Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images

Yan Guo, Jia He, Huifang Zhang, Zhou Shi, Panpan Wei, Yuhang Jing, Xiuzhong Yang, Yan Zhang, Laigang Wang () and Guoqing Zheng
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Yan Guo: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Jia He: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Huifang Zhang: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Zhou Shi: Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Panpan Wei: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Yuhang Jing: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Xiuzhong Yang: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Yan Zhang: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Laigang Wang: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Guoqing Zheng: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China

Agriculture, 2024, vol. 14, issue 3, 1-17

Abstract: Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (H dsm ) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R 2 , root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the H dsm , are 0.58, 4528.23 kg/hm 2 , and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm 2 ) is slightly smaller than the measured mean AGB (16,960.23 kg/hm 2 ). (2) The R 2 , RMSE, and RPD of the improved AGB estimation model, based on AGB/H dsm , are 0.88, 2291.90 kg/hm 2 , and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm 2 ) is more similar to the measured mean AGB (17,222.59 kg/hm 2 ). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the H dsm . Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

Keywords: aboveground biomass; UAV; height; transferability; BP neural network; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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