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Estimation the leaf phosphorus concentration of litchi (Litchi chinensis Sonn.) at different growth stages by canopy reflectance

机译:估计荔枝(荔枝Chinensis Sonn.)的叶片磷浓度在不同生长阶段的不同生长阶段

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This study aims to quantify litchi leaf phosphorus concentration (LPC) from canopy spectra at five different growth stages. The band iteration was used to optimize the ratio vegetation index (RVI) and ratio of band difference index (RBDI). The correlation analysis, linear regression and cross validation were used to analyze the relationship between spectral data and LPC and construct the robust estimation model for LPC. It is difficult to estimate the LPC by the general model. The correlation analysis based on the dataset at each growth stage showed that the band optimization can improve the relationship between spectral data and LPC. And the wavebands selected by the band iteration were located in blue, red, near infrared and shortwave infrared region, which were partly related to the absorption of anthocyanin, protein, starch et al. The estimation model by RBDI (905, 965, 1005) in fruit maturation stage had the best performance (R2cv>0.95). The best estimation model in flowering stage was calibrated by RVI (925, 1025) with the R2cv of 0.66. The linear model by RBDI (1525, 2105, 2225) presented the R2cv of 0.75 in autumn shoot maturation stage. And the RBDI (815, 1475, 2135) and RBDI (2005, 2095, 2375) had the best performances in flower spike growing stage and flower bud differentiation stage with the R2cvs of 0.68 and 0.63, respectively. The results indicated that it is available to estimate LPC by canopy reflectance spectra. Besides it is necessary to estimate the phosphorus content of litchi based on the growth stages.
机译:本研究旨在在五种不同的生长阶段来量化荔枝叶磷浓度(LPC)。带迭代用于优化植被指数(RVI)和带差异指数(RBDI)的比率。相关分析,线性回归和交叉验证用于分析光谱数据和LPC之间的关系,并构建LPC的鲁棒估计模型。通过一般模型难以估计LPC。基于每个生长阶段的数据集的相关性分析表明,频带优化可以改善频谱数据和LPC之间的关系。带迭代选择的波带位于蓝色,红色,近红外和短波红外区域,其与花青素,蛋白质,淀粉等人的吸收部分部分地相关。果实成熟阶段RBDI(905,965,1005)的估计模型具有最佳性能(R2CV> 0.95)。开花阶段中的最佳估计模型由RVI(925,1025)校准,R2CV为0.66。 RBDI(1525,2105,2225)的线性模型呈现秋季芽成熟阶段0.75的R2CV。 RBDI(815,15,15,155,2135)和RBDI(2005,2095,2375)在花穗生长阶段和花芽分化阶段具有0.68和0.63的R2CV的最佳性能。结果表明它可以通过冠层反射光谱来估计LPC。此外,必须基于生长阶段估算荔枝的磷含量。

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