In recent years, various techniques have been developed to generate crop-type maps based on remote sensing data. Wheat and barley are two major cereal crops cultivated as the first and fourth largest grain crops across the globe. The variations in spectral temporal profile of both crops are generally insignificant at small scales and therefore the two crops are phenologically fairly clearly separated; however, at large scale areas the variance of phenological parameters increases for both crops due to the effects of various climatic and orographic factors which adversely influences discrimination of wheat and barley. Additionally, wheat and barley are usually cultivated as both spring and winter or early and late season crops in some areas, making it more difficult to distinguish them. Therefore, developing a new method based on remote sensing data for effective discrimination of wheat and barley is an important necessity in the field of precision agriculture. To this end, this research presents a new phenology-based method to discriminate barley from wheat. In this study, Sentinel-2 (S2) time-series data of a study site in Iran (Markazi) and two sites in the USA (Idaho and North California), are employed. Spectral reflectance values of wheat and barley are examined during the growing season and a new spectral-temporal feature is successfully developed for automatic identification of the barley heading date. The Relief-f algorithm is then employed to select appropriate spectral features of S2 to distinguish wheat from barley at the heading date. Finally, generated spectral features at the heading date are used as input to Support Vector Machine (SVM) and Random Forest (RF) to produce barley and wheat maps. The Kappa coefficient and overall accuracy (OA) obtained for the three study sites are more than 0.67 and 76%, respectively. The findings of this study demonstrate the potential of remote sensing data to identify the phenological growth stages of barley and distinguish it successfully from wheat.

近年来，已经开发了各种技术来生成基于遥感数据的作物类型地图。小麦和大麦是全球种植的第一和第四大粮食作物的两种主要谷类作物。两种作物的光谱时间分布的变化在小尺度上通常不显着，因此这两种作物在物候上相当明显地分开；然而，在大面积地区，由于各种气候和地形因素的影响，这两种作物的物候参数的差异都会增加，这些因素会对小麦和大麦的区分产生不利影响。此外，在某些地区，小麦和大麦通常作为春、冬或早晚季作物种植，难以区分。所以，开发一种基于遥感数据的小麦和大麦有效鉴别新方法是精准农业领域的重要需求。为此，本研究提出了一种基于物候学的新方法来区分大麦和小麦。在这项研究中，使用了伊朗 (Markazi) 的一个研究地点和美国的两个地点（爱达荷州和北加利福尼亚州）的 Sentinel-2 (S2) 时间序列数据。在生长季节检查小麦和大麦的光谱反射率值，并成功开发了一种新的光谱时间特征，用于自动识别大麦抽穗日期。然后采用 Relief-f 算法选择合适的 S2 光谱特征来区分抽穗日期的小麦和大麦。最后，在抽穗日期生成的光谱特征用作支持向量机 (SVM) 和随机森林 (RF) 的输入，以生成大麦和小麦图。三个研究地点的 Kappa 系数和总体准确度 (OA) 分别超过 0.67 和 76%。这项研究的结果证明了遥感数据在识别大麦物候生长阶段并将其与小麦成功区分开来的潜力。