Estimating aman and aus rice area in Bangladesh using sentinel-1 imagery and machine learning algorithms
- 1Computer and GIS Unit, Bangladesh Agricultural Research Council, Dhaka, Bangladesh
- 2ACASA Project, Bangladesh Agricultural Research Council, Dhaka, Bangladesh
- 3Center for Environmental and Geographic Information Services, Public Trust, Ministry of Water Resources, and Bangladesh Agricultural Research Council, Dhaka, Bangladesh
Abstract
Rice (Oryza sativa L.) is Bangladesh’s staple crop, cultivated across three distinct seasons: Rabi (Boro), Kharif-1 (Aus) and Kharif-2 (Aman). This study develops an integrated, cloud-independent framework for mapping Aus and Aman rice areas using freely available Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery; to accomplish this, supervised machine-learning (ML) algorithms are used in the Google Earth Engine (GEE) platform. Time-series dual-polarization (VV and VH) backscatter data were analyzed for June–November 2021 (Aman) and March–August 2022 (Aus). Four ML models such as Classification and Regression Tree (CART), Random Forest (RF), k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) were evaluated to identify the most accurate mapping approach. Among all ML models, k-NN achieved the highest accuracy of 94.5% and 99.8% for Aman and Aus, respectively. Despite the higher accuracy, RF for Aman (92.7% accuracy) and CART for Aus (99.6%) were finally selected based on their lower deviation from area estimates reported by Bangladesh Bureau of Statistics (BBS). The estimated Aman area (5.72 Mha) was closely matched with the official figure from the Bangladesh Bureau of Statistics (BBS), while the Aus estimate (0.76 Mha) showed a 29% underestimation compared to BBS data (1.06 Mha). However, the results demonstrate that Sentinel-1 SAR combined with ML classifiers provides a reliable, scalable, and weather-resilient approach for multi-season rice mapping. The proposed methodology establishes a strong foundation for timely production forecasting and climate-resilient agricultural planning, particularly in cloud-prone tropical regions.
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BibTeX
@article{rahman2026estimating,
title = {{Estimating aman and aus rice area in Bangladesh using sentinel-1 imagery and machine learning algorithms}},
author = {H. M. Hamidur Rahman and Hasan Mahmud and Md Mahfuzul Hasan and S. G. Hussain},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0355502},
url = {https://doi.org/10.1371/journal.pone.0355502},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Estimating aman and aus rice area in Bangladesh using sentinel-1 imagery and machine learning algorithms
AU - H. M. Hamidur Rahman
AU - Hasan Mahmud
AU - Md Mahfuzul Hasan
AU - S. G. Hussain
PY - 2026
DA - 2026/09/18
JO - PLOS One
DO - 10.1371/journal.pone.0355502
UR - https://doi.org/10.1371/journal.pone.0355502
AB - Rice (Oryza sativa L.) is Bangladesh’s staple crop, cultivated across three distinct seasons: Rabi (Boro), Kharif-1 (Aus) and Kharif-2 (Aman). This study develops an integrated, cloud-independent framework for mapping Aus and Aman rice areas using freely available Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery; to accomplish this, supervised machine-learning (ML) algorithms are used in the Google Earth Engine (GEE) platform. Time-series dual-polarization (VV and VH) backscatter data were analyzed for June–November 2021 (Aman) and March–August 2022 (Aus). Four ML models such as Classification and Regression Tree (CART), Random Forest (RF), k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) were evaluated to identify the most accurate mapping approach. Among all ML models, k-NN achieved the highest accuracy of 94.5% and 99.8% for Aman and Aus, respectively. Despite the higher accuracy, RF for Aman (92.7% accuracy) and CART for Aus (99.6%) were finally selected based on their lower deviation from area estimates reported by Bangladesh Bureau of Statistics (BBS). The estimated Aman area (5.72 Mha) was closely matched with the official figure from the Bangladesh Bureau of Statistics (BBS), while the Aus estimate (0.76 Mha) showed a 29% underestimation compared to BBS data (1.06 Mha). However, the results demonstrate that Sentinel-1 SAR combined with ML classifiers provides a reliable, scalable, and weather-resilient approach for multi-season rice mapping. The proposed methodology establishes a strong foundation for timely production forecasting and climate-resilient agricultural planning, particularly in cloud-prone tropical regions.
SN - 1932-6203
ER -