Abstract
The study of estimating rapeseed above-ground biomass (AGB) is of significant importance, as it can reflect the growth status of crops, enhance the commercial value of crops, promote the development of modern agriculture, and predict yield. Previous studies have mostly estimated crop AGB by extracting spectral indices from spectral images. This study aims to construct a model for estimating rapeseed AGB by combining spectral and LiDAR data. This study incorporates LiDAR data into the spectral data to construct a regression model. Models are separately constructed for the overall rapeseed varieties, nitrogen application, and planting density to find the optimal method for estimating rapeseed AGB. The results show that the R² for all samples in the study reached above 0.56, with the highest overall R² being 0.69. The highest R² for QY01 and ZY03 varieties was 0.56 and 0.78, respectively. Under high- and low-nitrogen conditions, the highest R² was 0.64 and 0.67, respectively. At a planting density of 36,000 plants per mu, the highest R² was 0.81. This study has improved the accuracy of estimating rapeseed AGB.
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