Abstract Crop maps produced by remote sensing data play an important role in agricultural crop studies. Most of the crop mapping methods rely on field samples to train a model, which is a costly and time-consuming process. On the other hand, automatic crop mapping methods which are based on unique spectral characteristics are independent of ground truth data. Since most crops have specific spectral and temporal features during the growing season, this research aims at developing a new automatic method to discriminate potato from other crops using Sentinel-2 time series imageries. In this research, Crop type data of three study sites in Iran consisting of 2019 fields of potato and other crops, which were sampled by a GPS receiver were used. Moreover, an additional site in the United States comprised of 880 fields from Cropland Data Layer (CDL) in raster format, was also utilized. We employed 50% (2 9 2) of the Hamedan fields to train the model and the remaining 2607 fields from other sites and 50% from Hamedan were used to validate the model. Then, the temporal reflectance spectra of various crops and potato were extracted and considered. Results show that potato has four unique spectral characteristics which can be utilized to distinguish potato fields. These include the near-infrared reflectance values at the cultivation and harvest dates, variations of the near-infrared reflectance at the greenness peak time and the ratio of the near-infrared reflectance values to the red reflectance values at the greenness peak. Therefore, a novel feature was proposed based on a combination of the above spectral characteristics for discrimination of potato fields with a kappa coefficient of higher than 0.8 and an overall accuracy of better than 90%, in the four study sites.

中文翻译：

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一种使用 Sentinel-2 图像时间序列自动映射马铃薯的新方法

摘要 遥感数据制作的作物图在农作物研究中具有重要作用。大多数作物制图方法依赖于田间样本来训练模型，这是一个昂贵且耗时的过程。另一方面，基于独特光谱特征的自动作物制图方法与地面实况数据无关。由于大多数作物在生长季节具有特定的光谱和时间特征，因此本研究旨在开发一种新的自动方法，使用 Sentinel-2 时间序列图像将马铃薯与其他作物区分开来。在这项研究中，使用了由 GPS 接收器采样的伊朗三个研究地点的作物类型数据，包括 2019 年的马铃薯和其他作物田。而且，还利用了美国的另一个站点，该站点由栅格格式的农田数据层 (CDL) 的 880 个字段组成。我们使用 50% (2 9 2) 的哈马丹字段来训练模型，其余 2607 个来自其他站点的字段和来自哈马丹的 50% 用于验证模型。然后，提取并考虑了各种作物和马铃薯的时间反射光谱。结果表明，马铃薯具有四种独特的光谱特征，可用于区分马铃薯田地。这些包括栽培和收获日期的近红外反射率值、绿度峰值时间近红外反射率的变化以及绿度峰值时近红外反射率值与红色反射率值的比值。所以，