ArticleScientific data2019
High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A comprehensive UK crop yield dataset incorporating satellite, weather, and soil type information.Scientific data · 2026Article
- Article
- Estimating aman and aus rice area in Bangladesh using sentinel-1 imagery and machine learning algorithms.PloS one · 2026Article
- Deep Architectures Fail to Generalize: A Lightweight Alternative for Agricultural Domain Transfer in Hyperspectral Images.Sensors (Basel, Switzerland) · 2025Article
- High-resolution maps of rice cropping intensity across Southeast Asia.Scientific data · 2025Article
- Radar versus optical: The impact of cloud cover when mapping seasonal surface water for health applications in monsoon-affected India.PloS one · 2025Article
- CROPGRIDS: a global geo-referenced dataset of 173 crops.Scientific data · 2024Article
- A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020.Scientific data · 2024Article
- Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images.Plant methods · 2024Article
- Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes.Frontiers in pharmacology · 2023Article
- Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation.Scientific reports · 2022Article
- Agricultural SandboxNL: A national-scale database of parcel-level processed Sentinel-1 SAR data.Scientific data · 2022Article
- High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data.Scientific data · 2019Article
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4 authors.
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Abstract
Knowledge of where, when, and how much paddy rice is planted is crucial information for understating of regional food security, freshwater use, climate change, and transmission of avian influenza virus. We developed seasonal paddy rice maps at high resolution (10 m) for Bangladesh and Northeast India, typical cloud-prone regions in South Asia, using cloud-free Synthetic Aperture Radar (SAR) images from Sentinel-1 satellite, the Random Forest classifier, and the Google Earth Engine (GEE) cloud computing platform. The maps were provided for all the three distinct rice growing seasons of the region: Boro, Aus and Aman. The paddy rice maps were evaluated against the independent validation samples, and compared with the existing products from the International Rice Research Institute (IRRI) and the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The generated paddy rice maps were spatially consistent with the compared maps and had a satisfactory accuracy over 90%. This study showed the potential of Sentinel-1 data and GEE on large scale paddy rice mapping in cloud-prone regions like tropical Asia.
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