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<p>Radar remote sensing data is underused for agricultural applications due to the complexity of its pre-processing and to the non-obvious physical interpretation of the derived features. To address these challenges, this work presents the SAR and Optical Dataset for Agriculture in Seville (SODAS), which integrates time series of radar images (Sentinel-1), optical images (Sentinel-2), precipitation records, and crop-type maps. The georeferenced images cover an agricultural area in Seville, Spain, from 2017 to 2021. The SAR images are provided in the form of dual-polarimetric covariance matrices, which include the backscattering coefficient and the correlation between channels, and repeat-pass interferometric products (coherence and phase) at VV and VH polarimetric channels. The optical images correspond to reflectivity at red, green, blue, and near infra-red bands, as well as NDVI products. This dataset has many potential uses, such as development of algorithms for crop-type mapping, retrieval of biophysical parameters, crop monitoring, and data fusion. Additionally, a Jupyter notebook to load the dataset, create and compare time series, and visualise images is included.</p>

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A SAR and optical remote sensing dataset in Seville for scientific research in agriculture

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月8日

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  • Arturo Villarroya-Carpio,
  • Víctor Cazcarra-Bes,
  • Alejandro Mestre-Quereda,
  • Juan M. Lopez-Sanchez

摘要

Radar remote sensing data is underused for agricultural applications due to the complexity of its pre-processing and to the non-obvious physical interpretation of the derived features. To address these challenges, this work presents the SAR and Optical Dataset for Agriculture in Seville (SODAS), which integrates time series of radar images (Sentinel-1), optical images (Sentinel-2), precipitation records, and crop-type maps. The georeferenced images cover an agricultural area in Seville, Spain, from 2017 to 2021. The SAR images are provided in the form of dual-polarimetric covariance matrices, which include the backscattering coefficient and the correlation between channels, and repeat-pass interferometric products (coherence and phase) at VV and VH polarimetric channels. The optical images correspond to reflectivity at red, green, blue, and near infra-red bands, as well as NDVI products. This dataset has many potential uses, such as development of algorithms for crop-type mapping, retrieval of biophysical parameters, crop monitoring, and data fusion. Additionally, a Jupyter notebook to load the dataset, create and compare time series, and visualise images is included.