Affiliation:
1. State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2. Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, China
3. Key Laboratory of Crop Drought Tolerance Research of Hebei Province, Hengshui 053000, China
4. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract
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 ‘b1−b2/b3−b4’ 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.
Funder
National Key Research and Development Program of China
Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences
Key Research and Development Program of Hebei province
Subject
General Earth and Planetary Sciences
Cited by
1 articles.
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