基于冠层高光谱参数的水稻叶片碳氮比监测

    C/N content ratio of rice leaf monitoring based on canopy hyperspectral parameters

    • 摘要: 叶片碳氮比反映了植物碳氮代谢的相对强弱,对诊断和调节植物生长与产量形成具有重要作用。该文基于不同水稻品种和不同施氮水平下2 a的田间试验,系统分析了不同生育时期水稻叶片碳氮比与对应冠层高光谱反射特征的定量关系。结果表明,叶片碳氮比与拔节后不同生育时期冠层原始反射率的相关性趋势一致,与可见光波段(350~742 nm)极显著正相关,与近红外波段(750~1143 nm)极显著负相关。8个参数与2个品种不同生育时期的叶片碳氮比均有较好的相关性。通过比较模型的拟合决定系数(R2)和预测标准误(SE),确定672 nm的归一化吸收深度(ND672)与冠层叶片碳氮比(LCNR)的线性回归方程为水稻冠层叶片碳氮比的最佳监测模型。模型经过不同生育时期数据的交叉测试和独立试验资料的检验,得出对冠层叶片碳氮比的预测精确度范围为0.687~0.986,准确度为0.907~1.126,相对跟均方差为7.07~18.25,表明水稻冠层高光谱特征可以用来定量估测不同栽培条件下叶片碳氮比的变化状况。

       

      Abstract: Carbon (C) content and nitrogen (N) content ratio of leaf is a key index of carbon and nitrogen metabolic status, thus it is important for precision diagnosis and management of plant growth and yield formation. In order to study the feasibility of estimating the ratio of C and N contents of rice leaves with properties of canopy reflectance spectra, two field experiments were conducted with different nitrogen levels and rice cultivars (Oryza sativa L.) in two years. Then the relationships of C/N ratio of leaf to reflectance of single bands, different vegetation indices, derivative indices and parameters normalized by the continuum were analyzed comprehensively. The results showed that there was consistent correlation between C/N ratio and canopy reflectance after rice jointing stage. The C/N ratio of leaf was positively correlated to reflectance at 350~742 nm, and negatively correlated at 750~1143 nm, while highly correlated to eight spectral parameters among all cultivars and growth stages. After comparing the R2 and SE of regression equations, the spectral index of ND672 was found to be the best parameter for predicting C/N ratio of leaf in rice. The derived equation was tested with the observed data of all growth stages in the field experiment. The estimation precision ranged 0.687~0.986, estimation accuracy 0.907~1.126, and RMSE 7.851~18.25, indicating a good fit between the predicted and observed values of C/N ratio of leaf. Tests with other independent dataset showed that the estimation precision was 0.857~0.967, estimation accuracy 0.970~1.049, and RMSE 7.07~16.01. It was concluded that the present hyperspectral model was feasible and reliable for estimating C/N ratio of rice leaf with different cultivars and nitrogen levels.

       

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