Liu Xiuyu, Li Xuehua, Gao Lixin, Zhang Jinshui, Qin Dapeng, Wang Kun, Li Zhenhai
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China.
Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Front Plant Sci. 2023 Jul 24;14:1016890. doi: 10.3389/fpls.2023.1016890. eCollection 2023.
Winter wheat is one of the major food crops in China, and timely and effective early-season identification of winter wheat is crucial for crop yield estimation and food security. However, traditional winter wheat mapping is based on post-season identification, which has a lag and relies heavily on sample data. Early-season identification of winter wheat faces the main difficulties of weak remote sensing response of the vegetation signal at the early growth stage, difficulty of acquiring sample data on winter wheat in the current season in real time, interference of crops in the same period, and limited image resolution. In this study, an early-season refined mapping method with winter wheat phenology information as priori knowledge is developed based on the Google Earth Engine cloud platform by using Sentinel-2 time series data as the main data source; these data are automated and highly interpretable. The normalized differential phenology index (NDPI) is adopted to enhance the weak vegetation signal at the early growth stage of winter wheat, and two winter wheat phenology feature enhancement indices based on NDPI, namely, wheat phenology differential index (WPDI) and normalized differential wheat phenology index (NDWPI) are developed. To address the issue of " different objects with the same spectra characteristics" between winter wheat and garlic, a plastic mulched index (PMI) is established through quantitative spectral analysis based on the differences in early planting patterns between winter wheat and garlic. The identification accuracy of the method is 82.64% and 88.76% in the early overwintering and regreening periods, respectively, These results were consistent with official statistics (R2 = 0.96 and 0.98, respectively). Generalization analysis demonstrated the spatiotemporal transferability of the method across different years and regions. In conclusion, the proposed methodology can obtain highly precise spatial distribution and planting area information of winter wheat 4_6 months before harvest. It provides theoretical and methodological guidance for early crop identification and has good scientific research and application value.
冬小麦是中国主要粮食作物之一，及时有效地对冬小麦进行早期识别对于作物产量估算和粮食安全至关重要。然而，传统的冬小麦制图基于季末识别，存在滞后性且严重依赖样本数据。冬小麦的早期识别面临着植被信号在生长初期遥感响应较弱、实时获取当季冬小麦样本数据困难、同期作物干扰以及图像分辨率有限等主要难题。本研究基于谷歌地球引擎云平台，以哨兵 - 2时间序列数据作为主要数据源，开发了一种以冬小麦物候信息为先验知识的早期精细化制图方法；这些数据具有自动化且高度可解释的特点。采用归一化差分物候指数（NDPI）来增强冬小麦生长初期较弱的植被信号，并基于NDPI开发了两个冬小麦物候特征增强指数，即小麦物候差异指数（WPDI）和归一化差分小麦物候指数（NDWPI）。为解决冬小麦与大蒜之间“同谱异物”的问题，基于冬小麦和大蒜早期种植模式的差异，通过定量光谱分析建立了地膜覆盖指数（PMI）。该方法在越冬初期和返青期的识别准确率分别为82.64%和88.76%，这些结果与官方统计数据一致（R²分别为0.96和0.98）。泛化分析表明该方法在不同年份和地区具有时空可转移性。综上所述，所提出的方法能够在收获前4 - 6个月获得高精度的冬小麦空间分布和种植面积信息。它为作物早期识别提供了理论和方法指导，具有良好的科研和应用价值。
