Detecting winter canola(Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data

查看全文 作　　者：Chao [1]Zhang;Zi’ang [1]Xie;Jiali [2]Shang;Jiangui [2]Liu;Taifeng [2]Dong;Min [1]Tang;Shaoyuan [1]Feng;Huanjie [3]Cai

机构地区：[1]College of Hydraulic Science and Engineering,Yangzhou University,Yangzhou 225009,Jiangsu,China;[2]Agriculture and Agri-Food Canada,Ottawa Research and Development Centre,960 Carling Avenue,Ottawa,ON K1A 0C6,Canada;[3]Key Laboratory of Agricultural Soil and Water Engineering in Arid Area of Ministry of Education,Northwest A&F University,Yangling 712100,Shaanxi,China

出　　处：《The Crop Journal》索引2022年第10卷第5期,共10页

基　　金：supported by the National Natural Science Foundation of China (51909228);the Postdoctoral Science Foundation of China (2020M671623);the ‘‘Blue Project” of Yangzhou University。 摘　　要：Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology. 关键词：Time-seriesⅥ Asymmetric Gaussian function Phenological stage Shape model Remote sensing

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Detecting winter canola(Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data

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