Spectral estimation of the aboveground biomass of cotton under water-nitrogen coupling conditions.

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.