The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time and promoting yield increase. This study analyzed the changes of AGDB based on a winter wheat irrigation experiment. At the same time, the AGDB and canopy hyperspectral reflectance of winter wheat were obtained. The effect of spectral preprocessing algorithms such as reciprocal logarithm (Lg), multiple scattering correction (MSC), standardized normal variate (SNV), first derivative (FD), and second derivative (SD); sample division methods such as the concentration gradient method (CG), the Kennard–Stone method (KS), and the sample subset partition based on the joint X–Y distances method (SPXY); sample division ratios such as 1:1 (Ratio1), 3:2 (Ratio2), 2:1 (Ratio3), 5:2 (Ratio4), and 3:1 (Ratio5); dimension reduction algorithms such as uninformative variable elimination (UVE); and modeling algorithms such as partial least-squares regression (PLSR), stepwise multiple linear regression (SMLR), artificial neural network (ANN), and support vector machine (SVM) on the hyperspectral monitoring model of winter wheat AGDB was studied. The results showed that irrigation can improve the AGDB and canopy spectral reflectance of winter wheat. The spectral preprocessing algorithm can change the original spectral curve and improve the correlation between the original spectrum and the AGDB of winter wheat and screen out the bands of 1400 nm, 1479 nm, 1083 nm, 741 nm, 797 nm, and 486 nm, which have a high correlation with AGDB. The calibration sets and validation sets divided by different sample division methods and sample division ratios have different data-distribution characteristics. The UVE method can obviously eliminate some bands in the full-spectrum band. SVM is the best modeling algorithm. According to the universality of data, the better sample division method, sample division ratio, and modeling algorithm are SPXY, Ratio4, and SVM, respectively. Combined with the original spectrum and by using UVE to screen bands, a model with stable performance and high accuracy can be obtained. According to the particularity of data, the best model in this study is FD-CG-Ratio4-Full-SVM, for which the R²_c, RMSE_c, R²_v, RMSE_v, and RPD are 0.9487, 0.1663 kg·m⁻², 0.7335, 0.3600 kg·m⁻², and 1.9226, respectively, which can realize hyperspectral monitoring of winter wheat AGDB. This study can provide a reference for the rational irrigation of winter wheat in the field and provide a theoretical basis for monitoring the AGDB of winter wheat by using hyperspectral remote sensing technology.

(1) Background: With the increase in people’s consumption of processed oat products, the production of selenium (Se)-enriched oat has become a possibility to supplement the human body with Se. Therefore, the effects of various factors on the Se-enriched ability and yield of different oat varieties were comprehensively studied. (2) Methods: cv.“Pinyan 5” and cv.“Bayou 18” were applied at the stem-elongation stage and heading stage in the Jinzhong (JZ), and cv.“Bayou 1” and cv.“Jinyan 18” were applied at the heading stage and flowering stage in the northwestern Shanxi (JXB) with different doses of Na₂SeO₃ (0, 5.48, 10.96, 21.92, 43.84, 65.76, 98.64, 0, 5.48, 10.96, 21.92, 43.84, 65.76, 98.64, 147.96 g hm⁻²) by soil application and foliar spraying. (3) Results: The grain Se content and yield of oat were affected by the variety, Se application dose, stage and method of Se supplementation. Additionally, the Se content in oat grain was positively correlated with the Se application dose while the yield of oat first increased and then decreased with the Se application dose. (4) Conclusions: In the JZ and JXB, 21.92 g hm⁻² and 43.84 g hm⁻² Se was sprayed on the leaves of cv.“Bayou 18” and cv.“Bayou 1” at the heading stage, respectively, was the most effective Se biofortification program.