title: [Division of winter wheat yield estimation by remote sensing based on MODIS EVI time series data and spectral angle clustering].
abstract: Crop yield estimation division is the basis of crop yield estimation; it provides an important scientific basis for estimation research and practice. In the paper, MODIS EVI time-series data during winter wheat growth period is selected as the division data; JiangSu province is study area; A division method combined of advanced spectral angle mapping(SVM) and K-means clustering is presented, and tested in winter wheat yield estimation by remote sensing. The results show that: division method of spectral angle clustering can take full advantage of crop growth process that is reflected by MODIS time series data, and can fully reflect region differences of winter wheat that is brought by climate difference. Compared with the traditional division method, yield estimation result based on division result of spectral angle clustering has higher R2 (0.702 6 than 0.624 8) and lower RMSE (343.34 than 381.34 kg x hm(-2)), reflecting the advantages of the new division method in the winter wheat yield estimation. The division method in the paper only use convenient obtaining time-series remote sensing data of low-resolution as division data, can divide winter wheat into similar and well characterized region, accuracy and stability of yield estimation model is also very good, which provides an efficient way for winter wheat estimation by remote sensing, and is conducive to winter wheat yield estimation.
authors.affiliation: State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China. zzcsunmoonstar@163.com
authors.forename: Zai-Chun
authors.initials: ZC
authors.lastname: Zhu
authors.forename: Lian-Qun
authors.initials: LQ
authors.lastname: Chen
authors.forename: Jin-Shui
authors.initials: JS
authors.lastname: Zhang
authors.forename: Yao-Zhong
authors.initials: YZ
authors.lastname: Pan
authors.forename: Wen-Quan
authors.initials: WQ
authors.lastname: Zhu
authors.forename: Tan-Gao
authors.initials: TG
authors.lastname: Hu
mesh_terms: D016000:Cluster Analysis; D008962:Models, Theoretical; D058998:Remote Sensing Technology; D013057:Spectrum Analysis; D014908:Triticum