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.
Background: MicroRNAs (miRNAs) play crucial roles in the regulation of plant development and growth, but little information is available concerning their roles during grain development under different nitrogen (N) application levels. Our objective was to identify miRNAs related to the regulation of grain characteristics and the response to different N fertilizer conditions. Results: A total of 79 miRNAs (46 known and 33 novel miRNAs) were identified that showed significant differential expression during grain development under both high nitrogen (HN) and low nitrogen (LN) treatments. The miRNAs that were significantly upregulated early in grain development target genes involved mainly in cell differentiation, auxin-activated signaling, and transcription, which may be associated with grain size; miRNAs abundant in the middle and later stages target genes mainly involved in carbohydrate and nitrogen metabolism, transport, and kinase activity and may be associated with grain filling. Additionally, we identified 50 miRNAs (22 known and 28 novel miRNAs), of which 11, 9, and 39 were differentially expressed between the HN and LN libraries at 7, 17, and 27 days after anthesis (DAA). The miRNAs that were differentially expressed in response to nitrogen conditions target genes involved mainly in carbohydrate and nitrogen metabolism, the defense response, and transport as well as genes that encode ubiquitin ligase. Only one novel miRNA (PC-5p-2614_215) was significantly upregulated in response to LN treatment at all three stages, and 21 miRNAs showed significant differential expression between HN and LN conditions only at 27 DAA. We therefore propose a model for target gene regulation by miRNAs during grain development with N-responsive patterns. Conclusions: The potential targets of the identified miRNAs are related to various biological processes, such as carbohydrate/nitrogen metabolism, transcription, cellular differentiation, transport, and defense. Our results indicate that miRNA-mediated networks, via posttranscriptional regulation, play crucial roles in grain development and the N response, which determine wheat grain weight and quality. Our study provides useful information for future research of regulatory mechanisms that focus on improving grain yield and quality.
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