A novel methodology is proposed to robustly map oil seed rape (OSR) flowering phenology from time series generated from the Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) sensors. The time series are averaged at parcel level, initially for a set of 229 reference parcels for which multiple phenological observations on OSR flowering have been collected from April 21 to May 19, 2018. The set of OSR parcels is extended to a regional sample of 32,355 OSR parcels derived from a regional S2 classification. The study area comprises the northern Brandenburg and Mecklenburg-Vorpommern (N) and the southern Bavaria (S) regions in Germany. A method was developed to automatically compute peak flowering at parcel level from the S2 time signature of the Normalized Difference Yellow Index (NDYI) and from the local minimum in S1 VV polarized backscattering coefficients. Peak flowering was determined at a temporal accuracy of 1 to 4 days. A systematic flowering delay of 1 day was observed in the S1 detection compared to S2. Peak flowering differed by 12 days between the N and S. Considerable local variation was observed in the N-S parcel-level flowering gradient. Additional in-situ phenology observations at 70 Deutscher Wetterdienst (DWD) stations confirm the spatial and temporal consistency between S1 and S2 signatures and flowering phenology across both regions. Conditions during flowering strongly determine OSR yield, therefore, the capacity to continuously characterize spatially the timing of key flowering dates across large areas is key. To illustrate this, expected flowering dates were simulated assuming a single OSR variety with a 425 growing degree days (GDD) requirement to reach flowering. This GDD requirement was calculated based on parcel-level peak flowering dates and temperatures accumulated from 25-km gridded meteorological data. The correlation between simulated and S2 observed peak flowering dates still equaled 0.84 and 0.54 for the N and S respectively. These Sentinel-based parcel-level flowering parameters can be combined with weather data to support in-season predictions of OSR yield, area, and production. Our approach identified the unique temporal signatures of S1 and S2 associated with OSR flowering and can now be applied to monitor OSR phenology for parcels across the globe.

中文翻译：

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使用 Sentinel-1 和 -2 时间序列检测油菜包裹中的开花物候

提出了一种新方法，可根据由哥白尼 Sentinel-1 (S1) 和 Sentinel-2 (S2) 传感器生成的时间序列稳健地绘制油菜 (OSR) 开花物候学。时间序列在宗地级别取平均值，最初针对一组 229 个参考宗地，这些宗地收集了 2018 年 4 月 21 日至 5 月 19 日期间对 OSR 开花的多次物候观测。 OSR 宗地集扩展到 32,355 个区域样本源自区域 S2 分类的 OSR 宗地。研究区域包括德国的勃兰登堡北部和梅克伦堡-前波美拉尼亚 (N) 以及巴伐利亚南部 (S) 地区。开发了一种方法，用于根据归一化黄色指数 (NDYI) 的 S2 时间特征和 S1 VV 极化反向散射系数的局部最小值自动计算包裹级别的开花峰值。以 1 至 4 天的时间精度确定开花高峰。与 S2 相比，在 S1 检测中观察到 1 天的系统开花延迟。N 和 S 之间的开花高峰期相差 12 天。在 NS 包裹级开花梯度中观察到相当大的局部变化。在 70 个 Deutscher Wetterdienst (DWD) 站进行的其他原位物候观测证实了这两个地区的 S1 和 S2 特征与开花物候之间的空间和时间一致性。开花期间的条件强烈决定 OSR 产量，因此，在空间上连续表征大面积关键开花日期的能力是关键。为了说明这一点，我们模拟了预期开花日期，假设单个 OSR 品种需要 425 个生长期 (GDD) 才能开花。此 GDD 要求是根据从 25 公里网格化气象数据中累积的宗地级高峰开花日期和温度计算得出的。对于 N 和 S，模拟和 S2 观察到的高峰开花日期之间的相关性仍然分别等于 0.84 和 0.54。这些基于 Sentinel 的地块级别开花参数可以与天气数据相结合，以支持 OSR 产量、面积和产量的季节性预测。