Crop segregation in optical remote sensing cannot always be easily achieved among similar species since their spectral response can be similar. We aimed to distinguish fields of two C4 gramineae with similar plant structure and crop calendar, maize (Zea mays) and sorghum (Sorghum bicolor), in an agricultural area of the state of Guanajuato, in central México. One hundred and forty-three fields of these two crops were identified and their average spectral signatures were derived at different phenological stages, using digital information from 11 Sentinel 2A/B images, corresponding to the 2019 spring–summer cycle. We also calculated the temporal profile of six spectral indices for each crop, to evaluate seasonal variations and conducted a cropland classification, using the random forests algorithm and different combinations of dates and indices. Results show that segregation of maize and sorghum fields can be achieved due to small differences in the near-infrared region of their spectral signatures and disparities in the rate of decay during the senescence stage. Classification results also confirmed the separability of these two crops, obtaining a very high overall accuracy and kappa values (98.43% and 97.07%, correspondingly) based on the reflectance of nine images and only three spectral indices: the normalized difference vegetation index, the normalized difference water index, and the structure insensitive pigment index. This investigation demonstrates that the combined use of time series of high-resolution images and spectral indices can contribute to distinguish fields of similar crop species.

中文翻译：

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利用多时相Sentinel-2数据在墨西哥中部进行玉米和高粱田间隔离

由于相似物种之间的光谱响应可能相似，因此在光学遥感中无法始终实现作物隔离。我们的目标是在墨西哥中部瓜纳华托州的一个农业区中，区分两个具有相似植物结构和作物日历的C4禾本科植物，玉米（Zea mays）和高粱（Sorghum bicolor）。利用来自11张Sentinel 2A / B图像的数字信息（对应于2019年春夏周期），鉴定了这两种作物的143个田地，并在不同物候阶段得出了它们的平均光谱特征。我们还使用随机森林算法以及日期和索引的不同组合，为每种作物计算了六个光谱指数的时间分布图，以评估季节变化并进行了农田分类。结果表明，由于玉米和高粱的光谱特征的近红外区域存在细微差异，并且在衰老阶段的衰变速率存在差异，因此可以实现玉米和高粱的分离。分类结果还证实了这两种农作物的可分离性，基于九幅图像的反射率和仅三个光谱指数（归一化差异植被指数，归一化指数），获得了非常高的总体准确性和卡伯值（分别为98.43％和97.07％）差异水指数，以及结构不敏感的颜料指数。这项调查表明，高分辨率图像的时间序列和光谱指数的组合使用可以有助于区分相似农作物的田间。