Global 30-m resolution distribution maps of winter-triticeae crops from 2017 to 2022

Winter-triticeae crops, such as winter wheat, winter barley, winter rye, and triticale, are important in human diets and planted worldwide, and thus accurate spatial distribution information of winter-triticeae crops is crucial for monitoring crop production and food security. However, there is still a lack of global high-resolution maps of winter-triticeae crops because of the reliance of existing crop mapping methods on training samples, which limits their application at the global scale. In this study, we propose a new method based on the Winter-Triticeae Crops Index (WTCI) for global winter-triticeae crops mapping. This is a new sample-free method for identifying winter-triticeae crops based on differences in their normalized difference vegetation index (NDVI) characteristics from the heading to the harvesting stages and those of other types of vegetation. Based on this new method, we produced the first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022. Validation using field survey samples and Google Earth samples indicated that the method exhibited satisfying performance and stable spatiotemporal transferability, with producer’s accuracy, user’s accuracy and overall accuracy of 81.12%, 87.85% and 87.7%, respectively. Moreover, compared with the Cropland Data Layer (CDL) and the Land Parcel Identification System (LPIS) datasets, the overall accuracy and F1 score in most regions of the United States and Europe were more than 80% and 0.75. The identified area of winter-triticeae crops was consistent with the agricultural statistical area in almost all investigated counties or regions, and the correlation coefficient (R2) between the identified area and the statistical area was over 0.6, while the relative mean absolute error (RMAE) was less than 30% in all six years. Overall, this study provides a reliable and automatic identification method for winter-triticeae crops without any training samples. The high-resolution distribution maps of global winter-triticeae crops are expected to support multiple agricultural applications.冬小麦族作物（Winter-triticeae crops），如冬小麦、冬大麦、冬黑麦与小黑麦，是人类膳食结构中的关键作物，且在全球范围内广泛种植。因此，精准的冬小麦族作物空间分布信息，对于作物生产监测与粮食安全保障均具有重要意义。然而，现有作物制图方法均依赖训练样本，这限制了其在全球尺度的应用，目前仍缺乏全球高分辨率的冬小麦族作物分布图集。本研究提出一种基于冬小麦族作物指数（Winter-Triticeae Crops Index, WTCI）的全新方法，用于全球冬小麦族作物制图。该方法是一种无训练样本的创新识别方案，可基于冬小麦族作物从抽穗期至收获期的归一化差异植被指数（Normalized Difference Vegetation Index, NDVI）特征与其他植被类型的特征差异，实现该类作物的精准识别。基于该新方法，我们生成了2017至2022年全球首套30米分辨率的冬小麦族作物分布图谱。通过野外调查样本与谷歌地球（Google Earth）样本开展验证，结果表明该方法性能优异且时空迁移性稳定，其生产者精度、用户精度及总体精度分别达81.12%、87.85%与87.7%。此外，相较于耕地数据层（Cropland Data Layer, CDL）与地块识别系统（Land Parcel Identification System, LPIS）数据集，欧美多数区域的总体精度与F1分数均超过80%与0.75。本研究识别的冬小麦族作物种植面积，与绝大多数调研县域或区域的农业统计面积高度吻合；识别面积与统计面积间的相关系数（R²）均大于0.6，且六年的相对平均绝对误差（Relative Mean Absolute Error, RMAE）均低于30%。总体而言，本研究提供了一种无需任何训练样本的可靠冬小麦族作物自动识别方法。这套全球高分辨率冬小麦族作物分布图谱，有望为多类农业应用提供有力支撑。