Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100-and 300-m S1 Products

	Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100-and 300-m S1 Products
	Zheng, Yang 1; Zhang, Miao 1; Zhang, Xin 1; Zeng, Hongwei 1; Wu, Bingfang 1
刊名	REMOTE SENSING
	2016
卷号	8 期号:10
关键词	WINTER-WHEAT YIELD LEAF-AREA-INDEX ENSEMBLE KALMAN FILTER REMOTE-SENSING INFORMATION CROP MODEL MODIS LAI SIMULATION GROWTH CHINA PERFORMANCE
通讯作者	Wu, BF (reprint author), Chinese Acad Sci, Key Lab Digital Earth, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.
英文摘要	Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 <= R-2 <= 0.94 for the red band, 0.50 <= R-2 <= 0.95 for the near-infrared band, and 0.88 <= R-2 <= 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%-20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R-2 and low root mean square errors (RMSE) (0.864 <= R-2 <= 0.871 and 168 <= RMSE <= 191 g/m(2) for biomass; and 0.631 <= R-2 <= 0.663 and 41.8 <= RMSE <= 62.8 g/m(2) for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.
学科主题	Remote Sensing
类目[WOS]	Remote Sensing
收录类别	SCI
语种	英语
WOS记录号	WOS:000387357300038
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39231]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.Chinese Acad Sci, Key Lab Digital Earth, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zheng, Yang,Zhang, Miao,Zhang, Xin,et al. Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100-and 300-m S1 Products[J]. REMOTE SENSING,2016,8(10).
APA	Zheng, Yang,Zhang, Miao,Zhang, Xin,Zeng, Hongwei,&Wu, Bingfang.(2016).Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100-and 300-m S1 Products.REMOTE SENSING,8(10).
MLA	Zheng, Yang,et al."Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100-and 300-m S1 Products".REMOTE SENSING 8.10(2016).