Estimation of Winter Wheat Yield Using Multiple Temporal Vegetation Indices Derived from UAV-Based Multispectral and Hyperspectral Imagery

文献类型： 外文期刊

第一作者： Liu, Yu

作者： Liu, Yu;Sun, Liang;Liu, Binhui;Zhang, Wenying;Wang, Bianyin;Chen, Zhaoyang;Liu, Binhui;Zhang, Wenying;Wang, Bianyin;Chen, Zhaoyang;Wu, Yongfeng;Ma, Juncheng

作者机构：

关键词： UAV; multiple temporal; hyperspectral imagery; field plot scale; vegetation index; yield estimation

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 19 期

页码：

收录情况： SCI

摘要： Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of '(b(1)-b(2))/(b(3)-b(4))' derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale.

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