Using hyperspectral remote sensing technology to monitor leaf area index (LAI) in a timely, fast and non-destructive manner is essential for accurate quantitative crop management. The relationships between existing vegetation indices (VIs) and LAI usually tend to saturate under dense canopies in crop production. The purpose of this study was to propose a new VI in which the estimating saturation is greatly weakened, and prediction accuracy is improved under conditions of high LAI in winter wheat (Triticum aestivum L.). The quantitative relationship between ground-based canopy spectral reflectance and LAI in wheat was investigated. The results showed that the optimized band combination, namely, the form of non-linear vegetation index (NLI) was more sensitive to changes in LAI. When λ(x1) = 798 nm and λ(y2) = 728 nm, the band combination NLI (798,728) had the highest R² of 0.757. Among the common VIs, the modified triangular vegetation index 2 (MTVI2), the ratio spectral index [RSI (760,730)] and the 2-band enhanced vegetation index (EVI2) gave superior performance (R² > 0.710) in terms of LAI estimation, but were worse than NLI (798,728). Inspired by the modified non-linear vegetation index (MNLI), NLI (798,728) was further optimized to become a novel optimized non-linear vegetation index (ONLI), which can be calculated by the formula \({{\left( { 1 { + 0} . 0 5} \right) \, \times \, \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, - \,R_{ 7 2 8} } \right)} \mathord{\left/ {\vphantom {{\left( { 1 { + 0} . 0 5} \right) \, \times \, \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, - \,R_{ 7 2 8} } \right)} { \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, + \,R_{ 7 2 8} { + 0} . 0 5} \right)}}} \right. \kern-0pt} { \left( { 0. 6\, \times \,R_{ 7 9 8}^{2} \, + \,R_{ 7 2 8} { + 0} . 0 5} \right)}}\). The unified ONLI model gave an R² of 0.779 and root mean square error (RMSE) of 1.013 across all datasets. These results indicate that the novel ONLI has strong adaptability to various cultivation conditions and can provide a good estimate of LAI in winter wheat.

Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky–Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS–SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R² = 0.741–0.852), while the SVR and PLSR models performed similarly (R² = 0.733–0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R² was 0.849–0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R² increased by 14.39%, and the R² of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.

Wheat yield components vary between different ecological regions and yield levels. Grain number responses to pre-anthesis dry matter (DM) and nitrogen (N) in increasing yield were always investigated in spike organs, neglecting the effect of non-spike organ nutrition or overall distribution. This paper determined the relationships between grain number and pre-anthesis DM and N in spike and non-spike organs under different yield levels, with using two sorts of field experiments (different water-nitrogen modes and cultivation management patterns) from 2012–2015 in Huang-Huai plain. The results indicated that improving yield under yield of 7500 kg ha⁻¹ mainly depends on GNs. GNs showed significant positive relationships with above-ground DM accumulation from jointing to anthesis under high or low yield levels. Rapid DM growth in spring achieves higher GNs. Spike and non-spike DM and N contents both demonstrated strong positive relationships with GNs, spike DM distribution also shows a positive correlation, but spike N distribution ratio show negatively correlation with GNs. Improved N distribution in non-spike organs and DM partition in spike organs conduce to increasing GNs.

The pathogenesis of wheat powdery mildew (WPM) is from the bottom to upper layers of the plant, and the vertical observation angle limits the early monitoring of WPM status. Multiangle remote sensing could effectively extract spatial structural information from different plant layers. The objectives of this study were to improve the monitoring accuracy of WPM severity and to screen suitable observation angles by developing a novel vegetation index. The monitoring capacities of the novel parameters [Normalized Powdery Mildew Index (NPMI) and Ratio Powdery Mildew Index (RPMI)] and 14 optimized traditional spectral parameters were compared at 13 observation angles and different angle ranges. The correlation between all spectral parameters and disease severity was superior in the forward observation direction than in the backward observation direction in the principal plane of the Sun, and the correlation between the two observation directions decreased with an increase in observation angle. The new spectral parameter suitable for inversion of disease index was RPMI (R ₇₄₄ /R ₇₆₂ - 0.5 × R ₇₁₀ ), and the best optimal observation angle was +10°, with a 0.852 coefficient, which was 31.74% higher than that of the two-band spectral parameter, R ₇₄₄ /R ₇₆₂ . The fitting accuracy of the new parameter in the range of 0° to +30° in the forward direction was 0.704. RPMI could not only improve the monitoring accuracy of powdery mildew severity at a single angle but also achieve a more stable monitoring accuracy in the 0° to +30° range in the forward direction, which significantly expands the application scope of remote sensing monitoring and enhances the flexibility of the technology in actual production environments.