Land Cover Classification Using LAPAN-A3 and Sentinel-2 Imagery in Google Earth Engine: A Machine Learning-Based Comparative Analysis
Article Sidebar
Keywords:
-
LAPAN-A3, Sentinel-2, Land Cover Classification, Machine Learning, Google Earth Engine (GEE)
Abstract
Open-source satellite imagery such as Sentinel-2 has been widely proven reliable for various geospatial applications. However, achieving geospatial independence remains crucial for any country to reduce reliance on foreign data sources and strengthen national sovereignty in Earth observation capabilities. In this context, Indonesia initiated a microsatellite development program in 2007, which has now reached its third generation with LAPAN-A3. Despite these efforts, LAPAN-A3 is still considered an experimental satellite, and further evaluation is required before it can be fully adopted for operational applications. This study evaluates the performance of LAPAN-A3 imagery for land cover mapping using machine learning approaches and compares its performance with the well-established global dataset Sentinel-2. Two widely used classifiers, Random Forest (RF) and Support Vector Machine (SVM), were implemented within the Google Earth Engine (GEE) platform and tested using different combinations of spectral features. The results show consistent improvements in classification performance when additional spectral features are incorporated for both LAPAN-A3 and Sentinel-2 datasets. In all feature configurations, RF outperforms SVM, achieving higher Overall Accuracy (OA) and Kappa coefficients. Although Sentinel-2 generally yields slightly better results, LAPAN-A3 demonstrates promising performance despite its experimental nature. These findings highlight the potential of LAPAN-A3 as a national remote sensing asset that can contribute to Indonesia’s long-term goal of achieving geospatial independence and strengthening domestic Earth observation capabilities.
References
Adugna, T., W. Xu, and J. Fan (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sensing, 14(3); 574.
Amini, S., M. Saber, H. Rabiei-Dastjerdi, and S. Homayouni (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sensing, 14(11); 2654.
Avci, C., M. Budak, N. Yagmur, and F. B. Balcik (2023). Comparison Between Random Forest and Support Vector Machine Algorithms for LULC Classification. International Journal of Engineering and Geosciences, 8(1); 1–10.
Belgiu, M. and L. Drăguţ (2016). Random Forest in Remote Sensing: A Review of Applications and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114; 24–31.
Chen, T.-H. K., B. Pandey, and K. C. Seto (2023). Detecting Subpixel Human Settlements in Mountains Using Deep Learning: A Case of the Hindu Kush Himalaya 1990–2020. Remote Sensing of Environment, 294; 113625.
Corbane, C., V. Syrris, F. Sabo, P. Politis, M. Melchiorri, M. Pesaresi, and T. Kemper (2021). Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery. Neural Computing and Applications, 33; 6697–6720.
Erdanaev, E., M. Kappas, and D. Wyss (2022). The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan. International Journal of Geoinformatics, 18(2); 37–53.
Eskandari, S., M. Reza Jaafari, P. Oliva, O. Ghorbanzadeh, and T. Blaschke (2020). Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data. Remote Sensing, 12(12); 1912.
Hakim, P. R., A. H. Syafrudin, S. Salaswati, S. Utama, and W. Hasbi (2019). Development of Systematic Image Preprocessing of LAPAN-A3/IPB Multispectral Images. International Journal of Advanced Studies in Computer Science in Engineering, 7(10); 9–18.
Herawan, A., A. Julzarika, P. R. Hakim, and E. A. Anggari (2021). Object-Based Land Cover Classification on LAPAN-A3 Satellite Imagery Using Tree Algorithm (Case Study: Rote Island). International Journal on Advanced Science, Engineering and Information Technology, 11(6); 2254–2260.
Julzarika, A. (2017). Utilization of LAPAN Satellite (TUBSAT, A2, and A3) in Supporting Indonesia’s Potential as Maritime Center of the World. In IOP Conference Series: Earth and Environmental Science, volume 54. IOP Publishing, page 012097.
Khamsah, N. M. N., S. Utama, R. H. Surayuda, and P. R. Hakim (2019). The Development of LAPAN-A3 Satellite Off-Nadir Imaging Mission. In 2019 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). IEEE, pages 1–6.
Kurihara, J., T. Kuwahara, S. Fujita, Y. Sato, K. Hanyu, M. Sakai, and W.-H. Ip (2020). A High Spatial Resolution Multispectral Sensor on the RISESAT Microsatellite. Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 18(5); 186–191.
Kurihara, J., Y. Takahashi, Y. Sakamoto, T. Kuwahara, and K. Yoshida (2018). HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing. Sensors, 18(2); 1–11.
Mohammadpour, P., D. X. Viegas, and C. Viegas (2022). Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature-A Case Study for Lousã Region, Portugal. Remote Sensing, 14(18); 4585.
Mountrakis, G., J. Im, and C. Ogole (2011). Support Vector Machines in Remote Sensing: A Review. ISPRS Journal of Photogrammetry and Remote Sensing, 66; 247–259.
Nugroho, J. T., Zylshal, and D. Kushardono (2018). LAPAN-A3 Satellite Data Analysis for Land Cover Classification (Case Study: Toba Lake Area, North Sumatra). International Journal of Remote Sensing and Earth Science, 15(1); 71–80.
Ottosen, T.-B., G. Petch, M. Hanson, and C. A. Skjøth (2020). Tree Cover Mapping Based on Sentinel-2 Images Demonstrate High Thematic Accuracy in Europe. International Journal of Applied Earth Observation and Geoinformation, 84; 101947.
Phiri, D., M. Simwanda, S. Salekin, V. R. Nyirenda, Y. Murayama, and M. Ranagalage (2020). Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12(14); 2291.
Plakman, V., T. Janssen, N. Brouwer, and S. Veraverbeke (2020). Mapping Species at an Individual-Tree Scale in a Temperate Forest Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification. Remote Sensing, 12(22); 1–25.
Resti, Y., C. Irsan, J. F. Latif, I. Yani, and N. R. Dewi (2023). A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests. Science and Technology Indonesia, 8(2); 288–297.
Setiawan, Y., L. B. Prasetyo, H. Pawitan, L. Liyantono, Syartinilia, A. K. Wijayanto, and P. R. Hakim (2018). Pemanfaatan Fusi Data Satelit LAPAN-A3/IPB dan Landsat 8 untuk Monitoring Lahan Sawah. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), 8(1); 67–76. (in Indonesia).
Sheykhmousa, M., M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13; 6308–6325.
Shofiana, D. A., M. Caniadi, R. Sholehurrohman, and Aristoteles (2025). Decision Tree Algorithms in Water Quality Classification: A Comparative Study of Random Forest, XGBoost, and C5.0. Science and Technology Indonesia, 10(4); 999–1011.
Thanh Noi, P. and M. Kappas (2017). Comparison of Random Forest, K-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18(1); 18.
Zhang, C., Y. Liu, and N. Tie (2023). Forest Land Resource Information Acquisition with Sentinel-2 Image Utilizing Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Trees and Multi-Layer Perceptron. Forests, 14(2); 254.
Zylshal, Zylshal, R. Wirawan, and D. Kushardono (2018). Assessing the Potential of LAPAN-A3 Data for Landuse/Landcover Mapping. Indonesian Journal of Geography, 50(2); 184–196.
Zylshal, Z., N. Sari, J. Nugroho, and D. Kushardono (2017). Comparison of Spectral Characteristic Between LAPAN-A3 and Sentinel-2A. In IOP Conference Series: Earth and Environmental Science, volume 98. IOP Publishing, page 012051.
Amini, S., M. Saber, H. Rabiei-Dastjerdi, and S. Homayouni (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sensing, 14(11); 2654.
Avci, C., M. Budak, N. Yagmur, and F. B. Balcik (2023). Comparison Between Random Forest and Support Vector Machine Algorithms for LULC Classification. International Journal of Engineering and Geosciences, 8(1); 1–10.
Belgiu, M. and L. Drăguţ (2016). Random Forest in Remote Sensing: A Review of Applications and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114; 24–31.
Chen, T.-H. K., B. Pandey, and K. C. Seto (2023). Detecting Subpixel Human Settlements in Mountains Using Deep Learning: A Case of the Hindu Kush Himalaya 1990–2020. Remote Sensing of Environment, 294; 113625.
Corbane, C., V. Syrris, F. Sabo, P. Politis, M. Melchiorri, M. Pesaresi, and T. Kemper (2021). Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery. Neural Computing and Applications, 33; 6697–6720.
Erdanaev, E., M. Kappas, and D. Wyss (2022). The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan. International Journal of Geoinformatics, 18(2); 37–53.
Eskandari, S., M. Reza Jaafari, P. Oliva, O. Ghorbanzadeh, and T. Blaschke (2020). Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data. Remote Sensing, 12(12); 1912.
Hakim, P. R., A. H. Syafrudin, S. Salaswati, S. Utama, and W. Hasbi (2019). Development of Systematic Image Preprocessing of LAPAN-A3/IPB Multispectral Images. International Journal of Advanced Studies in Computer Science in Engineering, 7(10); 9–18.
Herawan, A., A. Julzarika, P. R. Hakim, and E. A. Anggari (2021). Object-Based Land Cover Classification on LAPAN-A3 Satellite Imagery Using Tree Algorithm (Case Study: Rote Island). International Journal on Advanced Science, Engineering and Information Technology, 11(6); 2254–2260.
Julzarika, A. (2017). Utilization of LAPAN Satellite (TUBSAT, A2, and A3) in Supporting Indonesia’s Potential as Maritime Center of the World. In IOP Conference Series: Earth and Environmental Science, volume 54. IOP Publishing, page 012097.
Khamsah, N. M. N., S. Utama, R. H. Surayuda, and P. R. Hakim (2019). The Development of LAPAN-A3 Satellite Off-Nadir Imaging Mission. In 2019 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). IEEE, pages 1–6.
Kurihara, J., T. Kuwahara, S. Fujita, Y. Sato, K. Hanyu, M. Sakai, and W.-H. Ip (2020). A High Spatial Resolution Multispectral Sensor on the RISESAT Microsatellite. Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 18(5); 186–191.
Kurihara, J., Y. Takahashi, Y. Sakamoto, T. Kuwahara, and K. Yoshida (2018). HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing. Sensors, 18(2); 1–11.
Mohammadpour, P., D. X. Viegas, and C. Viegas (2022). Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature-A Case Study for Lousã Region, Portugal. Remote Sensing, 14(18); 4585.
Mountrakis, G., J. Im, and C. Ogole (2011). Support Vector Machines in Remote Sensing: A Review. ISPRS Journal of Photogrammetry and Remote Sensing, 66; 247–259.
Nugroho, J. T., Zylshal, and D. Kushardono (2018). LAPAN-A3 Satellite Data Analysis for Land Cover Classification (Case Study: Toba Lake Area, North Sumatra). International Journal of Remote Sensing and Earth Science, 15(1); 71–80.
Ottosen, T.-B., G. Petch, M. Hanson, and C. A. Skjøth (2020). Tree Cover Mapping Based on Sentinel-2 Images Demonstrate High Thematic Accuracy in Europe. International Journal of Applied Earth Observation and Geoinformation, 84; 101947.
Phiri, D., M. Simwanda, S. Salekin, V. R. Nyirenda, Y. Murayama, and M. Ranagalage (2020). Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12(14); 2291.
Plakman, V., T. Janssen, N. Brouwer, and S. Veraverbeke (2020). Mapping Species at an Individual-Tree Scale in a Temperate Forest Using Sentinel-2 Images, Airborne Laser Scanning Data, and Random Forest Classification. Remote Sensing, 12(22); 1–25.
Resti, Y., C. Irsan, J. F. Latif, I. Yani, and N. R. Dewi (2023). A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests. Science and Technology Indonesia, 8(2); 288–297.
Setiawan, Y., L. B. Prasetyo, H. Pawitan, L. Liyantono, Syartinilia, A. K. Wijayanto, and P. R. Hakim (2018). Pemanfaatan Fusi Data Satelit LAPAN-A3/IPB dan Landsat 8 untuk Monitoring Lahan Sawah. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), 8(1); 67–76. (in Indonesia).
Sheykhmousa, M., M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13; 6308–6325.
Shofiana, D. A., M. Caniadi, R. Sholehurrohman, and Aristoteles (2025). Decision Tree Algorithms in Water Quality Classification: A Comparative Study of Random Forest, XGBoost, and C5.0. Science and Technology Indonesia, 10(4); 999–1011.
Thanh Noi, P. and M. Kappas (2017). Comparison of Random Forest, K-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18(1); 18.
Zhang, C., Y. Liu, and N. Tie (2023). Forest Land Resource Information Acquisition with Sentinel-2 Image Utilizing Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Trees and Multi-Layer Perceptron. Forests, 14(2); 254.
Zylshal, Zylshal, R. Wirawan, and D. Kushardono (2018). Assessing the Potential of LAPAN-A3 Data for Landuse/Landcover Mapping. Indonesian Journal of Geography, 50(2); 184–196.
Zylshal, Z., N. Sari, J. Nugroho, and D. Kushardono (2017). Comparison of Spectral Characteristic Between LAPAN-A3 and Sentinel-2A. In IOP Conference Series: Earth and Environmental Science, volume 98. IOP Publishing, page 012051.
Authors
Susetyo, D. B., Dewayany Sutrisno, Atriyon Julzarika, Herawan, A., Patria Rachman Hakim, & Ahmad Fauzi. (2026). Land Cover Classification Using LAPAN-A3 and Sentinel-2 Imagery in Google Earth Engine: A Machine Learning-Based Comparative Analysis. Science and Technology Indonesia, 11(3), 1164–1175. https://doi.org/10.26554/sti.2026.11.3.1164-1175
This work is licensed under a Creative Commons Attribution 4.0 International License.