Year: 2025
Type: article
Abstract: In the context of the need for efficient agricultural monitoring under climate change, this study aimed to achieve rapid and non-destructive monitoring of foxtail millet aboveground biomass (AGB), AGB data and canopy spectral data were measured under varying sowing dates.Preprocessed using Standard Normal Variate (SNV), Multiple Scattering Correction (MSC), and First Derivative (1ST) methods.Based on this, foxtail millet AGB monitoring models were constructed using the full spectrum, characteristic wavelengths, and optimized spectral indices, and their accuracy was evaluated.The results showed that all three preprocessing methods improved the correlation between spectral reflectance and foxtail millet AGB.The best spectral index was 1ST-TB1 (451, 626, 697), which exhibited a correlation coefficient of 0.863 with foxtail millet AGB.Among the constructed models, the 1ST-PLS model based on the full spectrum exhibited higher accuracy (R 2 =0.834;RMSE=1.443t/hm 2 ; RPD=2.507).This study confirms the feasibility of spectral monitoring for foxtail millet AGB and provides a reference for rapid, non-destructive monitoring of foxtail millet AGB, and provide a robust framework for precision agriculture, enabling efficient foxtail millet management and yield optimization.
Institution: Shanxi Agricultural University
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: diamond
Funder: Shanxi Agricultural University
OpenAlex ID: W4410935588