Weed management in maize farms is a time-specific activity and requires timely detection of weed infestations. The challenge to early detection of weeds is that many dicotyledonous crops and broad-leaved weeds often display similar reflectance profile in the early growth stage and requires hyperspectral data to detect them. However, the advent of Sentinel-2 sensor series, with enhanced spectral configuration featuring red-edge bands that are known for species-level discrimination of plants, presents an affordable opportunity to detect weeds using multispectral data. The present study explores the question of whether Sentinel-2 sensor with its advanced spectral configuration can differentiate weeds from maize () in the early growth stages of maize. The study recorded 165 GPS points of weeds, maize, and mixed class in six maize farms during the early stages of maize growth. These GPS points were overlaid on Sentinel-2 images acquired within two days of field data gathering to guide the collection of spectral signatures of the maize, mixed, and weed classes. Spectral signatures were divided into training (70%) and validation (30%) data in a Random Forest (RF) model with S-2 spectral bands and vegetation indices as predictor variables. Spectral signatures were firstly tested for spectral separability between classes using ANOVA. The results of spectral analysis showed that the weed class had higher interclass variability from the maize and mixed class particularly in the red-edge and NIR regions of Sentinel-2. The classification matrix consistently showed that weeds were detected with high user and producers’ accuracy of 95%. These results indicate the utility of the enhanced spectral configuration of Sentinel-2 data in the early detection of weeds in maize farms.

中文翻译：

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使用 Sentinel-2 数据绘制玉米田杂草侵染情况

玉米农场的杂草管理是一项特定时间的活动，需要及时发现杂草侵扰。杂草早期检测面临的挑战是，许多双子叶作物和阔叶杂草在生长早期往往表现出相似的反射率轮廓，需要高光谱数据来检测它们。然而，Sentinel-2 传感器系列的出现，具有增强的光谱配置，以红边波段为特色，以植物物种级别的区分而闻名，为使用多光谱数据检测杂草提供了一个经济实惠的机会。本研究探讨了Sentinel-2传感器以其先进的光谱配置能否在玉米生长早期区分杂草和玉米的问题。该研究记录了 6 个玉米农场在玉米生长早期阶段杂草、玉米和混合类的 165 个 GPS 点。这些 GPS 点叠加在现场数据收集两天内采集的 Sentinel-2 图像上，以指导玉米、混合玉米和杂草类光谱特征的收集。光谱特征在随机森林 (RF) 模型中分为训练数据 (70%) 和验证数据 (30%)，以 S-2 光谱带和植被指数作为预测变量。首先使用方差分析测试光谱特征的类之间的光谱可分离性。光谱分析结果表明，杂草类与玉米和混合类相比具有更高的类间变异性，特别是在 Sentinel-2 的红边和近红外区域。分类矩阵一致表明，用户和生产者的杂草检测准确率高达 95%。这些结果表明 Sentinel-2 数据的增强光谱配置在玉米农场杂草的早期检测中的效用。