., Defila, C., Lipa, W., & Menzel, A. (2007). Guidelines for plant phenological observations. World Climate Data and Monitoring Programme, 1484, 1–41.
Li, H., Wang, Z., Sun, L., Zhao, L., Zhao, Y., Li, X., Han, Y., Liang, S., & Chen, J. (2024). Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data. Remote Sensing, 16(15), 2785. https://doi.org/10.3390/rs16152785
Mizan, R. A., Widayani, P., & Farda, N. M. (2021). Assessment And Comparison of Machine Learning Algorithm Capability in Spatial Modeling Of Dengue Fever Vulnerability Based on Landsat Image 8 Oli/Tirs. Jurnal Geografi, 13(2), 211–224. https://doi.org/10.24114/jg.v13i2.21019
Msomba, B. H., Ndaki, P. M., & Joseph, C. O. (2024). Sugarcane sustainability in a changing climate: A systematic review on pests, diseases, and adaptive strategies. Frontiers in Agronomy, 6, 1423233. https://doi.org/10.3389/fagro.2024.1423233
Nadzir, Z. A., Simarmata, N., & Aliffia, A. (2020). Pengembangan Algoritma Identifikasi Sawah Padi Berdasarkan Spektra Fase Padi (Studi Kasus: Lampung Selatan) (Paddy Field Identification Algorithm Development Using Spectral Value of Paddy Field (Case Study: South Lampung)). Jurnal Sains Informasi Geografis, 3(1), 23. https://doi.org/10.31314/jsig.v3i1.537
Nihar, A., Patel, N. R., Pokhariyal, S., & Danodia, A. (2022). Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods. Journal of the Indian Society of Remote Sensing, 50(2), 217–225. https://doi.org/10.1007/s12524-021-01444-0
Nurhadi, M., Widayani, P., & Wibowo, S. B. (2025). Analisis Jenis Budidaya Tebu Menggunakan Algoritma Random Forest (Studi Kasus Kecamatan Tajinan, Kabupaten Malang). Jurnal Tanah dan Sumberdaya Lahan, 12(2), 275–289. https://doi.org/10.21776/ub.jtsl.2025.012.2.6
Phan, H., Le Toan, T., & Bouvet, A. (2021). Understanding Dense Time Series of Sentinel-1 Backscatter from Rice Fields: Case Study in a Province of the Mekong Delta, Vietnam. Remote Sensing, 13(5), 921. https://doi.org/10.3390/rs13050921
Pott, L. P., Amado, T. J. C., Schwalbert, R. A., Corassa, G. M., & Ciampitti, I. A. (2021). Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil. ISPRS Journal of Photogrammetry and Remote Sensing, 176, 196–210. https://doi.org/10.1016/j.isprsjprs.2021.04.015
Ratanopad Suwanlee, S., Keawsomsee, S., Izquierdo-Verdiguier, E., Som-Ard, J., Moreno-Martinez, A., Veerachit, V., Polpinij, J., & Rattanasuteerakul, K. (2025). Mapping sugarcane plantations in Northeast Thailand using multi-temporal data from multi-sensors and machine-learning algorithms. Big Earth Data, 9(2), 187–216. https://doi.org/10.1080/20964471.2025.2463730
Simarmata, N., Nadzir, Z. A., & Agustina, L. K. (2022). Application Of Spot6/7 Satellite Imagery For Rice Field Mapping Based On Transformative Vegetation Indices. Jurnal Geografi, 14(1), 69. https://doi.org/10.24114/jg.v14i1.29036
Simatupang, A. U., Murti, S. H., & Purwanto, T. H. (2022). The Capability of Sentinel 1 (SAR) For Flood Mapping: A Case Study in Serang Watershed, Kulonprogo Regency. Jurnal Geografi, 14(2), 157–167. https://doi.org/10.24114/jg.v14i2.32213
Singh, R., Patel, N. R., & Danodia, A. (2022). Deriving Phenological Metrics from Landsat-OLI for Sugarcane Crop Type Mapping: A Case Study in North India. Journal of the Indian Society of Remote Sensing, 50(6), 1021–1030. https://doi.org/10.1007/s12524-022-01515-w
Som-ard, J., Atzberger, C., Izquierdo-Verdiguier, E., Vuolo, F., & Immitzer, M. (2021). Remote Sensing Applications in Sugarcane Cultivation: A Review. Remote Sensing, 13(20), 4040. https://doi.org/10.3390/rs13204040
Sovann, C., Olin, S., Mansourian, A., Sakhoeun, S., Prey, S., Kok, S., & Tagesson, T. (2025). Importance of Spectral Information, Seasonality, and Topography on Land Cover Classification of Tropical Land Cover Mapping. Remote Sensing, 17(9), 1551. https://doi.org/10.3390/rs17091551
Sreedhar, R., Varshney, A., & Dhanya, M. (2022). Sugarcane crop classification using time series analysis of optical and SAR sentinel images: A deep learning approach. Remote Sensing Letters, 13(8), 812–821. https://doi.org/10.1080/2150704X.2022.2088254
Sridhara, S., Soumya B. R., & Kashyap, G. R. (2024). Multistage sugarcane yield prediction using machine learning algorithms. Journal of Agrometeorology, 26(1), 37–44. https://doi.org/10.54386/jam.v26i1.2411
Sulistiyanto, T. Q., Sinaga, S. M., & Suryanda, A. (2021). Pemahaman dan Perspektif Mahasiswa Mengenai Manfaat Air Tebu (Saccharum officinarum) dalam Prospek Kesehatan. Pro-Life, 8(3), 199–204. https://doi.org/10.33541/pro-life.v8i3.3225
Suspidayanti, L., & Rokhmana, C. A. (2021). Identifikasi Fase Pertumbuhan Padi Menggunakan Citra Sar (Synthetic Aperture Radar) Sentinel-1. Elipsoida : Jurnal Geodesi dan Geomatika, 4(01), 9–15. https://doi.org/10.14710/elipsoida.2021.10729
Virnodkar, S., Pachghare, V., Patil, V. C., & Jha, S. (2021). Performance Evaluation of RF and SVM for Sugarcane Classification Using Sentinel-2 NDVI Time-Series (pp. 163–174). https://doi.org/10.1007/978-981-15-6353-9_15
Wang, S., Di Tommaso, S., Faulkner, J., Friedel, T., Kennepohl, A., Strey, R., & Lobell, D. B. (2020). Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning. Remote Sensing, 12(18), 2957. https://doi.org/10.3390/rs12182957
Waters, E. K., Chen, C. C.-M., & Rahimi Azghadi, M. (2025). Sugarcane health monitoring with satellite spectroscopy and machine learning: A review. Computers and Electronics in Agriculture, 229, 109686. https://doi.org/10.1016/j.compag.2024.109686
Yeasin, M., Haldar, D., Kumar, S., Paul, R. K., & Ghosh, S. (2022). Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data. Remote Sensing, 14(14), 3249. https://doi.org/10.3390/rs14143249
Zheng, Y., Dos Santos Luciano, A. C., Dong, J., & Yuan, W. (2022). High-resolution map of sugarcane cultivation in Brazil using a phenology-based method. Earth System Science Data, 14(4), 2065–2080. https://doi.org/10.5194/essd-14-2065-2022
Zheng, Y., Li, Z., Pan, B., Lin, S., Dong, J., Li, X., & Yuan, W. (2022). Development of a Phenology-Based Method for Identifying Sugarcane Plantation Areas in China Using High-Resolution Satellite Datasets. Remote Sensing, 14(5), 1274. https://doi.org/10.3390/rs14051274
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