首页> 外文期刊>Australian Journal of Agricultural Research >Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice.
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Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice.

机译:水稻叶片氮素状况与冠层光谱反射率的定量关系。

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Non-destructive and quick methods for assessing leaf nitrogen (N) status are helpful for precision N management in field crops. The present study was conducted to determine the quantitative relationships of leaf N concentration on a leaf dry weight basis (LNC) and leaf N accumulation per unit soil area (LNA) to ground-based canopy spectral reflectance in rice (Oryza sativa L.). Time-course measurements were taken on canopy spectral reflectance, LNC, and leaf dry weights, with 4 field experiments under different N application rates and rice cultivars across 4 growing seasons. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalised difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC, LNA, and canopy reflectance spectra all markedly varied with N rates, with consistent change patterns among different rice cultivars and experiment years. There were highly significant linear correlations between LNC and canopy reflectance in the visible region from 560 to 710 nm (r>0.85), between LNA and canopy reflectance from 760 to 1100 nm (r>0.79), and from 460 to 710 nm wavelengths (r>0.70). Among all possible RVI, DVI, and NDVI of key wavebands from the MSR16 radiometer, NDVI of 1220 and 710 nm was most highly correlated to LNC, and RVI of 950 and 660 nm and RVI of 950 and 680 nm were the best spectral indices for quantitative monitoring of LNA in rice. The average relative root mean square errors (RRMSE) between the predicted LNC and LNA and the observed values with independent data were no more than 11% and 25%, respectively. These results indicated that the canopy spectral reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in rice.
机译:评估叶氮(N)状态的无损快速方法有助于田间作物精确控制N。本研究旨在确定水稻叶片干重(LNC)和每单位土壤面积(LNA)叶片氮累积量与水稻(Oryza sativa L.)地面冠层光谱反射率之间的定量关系。时程测量是对冠层光谱反射率,LNC和叶片干重进行的,在不同的氮肥施用量和4个生长季节的水稻品种下进行了4次田间试验。计算了来自MSR16辐射计的关键波段的所有可能比率植被指数(RVI),差异植被指数(DVI)和归一化差异植被指数(NDVI)。结果表明,LNC,LNA和冠层反射光谱均随N值的变化而显着变化,不同水稻品种和试验年间变化规律一致。 LNC与可见光范围从560至710 nm(r> 0.85),LNA与可见光范围从760至1100 nm(r> 0.79)和460至710 nm波长之间存在高度显着的线性相关性(r> 0.85) r> 0.70)。在来自MSR16辐射计的关键波段的所有可能的RVI,DVI和NDVI中,1220和710 nm的NDVI与LNC的相关性最高,而950和660 nm的RVI和950和680 nm的RVI是最佳的光谱指数。水稻中LNA的定量监测。预测的LNC和LNA与具有独立数据的观测值之间的平均相对均方根误差(RRMSE)分别不超过11​​%和25%。这些结果表明,冠层光谱反射率可潜在地用于水稻叶氮状况的无损实时监测。

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