| Summary: | Abstract Aims Hyperspectral remote sensing technology can quickly obtain above-ground biomass (AGB) information of cotton, playing an important role in realizing accurate management for cotton cultivation. Methods Using Tahe-2 as the research object, nitrogen application rates and irrigation amounts were set to 0 (N0), 100 (N1), 150 (N2), 200 (N3), 250 (N4) kg ha− 1 and 4500 (W1), 6000 (W2), 7500 (W3) m³ ha− 1 under the coupled conditions of water and nitrogen. Through correlation analysis between cotton AGB and canopy spectral reflectance, the intersection of feature wavelengths screened by the successive projection algorithm (SPA) and highly significant wavelengths was used as the input vector for modeling. Support vector machine (SVM), regression tree (RT), and convolutional neural network (CNN) were employed to verify the accuracy. Results The results revealed the following: (1) The AGB of cotton at the bud stage was highest under the W1N2 gradient. At the flowering stage, the highest AGB was observed under the W3N2 gradient. At the boll stage, the highest AGB was under the W3N0 gradient. (2) The optimal spectral model based on SVM for cotton AGB identification had higher R2 values and lower RMSE values at the boll stage, with R2 = 0.76, RMSE = 0.35 g and RPD = 17.59. The optimal spectral model based on RT had higher R2 values and lower RMSE values at the bud stage, with R2 = 0.79, RMSE = 0.24 g and RPD = 16.18. The optimal spectral model based on CNN also had higher R2 values and lower RMSE values at the bud stage, with R2 = 0.70, RMSE = 0.42 g and RPD = 4.50. These results indicated that the inversion effect at the bud stage was better than at other stages. Conclusions In terms of model testing, the RT model was found to be the most accurate for estimating cotton AGB, outperforming SVM and CNN.
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