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

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 adaptive reweighted sampling), the entire spectral reflectance, VIs, and harmonic components, were utilized to construct AGB estimation models by partial least squares regression (PLSR). Finally, the optimal model performance was validated across different years, growth stages, and treatment conditions. The results showed there were differences in spectral reflectance acquired by ASD and UHD185 sensors across various wavelengths, but overall, there was a high level of consistency between the two. The correlation of spectral reflectance and VIs with potato AGB at individual growth stage was notably higher than that observed for entire growth stages. The accuracy of AGB estimation using VIs obtained from ASD (the R2, RMSE and NRMSE of validation sets were 0.52, 592 kg/hm2 and 26.91 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.46, 612 kg/hm2 and 27.82 %, respectively) sensors were low. Utilizing sensitive bands and full spectral reflectance separately improved the precision of models, although the enhancement was somewhat limited. The HD-PLSR models from ASD (the R2, RMSE and NRMSE of validation sets were 0.69, 477 kg/hm2 and 21.69 %, respectively) and UHD185 (the R2, RMSE and NRMSE of validation sets were 0.66, 481 kg/hm2 and 21.86 %, respectively) achieved the best AGB estimation results. Using the HD-PLSR model to estimate AGB for two years, the R2 values were 0.79 and 0.76 for ASD and UHD185, with RMSE values of 381 kg/hm2 and 386 kg/hm2 and NRMSE values of 22.35 % and 22.70 %, respectively. The capability of the HD-PLSR model was confirmed at various growth stages and treatments. This work offers valuable remote sensing technical support for implementing potato growth monitoring and yield assessment in the field.