秦占飞, 常庆瑞, 谢宝妮, 申健. 基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测[J]. 农业工程学报, 2016, 32(23): 77-85. DOI: 10.11975/j.issn.1002-6819.2016.23.011
    引用本文: 秦占飞, 常庆瑞, 谢宝妮, 申健. 基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测[J]. 农业工程学报, 2016, 32(23): 77-85. DOI: 10.11975/j.issn.1002-6819.2016.23.011
    Qin Zhanfei, Chang Qingrui, Xie Baoni, Shen Jian. Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(23): 77-85. DOI: 10.11975/j.issn.1002-6819.2016.23.011
    Citation: Qin Zhanfei, Chang Qingrui, Xie Baoni, Shen Jian. Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(23): 77-85. DOI: 10.11975/j.issn.1002-6819.2016.23.011

    基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测

    Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district

    • 摘要: 实时监测水稻氮素状况对于评估水稻长势及精准田间管理意义重大。为确定宁夏引黄灌区水稻叶片全氮含量的最优高光谱估测方法,该文依托不同氮素水平水稻试验,基于成像高光谱数据和无人机高光谱影像,综合运用统计分析及遥感参数成图技术,对比分析光谱指数与偏最小二乘回归方法预测水稻叶片全氮含量的精确度和稳健性。结果表明,以组合波段738和522 nm光谱反射率的一阶导数构成的比值光谱指数(ratio spectral index,RSI)构建的线性模型为水稻叶片全氮含量的最优估测模型(检验R2为0.673,均方根误差为0.329,相对分析误差为2.02);无人机高光谱影像反演的水稻叶片全氮含量分布范围(1.28%~2.56%)与地面实际情况较相符(1.34%~2.49%)。研究结果可为区域尺度水稻氮素含量的空间反演及精准农业的高效实施提供科学和技术依据。

       

      Abstract: Abstract: Nitrogen is essential for the improvement of photosynthesis and productivity of plants. However, nitrogen fertilizer is also a significant non-point source of water and atmospheric pollution. Therefore, a timely and accurate assessment of leaf nitrogen content (LNC) in crops is critical for crop growth diagnosis and precision management, eventually promoting crop yield and quality while minimizing environmental costs. The aim of this study was to determine the most suitable algorithm, based on hyperspectral reflectance data, for the regional assessment of LNC at critical growth stages of paddy rice. In this study rice experiments with different nitrogen levels and growth stages were conducted at different sites of Ningxia irrigation zone. Ground-based hyperspectral datasets were obtained from the stem elongation stage to the dough grain stage at plot and field scales. The plot and field datasets were used for model calibration and validation, respectively. A hyperspectral imagery was obtained over the field region at milk grain stage using UHD 185 carried by an unmanned serial vehicle (UAV). On the basis of a comprehensive analysis of the hyperspectral data, significant spectral indices (SIs) such as the normalized difference spectral index (NDSI) and ratio spectral index (RSI) were derived for an accurate and robust assessment of the LNC. Spectral indices representing a complete combination of the spectral bands between 450 nm to 950 nm were calculated using the NDSI and RSI formulations. The contour map of coefficient of determination (R2) between LNC and the combinations of 2 separate wavelengths in the hyperspectrum was used to evaluate the new SIs through comparing the predictions with plot-experiment measurements and determine which one produce the higher prediction accuracy over the others. Then the predictions of the SIs were validated by independent datasets collected at field experiments. The capability of multivariable regression approaches such as partial least-squares regression (PLSR) was examined. R2, root mean square error (RMSE), relative error (RE) and relative prediction deviation (RPD) were employed to assess the model performance. The results showed that the reflectance spectra showed a positive response to the LNC in the near-infrared wavelength region and a negative response in the red region. The RSI using derivative values at around 738 to 522 nm was the superior SI in terms of its accuracy, simplicity, and applicability. The best estimation model of LNC was built. The model R2 and RMSE were 0.763 and 0.369 for calibration and 0.673 and 0.329 for validation, and the RPD was 2.02. These indicated that the model produced an acceptable result. We explored the relationship between the first derivative of reflectance at 738 and 522 nm as they were affected by the LNC. The first derivative of reflectance at 738 and 522 nm had different spectral responses to the change of LNC. The first derivative of reflectance at 738 and 522 nm were nearly proportional to the similar LNC values. However, the first derivative of reflectance at 738 nm increased and that at 522 nm decreased with the increase of LNC. Independent validation using the UAV dataset demonstrated the robustness of the new SI. The predication of LNC was 1.28%-2.56%, which was similar with measurements (1.34%-2.49%). Our study demonstrated that hyperspectral measurements provided a robust and practical tool to diagnostic mapping of the LNC on a regional scale.

       

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