关键词: 潮土, 碱解氮, 特征波段, 模型, 高光谱

Abstract: The alkali⁃hydrolyzable nitrogen（AH⁃N）content in soil profiles reflects the short⁃term nitrogen supply potential of soil. However，its hyperspectral estimation is often hindered by high⁃dimensional redundant features，which limits model accuracy. To optimize the combination of methods and improve estimation accuracy，this study focused on the distribution area of fluvo⁃aquic soil in Henan Province.Specifically，11 soil profiles（1 m depth）were selected across farmlands，orchards and vegetable fields.A near⁃infrared hyperspectral imager was utilized to acquire hyperspectral images of the profiles，obtaining the spectral information of 220 soil samples. Five spectral preprocessing methods，such as standard normal variate（SNV）and first derivative（FD），were used in combination with competitive adaptive reweighted sampling（CARS），successive projections algorithm（SPA），and uninformative variable elimination（UVE）to extract wavelengths sensitive to AH⁃N. Partial least squares regression（PLSR）and least squares support vector machine（LS⁃SVM）models were constructed to compare and analyze the estimation accuracy of different method combinations.The results indicated that both SNV and FD preprocessing improved the model performance. Overall，wavelength selection methods yielded better performance when coupled with the LS⁃SVM model than with the PLSR model. Among them，the FD⁃CARS⁃LS⁃SVM model exhibited the optimal performance，with a prediction set R² of 0.89 ，a root mean square error（RMSE）of 11.58 mg/kg，and a relative prediction deviation（RPD）of 2.92.Profile validation based on the optimal model showed that the R² values for all profiles ranged from 0.90 to 0.98，with RMSE values between 4.33 and 11.77 mg/kg，and RPD values all exceeding 2.3.These results demonstrate the robustness and stability of the model and indicate its capability for accurate inversion and vertical distribution characterization of AH⁃N content in fluvo⁃aquic soil profiles. In conclusion，the integration of FD preprocessing and CARS variable selection effectively eliminates redundant information in hyperspectral data. When coupled with the nonlinear LS⁃SVM model，this approach achieves optimal estimation of AH⁃N content in fluvo⁃aquic soil profiles. Furthermore，the proposed method effectively characterizes the vertical spatial differentiation of AH⁃N.

Key words: Fluvo?aquic soil, Alkali?hydrolyzable nitrogen, Characteristic band, Model, Hyperspectrum