Improving potato above ground biomass estimation combining hyperspectral data and harmonic decomposition techniques

作者：Yang Liu, Haikuan Feng, Yiguang Fan, Jibo Yue, Riqiang Chen, Yanpeng Ma, Mingbo Bian, Guijun Yang · 发表于：Computers and Electronics in Agriculture · 年份：2024 · DOI：10.1016/j.compag.2024.108699 · 被引用次数：71 · 研究领域：Spectroscopy and Chemometric Analyses、Remote Sensing in Agriculture、Water Quality Monitoring and Analysis

Accurately estimating potato above-ground biomass (AGB), which is closely associated with the growth and yield of crops, carries significant importance for guiding field management practices. Hyperspectral techniques have emerged as a powerful and efficient tool for quickly and non-invasively acquiring information about AGB due to its capability to provide rich spectral data closely related to crop physiology and biochemistry. However, using spectral features obtained from hyperspectral data, such as spectral reflectance and vegetation indices (VIs), often leads to inaccurate estimations of crop AGB at multiple growth stages due to spectral saturation effects and dynamic changes in spectral responses. To enhance the robustness of AGB estimation models, this study proposed a harmonic decomposition (HD) method derived from Fourier series to extract energy features. The ground (referred to as ASD) and unmanned aerial vehicle hyperspectral (referred to as UHD185) remote sensing data from three growth stages of potatoes in 2018 (validation set) and 2019 (calibration set) were utilized in the study. Firstly, a comparison was made between the spectral reflectance of the potato canopy measured by the ASD and UHD185 sensors. Subsequently, the correlation between spectral reflectance, VIs, and harmonic components obtained from ASD and UHD185 sensors was analyzed in relation to AGB at both the individual and whole growth stage. Then, sensitive bands selected through CARS (competitive ad...