d'Andrimont Raphaël, Taymans Matthieu, Lemoine Guido, Ceglar Andrej, Yordanov Momchil, van der Velde Marijn
European Commission, Joint Research Centre (JRC), Ispra, Italy.
Remote Sens Environ. 2020 Mar 15;239:111660. doi: 10.1016/j.rse.2020.111660.
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.
本文提出了一种新方法，可根据哥白尼哨兵 -1（S1）和哨兵 -2（S2）传感器生成的时间序列，稳健地绘制油菜（OSR）开花物候图。时间序列在地块层面进行平均，最初是针对一组229个参考地块，在2018年4月21日至5月19日期间收集了这些地块上关于油菜开花的多次物候观测数据。油菜地块集合扩展到了一个从区域S2分类中获取的包含32355个油菜地块的区域样本。研究区域包括德国的北勃兰登堡和梅克伦堡 - 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，可根据归一化差异黄指数（NDYI）的S2时间特征以及S1 VV极化后向散射系数的局部最小值，自动计算地块层面的开花峰值。开花峰值的确定时间精度为1至4天。与S2相比，在S1检测中观察到开花有1天的系统性延迟。N和S之间的开花峰值相差12天。在N - S地块层面的开花梯度上观察到了相当大的局部差异。在70个德国气象局（DWD）站点进行的额外实地物候观测证实了S1和S2特征与两个地区开花物候之间的时空一致性。开花期间的条件强烈决定油菜产量，因此，能够在空间上持续表征大面积关键开花日期的时间是关键。为了说明这一点，假设一个单一的油菜品种达到开花需要425个生长度日（GDD），模拟了预期开花日期。这个GDD要求是根据地块层面的开花峰值日期和从25公里网格气象数据中积累的温度计算得出的。对于N和S地区，模拟的和S2观测到的开花峰值日期之间的相关性分别仍为0.84和0.54。这些基于哨兵的地块层面开花参数可以与气象数据相结合，以支持油菜产量、面积和产量的季内预测。我们的方法确定了与油菜开花相关的S1和S2独特时间特征，现在可应用于监测全球各地地块的油菜物候。
