Publication: Machine learning-based and synthetic aperture radar time-series data for rice classification over Sentinel-1 imagery
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Issued Date
2025-04-01
Resource Type
ISSN
20893191
eISSN
23029285
Scopus ID
2-s2.0-85216615010
Journal Title
Bulletin of Electrical Engineering and Informatics
Volume
14
Issue
2
Start Page
1138
End Page
1150
Rights Holder(s)
SCOPUS
Bibliographic Citation
Bulletin of Electrical Engineering and Informatics Vol.14 No.2 (2025) , 1138-1150
Suggested Citation
Nardkulpat A., Boonpook W., Sitthi A., Tan Y. Machine learning-based and synthetic aperture radar time-series data for rice classification over Sentinel-1 imagery. Bulletin of Electrical Engineering and Informatics Vol.14 No.2 (2025) , 1138-1150. 1150. doi:10.11591/eei.v14i2.7833 Retrieved from: https://hdl.handle.net/20.500.14740/20433
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Author's Affiliation
Corresponding Author(s)
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Abstract
Rice extraction is critical in remote sensing, especially in Suphan Buri province, Thailand, using Sentinel-1 synthetic aperture radar (SAR) time-series data and advanced machine learning algorithms. Given the challenges of varied terrains and diverse crop types, the research employs different polarization modes (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV+VH) to enhance classification accuracy. The study evaluates the performance of three machine learning algorithms: random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The results demonstrate that combined VV+VH polarization outperforms VV and VH alone, providing better accuracy due to its ability to capture more detailed object features. LightGBM emerged as the most effective among the algorithms, particularly when dealing with large datasets. After hyperparameter tuning (n_estimators: 820, max_depth: 10, and learning_rate: 0.01), LightGBM achieved the highest accuracy. The rice class showed exceptional precision, recall, and F1-score, surpassing other land-use classes (agriculture/forest and urban areas). However, these classes still pose challenges, highlighting the need for future studies to integrate multi-sensor data and explore more sophisticated machine-learning models. This research offers a promising approach to enhancing rice monitoring and management in diverse agricultural landscapes, contributing to more accurate and efficient farming practices.