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 R 2 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 (R 2 > 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 (1+0.05) × 0.6 × R 2 798 − R 728 0.6 × R 2 798 + R 728 +0.05 . The unified ONLI model gave an R 2 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.

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Plant disease and pests influence the physiological state and restricts the healthy growth of crops. Physiological measurements are considered the most accurate way of assessing plant health status. In this paper, we researched the use of an in situ hyperspectral remote sensor to detect plant water status in winter wheat infected with powdery mildew. Using a diseased nursery field and artificially inoculated open field experiments, we detected the canopy spectra of wheat at different developmental stages and under different degrees of disease severity. At the same time, destructive sampling was carried out for physical tests to investigate the change of physiological parameters under the condition of disease. Selected vegetation indices (VIs) were mostly comprised of green bands, and correlation coefficients between these common VIs and plant water content (PWC) were generally 0.784–0.902 (p < 0.001), indicating the green waveband may have great potential in the evaluation of water content of winter wheat under powdery mildew stress. The Photochemical Reflectance Index (PRI) was sensitive to physiological response influenced by powdery mildew, and the relationships of PRI with chlorophyll content, the maximum quantum efficiency of PSII photochemistry (Fv/Fm), and the potential activity of PSII photochemistry (Fv/Fo) were good with R2 = 0.639, 0.833, 0.808, respectively. Linear regressions showed PRI demonstrated a steady relationship with PWC across different growth conditions, with R2 = 0.817 and RMSE = 2.17. The acquired PRI model of wheat under the powdery mildew stress has a good compatibility to different experimental fields from booting stage to filling stage compared with the traditional water signal vegetation indices, WBI, FWBI1, and FWBI2. The verification results with independent data showed that PRI still performed better with R2 = 0.819 between measured and predicted, and corresponding RE = 8.26%. Thus, PRI is recommended as a potentially reliable indicator of PWC in winter wheat with powdery mildew stress. The results will help to understand the physical state of the plant, and provide technical support for disease control using remote sensing during wheat production.

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 depends on increasing grain number per spike (GNs) or spike number (SN) or both, increased yield under higher 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.

Both increased harvest index (HI) and increased dry matter (DM) are beneficial to yield; however, little is known about the priority of each under different yield levels. This paper aims to determine whether HI or DM is more important and identify the physiological attributes that act as indicators of increased yield. Two field experiments involving different cultivation patterns and water-nitrogen modes, respectively, were carried out from 2013 to 2016 in Huang-Huai Plain, China. Plant DM, leaf area index (LAI), and radiation interception (RI) were measured. Increased yield under low yield levels <7500 kg ha-1 was attributed to an increase in both total DM and HI, while increases under higher yield levels >7500 kg ha-1 were largely dependent on an increase in HI. Under high yield levels, HI showed a significant negative correlation with total DM and a parabolic relationship with net accumulation of DM during filling. Higher net accumulation of DM during filling helped slow down the decrease in HI, thereby maintaining a high value. Moreover, net DM accumulation during filling was positively correlated with yield, while post-anthesis accumulation showed a significant linear relationship with leaf area potential (LAP, R2 = 0.404–0.526) and radiation interception potential (RIP, R2 = 0.452–0.576) during grain filling. These findings suggest that the increase in LAP and RIP caused an increase in net DM accumulation after anthesis. Under DM levels >13,000 kg ha-1 at anthesis, maintaining higher LAI and RI in lower layers during grain formation contributed to higher yield. Furthermore, the ratio of upper- to lower-layer RI showed a second-order curve with yield during filling, with an increase in the optimal range with grain development. Pre-anthesis translocation amount, translocation ratios and contribution ratios also showed second-order curves under high yield levels, with optimal values of 3000–4500 kg ha-1, 25–35, and 30–50%, respectively. These results confirm the importance of HI in improving the yield, thereby providing a theoretical basis for wheat production in the Huang-Huai Plain.