气候变化领域集成服务门户(CCPortal): Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series

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DOI	10.1016/j.rse.2020.111660
	Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series
	d'Andrimont R.; Taymans M.; Lemoine G.; Ceglar A.; Yordanov M.; van der Velde M.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	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. © 2020 The Authors
英文关键词	Anthesis; Brassica napus; Canola; Climate change; Copernicus; Crop modeling; Crop production; Crop yield forecasting; Growing degree days; LUCAS; Monitoring; Oil seed rape; Phenology; Rapeseed; Sentinel-1; Sentinel-2
语种	英语
scopus关键词	Backscattering; Climate change; Crops; Cultivation; Monitoring; Plants (botany); Time series; Anthesis; Brassica napus; Canola; Copernicus; Crop model; Crop production; Crop yield forecasting; Growing degree days; LUCAS; Oil seed rape; Phenology; Rapeseed; Sentinel-1; Sentinel-2; Oilseeds; agricultural modeling; canola; climate change; detection method; flowering; phenology; Sentinel; time series analysis; Bavaria; Brandenburg [Germany]; Germany; Mecklenburg-West Pomerania; Brassica napus; Brassica napus var. napus
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179446
作者单位	European Commission, Joint Research Centre (JRC), Ispra, Italy
推荐引用方式 GB/T 7714	d'Andrimont R.,Taymans M.,Lemoine G.,et al. Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series[J],2020,239.
APA	d'Andrimont R.,Taymans M.,Lemoine G.,Ceglar A.,Yordanov M.,&van der Velde M..(2020).Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series.Remote Sensing of Environment,239.
MLA	d'Andrimont R.,et al."Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series".Remote Sensing of Environment 239(2020).