Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

中文翻译：

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利用Sentinel-2图像早期识别种子玉米和常见玉米生产田

准确及时地进入农作物种子的生产区域，可以监控种子市场和安全的种子供应。种子玉米和普通玉米生产田通常具有相似的物候发展概况，但种植方式有所不同，这使得将这些田与十分之一分辨率的卫星图像区分开来具有挑战性。在这项研究中，我们提出了一种使用遥感影像的时间序列在甘肃省凉州区进行的，在生长期尽可能早地鉴定出玉米种子生产田地的方法。我们收集了从三月到九月捕获的Sentinel-2和GaoFen-1（GF-1）图像。分类的特征空间包括四个原始波段，即红色，绿色，蓝色和近红外（nir），以及八个植被指数。我们使用不同时间跨度的Sentinel-2时间序列分析了种子玉米鉴定的及时性，并确定了最早的时间框架以实现合理的分类精度。然后，对满足监管准确性要求的最早时间序列进行比较和分析。测试了四种机器/深度学习算法，包括K近邻（KNN），支持向量分类（SVC），随机森林（RF）和长短期记忆（LSTM）。结果表明，使用3月到6月的Sentinel-2图像，RF和LSTM算法的准确性达到88％以上，其中LSTM表现最佳（90％）。相比之下，KNN和SVC的准确性在82％至86％之间。6月底，可以在试验区进行种子玉米作图，精度可以满足种业监测的基本要求。使用GF-1图像进行分类的准确性和可靠性较低；使用3月到6月的图像，准确性为85％。为了在生长期早期实现对玉米种子田的近实时识别，我们针对当前季节采用了自动样本生成方法，该方法仅使用基于聚类分析的历史样本。到本季度末（9月），使用从历史地图中提取的新样本进行分类的准确率达到74％，到7月底达到63％。这项研究提供了重要的见解，可利用遥感图像对使用相同作物但种植方式不同的耕地进行分类。