Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries

Yusri Khoirurrizqi(1*), Rohmad Sasongko(2), Nur Laila Eka Utami(3), Amanda Irbah(4), Sanjiwana Arjasakusuma(5)

(1) Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta 55281
(2) Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta 55281
(3) Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta 55281
(4) Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta 55281
(5) Faculty of Geography, Universitas Gadjah Mada, Sekip Utara, Bulaksumur, Yogyakarta 55281
(*) Corresponding Author


The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security.



Rice field;Land Conversion;Remote sensing;Multi-Sensor;Machine Learning

Full Text:



Arjasakusuma, Sanjiwana, Sandiaga Swahyu Kusuma, Raihan Rafif, Siti Saringatin, and Pramaditya Wicaksono. (2020.) Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information, 9(663), 1–17. doi: 10.3390/ijgi9110663.

Azedou, Ali, Aouatif Amine, Isaya Kisekka, Said Lahssini, Youness Bouziani, and Said Moukrim. (2023). Enhancing Land Cover/Land Use (LCLU) Classification through a Comparative Analysis of Hyperparameters Optimisation Approaches for Deep Neural Network (DNN). Ecological Informatics 78 (4), 16–25. doi: 10.1016/j.ecoinf.2023. 102333.

Bahagia, Bahagia, Bambang Hudayana, Rimun Wibowo, and Zuzy Anna. (2020). Local Wisdom to Overcome Covid-19 Pandemic of Urug and Cipatat Kolot Societies in Bogor, West Java, Indonesia. Forum Geografi, 34 (2), 146–60. doi: 10.23917/forgeo.v34i2.12366.

Becker, Alexander, Stefania Russo, Stefano Puliti, Nico Lang, Konrad Schindler, and Jan Dirk Wegner. (2023). Coun-try-Wide Retrieval of Forest Structure from Optical and SAR Satellite Imagery with Deep Ensembles. ISPRS Journal of Photogrammetry and Remote Sensing, 195(11), 269–86. doi: 10.1016/j.isprsjprs.2022.11.011.

Blickensdörfer, Lukas, Marcel Schwieder, Dirk Pflugmacher, Claas Nendel, Stefan Erasmi, and Patrick Hostert. (2022). Mapping of Crop Types and Crop Sequences with Combined Time Series of Sentinel-1, Sentinel-2 and Landsat 8 Data for Germany. Remote Sensing of Environment, 269(2022), 112831. doi: 10.1016/j.rse.2021.112831.

Brown, Dana R. N., Christopher D. Arp, Todd J. Brinkman, Barbara A. Cellarius, Melanie Engram, Mark E. Miller, and Katie V. Spellman. (2023). Long-Term Change and Geospatial Patterns of River Ice Cover and Navigability in Southcentral Alaska Detected with Remote Sensing. Arctic, Antarctic and Alpine Research, 55(1), 99775–7340. doi: 10.1080/15230430.2023.2241279.

Chen, Bohan, Kevin Miller, Andrea L. Bertozzi, and Jon Schwenk. (2023). Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs. Communications on Applied Mathematics and Computation, 1(1), 11–15. doi: 10.1007/s42967-023-00284-8.

Fu, Bolin, Yiyin Liang, Zhinan Lao, Xidong Sun, Sunzhe Li, Hongchang He, Weiwei Sun, and Donglin Fan. (2023). Quantifying Scattering Characteristics of Mangrove Species from Optuna-Based Optimal Machine Learning Classification Using Multi-Scale Feature Selection and SAR Image Time Series. International Journal of Applied Earth Observation and Geoinformation, 122(4), 1569–8432. doi: 10.1016/j.jag.2023.103446.

Giofandi, Eggy Arya. (2020). Persebaran Fenomena Suhu Tinggi Melalui Kerapatan Vegetasi Dan Pertumbuhan Bangunan Serta Distribusi Suhu Permukaan. Jurnal Geografi : Media Informasi Pengembangan Dan Profesi Kegeografian, 17(2), 56–62. doi: 10.15294/jg.v17i2.24486.

Gupta, Anishi, and Sambhav Kumar Jain. (2022). Conversion from Multi-Spectral Data into SAR Data with Deep Con-volution Neural Architecture Using Generative Adversarial Network. Procedia Computer Science, 218(2023), 1760–67. doi: 10.1016/j.procs.2023.01.154.

Hadibasyir, Hamim Zaky, Seftiawan Samsu Rijal, and Dewi Ratna Sari. (2020). Comparison of Land Surface Tempera-ture During and Before the Emergence of Covid-19 Using Modis Imagery in Wuhan City, China. Forum Geo-grafi, 34(1), 1–15. doi: 10.23917/forgeo.v34i1.10862.

He, Xiaoning, Shuangcheng Zhang, Bowei Xue, Tong Zhao, and Tong Wu. (2023). Cross-Modal Change Detection Flood Extraction Based on Convolutional Neural Network. International Journal of Applied Earth Observation and Geoinformation, 117(1), 1569–8432. doi: 10.1016/j.jag.2023.103197.

Hidayati, Iswari Nur, R. Suharyadi, and Projo Danoedoro. (2018). Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index. Forum Geografi 32(1), 96–108. doi: 10.23917/forgeo.v32i1.5907.

Hossain, Md Sazzad, Md Asif Haider Khan, Tomiwa Victor Oluwajuwon, Jayanta Biswas, S. M. Rubaiot Abdullah, Md Seikh Sadiul Islam Tanvir, Sirajum Munira, and Md Naif Ahmed Chowdhury. (2023). Spatiotemporal Change Detection of Land Use Land Cover (LULC) in Fashiakhali Wildlife Sanctuary (FKWS) Impact Area, Bangla-desh, Employing Multispectral Images and GIS. Modelling Earth Systems and Environment, 9(3), 151–73. doi: 10.1007/s40808-022-01653-7.

Jia, Kun, Shunlin Liang, Xiangqin Wei, Yunjun Yao, Yingru Su, Bo Jiang, and Xiaoxia Wang. (2014). Land Cover Clas-sification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data. Remote Sensing 6(11), 11518–32. doi: 10.3390/rs61111518.

Kilbride, John Burns, Ate Poortinga, Biplov Bhandari, Nyein Soe Thwal, Nguyen Hanh Quyen, Jeff Silverman, Karis Tenneson, David Bell, Matthew Gregory, Robert Kennedy, and David Saah. (2023). A Near Real-Time Map-ping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine. Remote Sensing, 15(5223), 1–20.

Mahesh Batta. (2020). Machine Learning Algorithms - A Review. International Journal of Science and Research (IJSR) 9(1), 381–86. doi: 10.21275/ART20203995.

Maxwell, Aaron E., Timothy A. Warner, and Fang Fang. (2018). Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review. International Journal of Remote Sensing, 39(9), 2784–2817. doi: 10.1080/01431161.2018.1433343.

Movchan, Dmytro, Andrii Bilous, Lesia Yelistratova, Alexander Apostolov, and Artur Hodorovsky. (2023). Application of Various Approaches of Multispectral and Radar Data Fusion for Modelling of Aboveground Forest Biomass. Folia Forestalia Polonica, Series A, 65(2), 55–67. doi: 10.2478/ffp-2023-0006.

Onojeghuo, Alex Okiemute, Yuxin Miao, and George Alan Blackburn. (2023). Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery. Remote Sensing 15(6), 1–20. doi: 10.3390/rs15061517.

Parelius, Eleonora Jonasova. (2023). A Review of Deep-Learning Methods for Change Detection in Multispectral Re-mote Sensing Images. Remote Sensing 15(8), 65–70. doi: 10.3390/rs15082092.

Pham, Tien Dat, Nam Thang Ha, Neil Saintilan, Andrew Skidmore, Duong Cao Phan, Nga Nhu Le, Hung Luu Viet, Wataru Takeuchi, and Daniel A. Friess. (2023). Advances in Earth Observation and Machine Learning for Quantifying Blue Carbon. Earth-Science Reviews 243(5), 104501. doi: 10.1016/j.earscirev.2023.104501.

Rafif, Raihan, Sandiaga Swahyu Kusuma, Siti Saringatin, Giara Iman Nanda, Pramaditya Wicaksono, and Sanjiwana Arjasakusuma. (2021). Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Mul-ti-Temporal Planetscope Data. Land 10(12), 1–18. doi: 10.3390/land10121384.

Ratnawati, Luthfiana, and Dwi Ratna Sulistyaningrum. (2020). Penerapan Random Forest Untuk Mengukur Tingkat Keparahan Penyakit Pada Daun Apel. Jurnal Sains Dan Seni ITS, 8(2), 2337–3520. doi: 10.12962/ j23373520.v8i2.48517.

Shaik, Riyaaz Uddien, Shoba Periasamy, and Weiping Zeng. (2023). Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications. Remote Sensing, 15(5), 1–14. doi: 10.3390/rs15051378.

Stathopoulos, N., D. Rozos, and E. Vasileiou. 2023. Water Resources Management in Sperchios River Basin, Using SWOT Analysis. Journal of the Civil Engineering Forum, 9(3), 315–28. doi: 10.22146/jcef.7652.

Tong, Zhonggui, Yuxia Li, Jinglin Zhang, Lei He, and Yushu Gong. (2023). MSFANet: Multiscale Fusion Attention Network for Road Segmentation of Multispectral Remote Sensing Data. Remote Sensing, 15(8), 1–23. doi: 10.3390/rs15081978.

Tripathi, Akshar, Kapil Malik, Arjuman Rafiq Reshi, Md Moniruzzaman, and Reet Kamal Tiwari. (2023). Multi-Temporal SAR Interferometry (MTInSAR)-Based Study of Surface Subsidence and Its Impact on Krishna Go-davari (KG) Basin in India: A Support Vector Approach. Environmental Monitoring and Assessment, 195(11), 1–17. doi: 10.1007/s10661-023-11896-1.

Triscowati, Dwi Wahyu, Bagus Sartono, Anang Kurnia, Dede Dirgahayu, and Arie Wahyu Wijayanto. (2020). Classifi-cation of Rice-Plant Growth Phase Using Supervised Random Forest Method Based on Landsat-8 Multitem-poral Data. International Journal of Remote Sensing and Earth Sciences (IJReSES), 16(2), 187–96. doi: 10.30536/j.ijreses.2019.v16.a3217.

Tzepkenlis, Anastasios, Konstantinos Marthoglou, and Nikos Grammalidis. (2023). Efficient Deep Semantic Segmenta-tion for Land Cover Classification Using Sentinel Imagery. Remote Sensing, 15(8), 90–95. doi: 10.3390/rs15082027.

Vinet, Luc, and Alexei Zhedanov. (2020). A ‘missing’ Family of Classical Orthogonal Polynomials. Jurnal Kesehatan Komunitas Indonesia, 44(8), 1–12. doi: 10.1088/1751-8113/44/8/085201.

Wang, Ziyu, Shisong Cao, Mingyi Du, Wen Song, Jinling Quan, and Yang Lv. (2023). Local Climate Zone Classifica-tion by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data. Remote Sensing, 15(10), 1–10. doi: 10.3390/rs15102599.

Wu, Nitu, Luís Guilherme Teixeira Crusiol, Guixiang Liu, Deji Wuyun, and Guodong Han. (2023). Comparing Machine Learning Algorithms for Pixel/Object-Based Classifications of Semi-Arid Grassland in Northern China Using Multisource Medium Resolution Imageries. Remote Sensing, 15(3), 1–22. doi: 10.3390/rs15030750.

Xu, Yiming, Youquan Tan, Amr Abd-Elrahman, Tengfei Fan, and Qingpu Wang. (2023). Incorporation of Fused Re-mote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China. Remote Sensing, 15(8), 1–16. doi: 10.3390/rs15082017.

Yusrina, Azizah, Fiky Yosef Suratman, and Dharu Arseno. (2019). Pembentukan Citra Synthetic Aperture Radar (SAR) Menggunakan Metode Backprojection. E-Proceeding of Engineering, 6(2), 4268–74.

Zollini, Sara, Donatella Dominici, Maria Alicandro, María Cuevas-González, Eduard Angelats, Francesca Ribas, and Gonzalo Simarro. (2023). New Methodology for Shoreline Extraction Using Optical and Radar (SAR) Satellite Imagery. Journal of Marine Science and Engineering 11(3), 1–25. doi: 10.3390/jmse11030627.

Article Metrics

Abstract view(s): 394 time(s)
PDF: 182 time(s) HTML: 44 time(s)


  • There are currently no refbacks.